Reviewerly/exHarmony huggingface dataset:
Target-Based Versus Customer-Based Genetic Algorithms and Matrix Factorization Recommender Systems

Recommender systems (RS) are a practical choice to help the customers choose the targets (i.e. book, restaurant, movie, TV, mobile phone, etc) they like in a short time. Collaborative filtering (CF) and matrix factorization (MF) techniques are common methods that are used in the RSs. The CF technique considers the rates of the customers to the targets. It recommends targets I for customer x if I have been rated high by similar customers to x. The MF technique splits the rating matrix into two matrices, customer-factor and factor-target, such that their targets nearly obtains the rating matrix. Navgaran RS (NRS) is a RS that uses the genetic algorithms (GA) to predict the uknown entries in the customer-factor and factor-target matrices in order to make recommendation for the active customer (i.e. the customer who wants recommendation). NLM is a GA-based CF RS that hybridizes both the neighbourhood and the latent factor models to predict targets for the active customers. NLM is a customer-based RS and improved NRS. In our project, we have modified NLM to make it target-based instead of being customer-based. We show that our new RS, NLM <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">u</inf> , outperforms NLM and NRS in solving some instances.We experimentally used the datasets: MovieLens, FilmTrust, and CiaoDVD. We show that NLM <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">u</inf> has better precision value than NRS in MovieLens. It is faster and has less error than NRS and NLM in FilmTrust, and it is faster and has less error than NRS in CiaoDVD.
DNN Feature Map Gram Matrices for Mitigating White-Box Attacks on Image Classification

Deep convolutional neural networks accurately classify a diverse range of natural images, but may be easily deceived when designed, imperceptible perturbations are embedded in the images. In this paper, we design a multi-pronged training, input transformation, and image ensemble system that is attack agnostic and not easily estimated. Our system incorporates two novel features. The first is a transformation layer that iteratively filters the input image’s feature maps based on their Gram matrix representational differences to those of randomly sampled training instances from each class, which creates an ensemble of filtered input image copies. The second is a classification system that treats this filtered ensemble as a voting committee that focuses on only those predictions that changed from the original prediction on the unfiltered input image. Our evaluations on the CIFAR10 dataset show our system improves the robustness of an undefended network against a variety of bounded and unbounded white-box attacks under different distance metrics, while sacrificing little accuracy on clean images. Against adaptive full-knowledge attackers creating end-to-end attacks, our system successfully augments the existing robustness of adversarially trained networks, for which our methods are most effectively applied.
From MDP to POMDP and Back: Safety and Compositionality

We propose a compositional framework for the stochastic safety of distributed Markov Decision Processes (MDPs) and Partially Observable Markov Decision Processes (POMDPs). We use MDP and POMDPs and their distributed versions as an appropriate modelling paradigm for computational ecosystems, understood in the context of distributed systems. We extend our work on stochastic safety from MDPs to POMDPs, and then to networked MDP/POMDPs. We propose a unifying mathematical framework for stochastic safety for MDPs, their partially observable version and their composition.
An online path planner based on POMDP for UAVs

Path planning for Unmanned Aerial Vehicles (UAVs) is a challenging and important problem that received significant attention in recent years. In this paper we focus on Partially Observable Markov Decision Processes (POMDP), a powerful and popular model for planning and control that has several applications on UAVs (e.g search & rescue, surveillance, or automatic warehouse). In particular, we propose a methodology to plan trajectories that allow UAVs to reach a target while avoiding no-fly zones, optimizing, e.g, fuel consumption, flight time, or constraining the approaching angle. The fixed-wing aircraft dynamic is considered in the problem formulation and an uncertainty model error is added to take into account the approximations. The sensing system providing observations of the aircraft state consists of a GPS sensor, an Inertial Navigation System (INS), and other onboard sensors. Considering the complexity of the domain and following the recent literature, we propose an online solution approach based on Monte Carlo Tree Search (MCTS). The online nature of the approach mitigates the complexity typically associated with POMDP models by avoiding the computation of the whole policy. This approach has been validated and tested in simulation on different scenarios and with different cost functions.
Exploring Machine Learning Techniques for Twitter Sentiment Analysis to Identify Polarity - A Comprehensive Study

