Facial Expression Recognition: A Survey

Shaping the Stream: Techniques and Troubles of Algorithmic Recommendation

Since the key space of Vigenere cryptosystem is very large, therefore, brute force does not help in the analysis of the cryptosystem in real time. Statistical techniques are also not helpful when the key size is not small.The aim of this work was to examine the feasibility of Cuckoo Search algorithm as a cryptanalysis tool of classical cryptosystems especially Vigenere cipher. We applied Genetic Algorithm, Particle Swarm Optimization and Cuckoo Search techniques for the analysis of Vigenere cipher. After doing lots of experiments we observed that GA and PSO techniques can recover the entire key of Vigenere Cipher correctly for keys of small lengths. However, Cuckoo Search can recover more than 90% of the key characters for keys of size up to 25 characters because GA and PSO get trapped in local minima while Cuckoo Search finds the global optimum solution following characteristics of Levy flights.Experimental results show the superiority of CS in fast convergence and the accuracy in the cryptanalysis of Vigenere cipher over PSO and GA. Evolutionary techniques such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Cuckoo Search (CS) are promising nature-inspired meta-heuristic optimization algorithms. Cuckoo Search combined with Levy flights behavior and Markov chain random walk can search global optimal solution very quickly. The aim of this paper is to investigate the applicability of Cuckoo Search algorithm in cryptanalysis of Vigenere cipher. It is shown that optimal solutions obtained by CS are better than the best solutions obtained by GA or PSO for the analysis of the Vigenere cipher. The results show that a Cuckoo Search based attack is very effective on the Vigenere cryptosystem.

Cryptanalysis of Vigenere cipher using Cuckoo Search

The fields of music, health, and technology have seen significant interactions in recent years in developing music technology for health care and well-being. In an effort to strengthen the collabor...

https://journals.sagepub.com/doi/pdf/10.1177/2059204321997709

Music, Computing, and Health: A Roadmap for the Current and Future Roles of Music Technology for Health Care and Well-Being:

Deep self-attention network for facial emotion recognition

Manual segregation of a playlist and annotation of songs, in accordance with the current emotional state of a user, is labor intensive and time consuming. Numerous algorithms have been proposed to automate this process. However the existing algorithms are slow, increase the overall cost of the system by using additional hardware (e.g. EEG systems and sensors) and have less accuracy. This paper presents an algorithm that automates the process of generating an audio playlist, based on the facial expressions of a user, for rendering salvage of time and labor, invested in performing the process manually. The algorithm proposed in this paper aspires to reduce the overall computational time and the cost of the designed system. It also aims at increasing the accuracy of the designed system. The facial expression recognition module of the proposed algorithm is validated by testing the system against user dependent and user independent dataset. Experimental results indicate that the user dependent results give 100% accuracy, while user independent results for joy and surprise are 100 %, but for sad, anger and fear are 84.3 %, 80 % and is 66% respectively. The overall accuracy of the emotion recognition algorithm, for user independent dataset is 86%. In audio, 100 % recognition rates are obtained for sad, sad-anger and joy-anger but for joy and anger, recognition rates obtained are 95.4% and 90 % respectively. The overall accuracy of the audio emotion recognition algorithm is 98%. Implementation and testing of the proposed algorithm is carried out using an inbuilt camera. Hence, the proposed algorithm reduces the overall cost of the system successfully. Also, on average, the proposed algorithm takes 1.10 sec to generate a playlist based on facial expression. Thus, it yields better performance, in terms of computational time, as compared to the algorithms in the existing literature.

/pdf/an-accurate-algorithm-for-generating-a-music-playlist-based-4o4573dk6w.pdf

An Accurate Algorithm for Generating a Music Playlist based on Facial Expressions

The work presented in this paper describes a generic genetic algorithm called DUREHA’s (Dominance, Universal stochastic sampling and Rank-based Emulation of a Heuristic Algorithm) Algorithm for cryptanalysis of classical ciphers. The underlying objective of this paper is to automate the process of cryptanalysis in order to render salvage of time, and resources available, preserve population diversity, minimize the convergence rate and control mutation rates. While numerous algorithms have been proposed to automate this process for variegated ciphers, these approaches are yet isolated from each other. The existence of a generic algorithm to cryptanalyze any type of cipher is yet not true. The algorithm proposed in this paper aspires to address such issues. The implementation and experimentation of the proposed algorithm is accomplished using three types of classical ciphers namely monosubstitution, poly-substitution and columnar transposition. The theoretical validation and experimental results indicate that the proposed algorithm is able to decrypt the ciphers by reclaiming80.71% ,87.31%and 77.66% of letters in correct position in Mono-substitution, Columnar Transposition and Vignere cipher respectively. It is also able to distinguish between the three types of ciphers correctly and is able to correctly control the mutation andconvergence rates and preserve population diversity.

/pdf/a-generic-genetic-algorithm-to-automate-an-attack-on-56kiw3ltw7.pdf

Anukriti Dureha

Papers

An Accurate Algorithm for Generating a Music Playlist based on Facial Expressions

A Generic Genetic Algorithm to Automate an Attack on Classical Ciphers