Social media platforms like Twitter have seen a substantial increase in usage in recent years, generating a vast quantity of data that can be analyzed by businesses and researchers. A common tool for analyzing this data is sentiment analysis, which involves locating and classifying the emotions expressed in a text. In order to determine the polarity (positive, negative, or neutral) of tweets, this study investigates the application of machine learning algorithms for the Twitter lexicon-based approach. In order to determine the polarity of tweets, this study focuses on the application of machine learning algorithms for analysis on Twitter. The study gathers information from Twitter and preprocesses it using a number of methods. The data are prepared using feature extraction methods like the bag-of- words model and TF-IDF for analysis. Metrics are used to assess each algorithm's performance. A detailed literature review is presented in this article.
Machine Learning Approach for a Novel Facial Recognition System

A facial recognition system can be developed using a machine learning approach that involves data collection, preprocessing, feature extraction, model training, evaluation and testing, and deployment. The system can be trained on a large dataset of facial images using techniques such as PCA, LBP, or CNNs for feature extraction and SVM, Random Forest, or Neural Networks for model training. The performance of the system can be evaluated using a test set, and the system can be deployed in real-world scenarios. However, it is crucial to consider the ethical and privacy implications of facial recognition technology and implement appropriate safeguards to prevent misuse. The Eigenface, Fisherface, and LBPH (Local Binary Patterns Histogram) algorithms are three popular techniques for face recognition in the OpenCV library. This work evaluates the performance of each algorithm on a specific dataset to determine which algorithm is the most appropriate for this application.
Securing Booth-based Voting System by Integrating IoT and SDN

Voting is a decision-making process where people choose their own representative. The classical booth-based voting system has been in practice for a long time, where voters visit polling booths and cast their votes. However, this system is vulnerable to various types of errors, which can lead to the manipulation of votes and undermine the democratic process. One common scenario is the possibility of uninvited guests disrupting and destroying the polling booth, despite the presence of security personnel. Creating havoc on the polling booths causes loss of money, time, and votes. To prevent such unwanted situations, a secure booth-based voting system is proposed by integrating various technologies like the Internet of Things and Software-Defined Networking. By this model, IoT-based polling booths will accept votes from the voters, and the SDN controller can monitor the polling booths and accept only the valid votes from the validated polling booths. Moreover, all the votes will be stored in a central database as ciphertext with which destroying the polling booth would not harm the already cast votes and also prevent the disclosure of votes from any potential attackers. The proposed model is implemented using Mininet-Wifi, Mosquitto MQTT, and MySQL, and it gives satisfactory results.
A Twitter-based Software Vulnerability Alert Framework using Natural Language Processing

With the increasing usage of networks, the frequency of cyber attacks are expected to increase. To prepare against these attacks, modern organizations require effective ways to ensure cyber security. Twitter is one of the most well-known social media platforms in the world. Tweets by users on Twitter cover a wide range of topics, from daily life to politics and breaking news. This social media platform has a sizable and diverse user base, provides great accessibility, and timeliness, resulting in the production of a huge amount of publicly available data, which in turn can be used effectively for a variety of purposes. For detecting security vulnerability events via Twitter, we introduce a Natural Language Processing-based approach. The tool can be used to provide alerts and notifications to users when a security vulnerability is detected.
Text Classification Into Emotional States using Deep Learning based BERT Technique

Sentiment analysis, a subset of Natural Language Processing (NLP), has grown in relevance as the modern lifestyles have become more reliant on social media. Emotional expressions include actions, feelings, ideas, and nonverbal cues. Modern approaches for Natural Language Processing (NLP) have their origins in machine learning, notably statistical machine learning. This study introduces BERT, an innovative technique for reading sentiments into written language. This model is a synthesis of motor Bidirectional Encoder Representations From Transformers (BERT) representations. In order to develop its learning algorithm of word semantic representation, this model makes use of the BERT. In line with the language context, a comparison of models found that BERT outperforms the government foundation efficiency given by all the other models in the literature. Opinion mining is a branch of natural language processing that analyses public discourse to gauge public opinion on a product or topic.
Text Emotion Detection using Machine Learning Algorithms

Text Emotion determination is becoming more vital with every passing day as millions of text messages are released on a daily basis. In text analysis, the challenge lies in interpreting the ambiguity of human language, particularly when it comes to emotions. A study was carried out to detect the emotions classified into six categories as anger, fear, joy, love, sadness, and surprise. The algorithms, including Logistic Regression, Linear Support Vector Machine, and Random Forest were used for detecting and classifying the emotions. A comparative study of these methods was carried out by considering two features namely Term Frequency- Inverse Document Frequency and Count Vectors. The highest accuracy was obtained for the Count Vectors feature using the Logistic Regression method and the emotions in the text were classified accordingly.
Comparative Analysis of Machine Learning Techniques for Mental Health Prediction

The prevalence of mental health problems has prompted investigations into the use of machine learning to tackle the issues. Mental health is a crucial component of an individual’s overall well-being and can be detected and treated early on, significantly improving the quality of life for those affected. This study examines the use of machine learning algorithms to predict mental health disorders using a dataset of self-reported information. Four commonly used machine learning models K-nearest neighbor classifier, logistic regression, random forest and decision tree are compared in terms of their performance. The objective of this study is to compare the performance of these machine learning algorithms on a self-reported mental health dataset and identify the most suitable model for predicting mental health. The challenges faced by the system include the limited size and quality of the dataset, the need for ethical considerations in handling sensitive mental health information, and potential biases in the data .The results of the experiments identify the most suitable model for predicting mental health.
A Novel Data Flow Security Protection Algorithm for Digital Media in 6G Environment

With the recent development and practice of the homomorphic encryption, privacy computing, blockchain, quantum computing and other technologies, the safe and stable transmission and analysis of information flow has become particularly important. Under this background, this paper proposes the novel data flow security protection algorithm for digital media in 6G environment. As a logical first step, the watermarking technology is studied. The traditional model is improved with the chaotic coding to meet with the complex scenarios. Then, the statistical encryption algorithm is integrated to fit for the 6G scenario. Furthermore, the novel data flow security protection algorithm is achieve by the integration of the Hamming coding. In the experiment section, the 8, 12, 16, 32, 64, 128 bits scenarios are tested and 128 bits obtain the best performance. Our best model configuration empirically achieved the risk detection accuracy of 99.85%.
Architectural Visualisation using Virtual Reality

There are many significant advantages in using virtual reality technology for architecture and design, according to the research on the subject. The capacity to create interactive, realistic, and immersive environments that let clients and designers experience the building in a way that is not achievable with conventional 2D plans or physical models is one of the main benefits. Buildings, landscapes, and interiors can all be viewed and experienced from every angle thanks to the exact and realistic 3D models that architects and designers can produce utilising VR technology. This enables customers to fully appreciate the building's dimensions, scale, and proportions in addition to the ambience and vibe it exudes. VR technology can be utilised to discover and resolve any design flaws or challenges in addition to giving clients a more immersive experience. Architects and designers can test and improve their designs in a way that is not achievable through conventional means by simulating various scenarios and settings. By avoiding the need for expensive and time-consuming design adjustments, this can help to save time and money during the construction process. In addition, VR technology can be applied to the construction of greener, more energy-efficient buildings. Architects may optimise the design to lower energy use and boost sustainability by simulating how a building performs in various environments and climates. This may have a substantial effect on the building's long-term running costs. Integration of Nanite and Lumen in this system not only reduces the computational power needed but also increases the realism and efficiency thus making it easier for the user as well as developer to use the technology in a simplified manner. Overall, using VR technology in architecture and design has a number of advantages that can aid in the creation of structures that are more precise, effective, and sustainable. Designers may guarantee that their designs satisfy the needs and expectations of their clients while also accomplishing their objectives for sustainability and efficiency by offering them an immersive and interactive experience.
Architecture Design of Electronic Commerce System Based on Distributed Technology

Because of the small number of users, low concurrency and small amount of data in the early electronic commerce system, the application software only needs to deploy a single server to meet the throughput requirements. With the continuous growth of user scale and business volume, enterprises gradually adopt distributed architecture to improve the availability of electronic commerce system, and use multiple cheap machines to complete complex computing or storage tasks. In recent years, with the continuous maturity and development of cloud native technologies such as microservice technology and container cluster management technology, major Internet companies have followed the ‘big middle platform strategy’ and built 'cloud platforms' that adapt to their own organizational structures to cope with market changes and make flexible and rapid strategic adjustments. In general, the main driving factor of electronic commerce system evolution is the huge number of users faced by products. Cloud native is an extension of distributed architecture. This paper introduces the development stage of software distributed architecture, designs and implements distributed electronic commerce system.
Graph Attention Social Recommendation Model Based on Similar Item Graph

Social recommendation improves the performance of recommendation systems by mitigating the effects of data sparsity on user prediction through the social attributes in users' social networks. However, existing social recommendation models often use user-item interaction graphs to construct item feature vectors, ignoring the effects of similar items. In addition, the correlation between feature vectors is not considered when using inner products of elements to model the interaction between user and item feature vectors. In this paper, we propose a graph attention social recommendation model based on similar item graphs (SIG-GATSR), initially enabling the user-item interaction graph, social relationship graph, and similar item graphs to transfer information on the network through an improved graph attention network, and then constructing item feature vectors by combining similarity. Subsequently, we explicitly model the correlation between user and item feature vectors by performing the outer product operation. The final rating prediction is then obtained by modeling the interaction using multilayer convolutional neural networks. Detailed experimental results of Ciao, Epinions and Flixter showed that the proposed social recommendation model outperforms other related models in terms of prediction accuracy and normalized discounted cumulative gain (NDCG).
Fine-Tuned Convolutional Neural Networks for Tamil Handwritten Text Recognition

Tamil Handwritten Text Recognition is the process of identifying and transcribing handwritten Tamil characters into machine-encoded text. Due to variations in the style, stroke, and shape of characters, identifying handwritten text is a critical task. The recognition of Tamil handwritten characters is an essential step in many applications, such as digitizing historical Tamil documents, handwriting recognition in education, and Tamil language translation systems. The development of accurate and efficient recognition systems for Tamil handwritten characters can greatly benefit these applications and contribute to the preservation and dissemination of Tamil literature and culture. The fine-tuned Convolutional Neural Network (CNN) is proposed for the identification of Tamil handwritten text. The proposed approach involves the use of pre-trained CNN models and fine-tuning them on a dataset of Tamil handwritten characters. The dataset consists of a large number of images of individual Tamil characters, including various styles of handwriting. To assess the efficacy of the suggested method, various experiments are performed on optimizing the CNN model's hyper parameters, utilizing both grid search and random search approaches. The results proved that the grid search with CNN model yields better result to recognize the Tamil text from the images.
Innovative Approach of Data Encryption Algorithm for Securing Big Data

Nowadays, a large amount of data that is growing exponentially is known as Big data. In other words, big data contains more variety and comes in big volume with more velocity. Traditional processing software cannot handle complex and big amounts of data, but this data addresses a lot of business problems, that could not be able to tackle before so to deal with this data, open-source software HDFS (Hadoop Distributed File System) is used as a cloud storage system. Big Data Security is a collective term used for all measures and tools that are used to guard data and analytics processes used for accessing the data. Big data is a joint term and tool for data and data management systems against attacks, thefts, or other malicious activities that could harm or negatively affect them so server and serverless systems both are major concerned with high security of data. The author’s research presents data security in the cloud computing environment in this paper. HDFS does not provide any scheme of encryption and decryption for securing the data, HDFS act as a storage medium that stores data in Avro format. Users use the encryption key and encryption zone to encrypt their data into HDFS.
Quantum Money : Opportunities, Challenges and Open Issues

Quantum computing possesses capabilities beyond that of classical computers. Quantum cryptography can be a way to counter increasing computational powers. Traditional money can be counterfeited and is prone to fraud. Quantum money can be implemented to overcome the problems of traditional money. Using laws of quantum physics, it can create currencies which are impossible to replicate, and are easily verifiable. This paper talks about various Quantum Money schemes that have been introduced in the past, the impact it had, security issues and countermeasures related to it, the future possibilities and opportunities available with it.
4D UAV Waypoint Arrival Guidance Based on Baseline Tau Vector Field

This paper introduces a novel baseline tau vector field (BτVF) method to provide four-dimensional (4D) guidance in time-constrained waypoint arrival tasks of Unmanned Aerial Vehicle (UAV). To guide a UAV to reach the desired position with expected speed at the certain arrival time, the bio-inspired tau guidance laws are adopted to design the vector guidance field. The UAV guided by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{B}\tau\text{VF}$</tex> can approach the baseline between two waypoints, and more importantly, the vehicle can arrive at the destination with strict time and velocity constraints of 4D flight tasks. A challenging 4D waypoint arrival dynamic simulation is designed for rotor UAV and solved by the proposed <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{B}\tau\text{VF}$</tex> method. The simulation result demonstrates that the proposed method can provide 4D guidance between waypoints efficiently and effectively.