Reviewerly/exHarmony huggingface dataset:
Hyperparameter Selection in Reinforcement Learning Using the “Design of Experiments” Method

Artificial Intelligence and Machine Learning is a highly active area of research across numerous subgenres. One such example is Reinforcement Learning, which relies on trial and error based sampling of the environment to train an agent at completing a given task, commonly applied to Atari 2600 games within research applications. Many variants of Reinforcement Learning algorithms exist, all of which apply numerous hyperparameters to control the learning process in some way, from strength of backpropagation updates through to rates of exploration. The breadth of choice across these hyperparameters makes optimal training a challenging task, with no feedback given until significant time has lapsed in training. What is of interest here is the relative importance across these hyperparameters as well as any relationship amongst them at deriving high performance agents. Through this research we apply the common statistical approach of Design of Experiments to the task of understanding the state space of the numerous hyperparameters present in Reinforcement Learning algorithms such as the Double Deep Q-Network and Prioritized Experience Replay methods. We identify the learning rate as the only primary contributor of success or failure of value based Reinforcement Learning approaches to achieve optimal reward gain. This finding suggests the possibility of significantly reduced effort and time for considering the effects of non-dependent hyperparameters.
Cognitive Computing

Cognitive computing is an AI-derived paradigm that focuses on human-like reasoning and understanding done by computing equipment driven by software. AI-rooted cognitive competencies deliver these outcomes through software-managed computing equipment. It is an attempt at delivering outcomes that could compare to the human brain’s cognitive outcomes, including creativity and innovation. The goal of cognitive computing is to simulate human thought processes in a computerized model. The computer can mimic the way the human brain works by resorting to self-learning algorithms that apply data mining, pattern recognition, and natural language processing. Deep learning algorithms and neural networks process information by comparing it to a teaching set of data. The more data the system is exposed to, the more it learns, and the more accurate it becomes over time.
Effects of Protection Cloud Accounting and Connection with the Frequency of Cyber Attacks

The entire world is found in the process of fast information and technological changes. In this context, fast and efficient reactions of IT experts are required for the needs of redefining the work and protection of technological processes. Accounting is one of the vulnerable areas, so the issue of the importance of increased protection for the safety of accounting data is raised. Fast growth of information technology and current digital era requirements require the structure of information systems and forces companies to adopt new strategies that respond to the challenges of information security. One of the answers is the application of the work of accounting in the cloud. This work wants to highlight the effects of work and protection of accounting in the cloud in relation to frequent cyber attacks, which is the goal of this research. The results of an empirical research conducted on a sample of 183 respondents (accounting employee) show the importance of storage and authentication of accounting data with the frequency of cyber attacks.
A Recommendation System for Movies by Using Hadoop Mapreduce

Recommendation systems have become an integral component of the sales strategies of many businesses. Due to the immense size of data sets, however, innovative algorithms such as collaborative filtering, clustering models, and search-based methods are utilized. This study intends to demonstrate the benefits of the Hadoop MapReduce framework and item-to-item collaborative filtering by developing a user-ratings-based recommendation system for a larger movie data set. The resulting system offers information on movies filtered by year, director name, or comparable movies based on user reviews. Thus, we have been able to deliver credible movie suggestions based on these lists. The evaluation indicates that the recommended approaches are accurate and reliable.
Comparison of Adversarial and Non-Adversarial LSTM Music Generative Models

Algorithmic music composition is a way of composing musical pieces with minimal to no human intervention. While recurrent neural networks are traditionally applied to many sequence-to-sequence prediction tasks, including successful implementations of music composition, their standard supervised learning approach based on input-to-output mapping leads to a lack of note variety. These models can therefore be seen as potentially unsuitable for tasks such as music generation. Generative adversarial networks learn the generative distribution of data and lead to varied samples. This work implements and compares adversarial and non-adversarial training of recurrent neural network music composers on MIDI data. The resulting music samples are evaluated by human listeners, and their preferences are recorded. The evaluation indicates that adversarial training produces more aesthetically pleasing music.
Implementation and Evaluation of a Deep Neural Network for Spam Detection: An Empirical Study of Accuracy and Efficiency

The problem of spam emails is a widespread issue that creates a lot of inconvenience for individuals and organizations. According to statistics, approximately 84% of emails received on a daily basis are recognized as spam. This paper aims to present a solution to this problem by proposing the use of a neural network capable of identifying and classifying potential spam emails. The neural network was developed using Python, TensorFlow, Keras, Google Colaboratory, and Jupyter. These tools were chosen because they are widely used and well-suited for the task of creating a deep learning model. The results of the network were found to be satisfactory, with an accuracy rate of approximately 99%. This is comparable to the results achieved by large companies such as Google and Yahoo! who are known to use similar methods to combat spam. Overall, this paper demonstrates that neural networks can be a powerful tool for addressing the problem of spam emails and that the proposed solution has the potential to improve the efficiency and effectiveness of spam filtering for individuals and organizations.
Towards Data-Effective Educational Question Generation with Prompt-Based Learning

Practice and exam-style questions, as essential educational tools, contribute to educators’ effective teaching. Automatic question generation (QG) is a promising technique that can eliminate the manual effort of constructing questions and boost technology-enhanced education systems. Recently, deep neural network-based question-generation approaches have significantly improved upon state-of-the-art of question generation. Nevertheless, these approaches are often developed based on huge and non-educational datasets consisting of over 100,000 examples, which negatively affect the scalability and reliability of the educational QG systems. This study proposes a prompt-based learning QG approach that could generate questions in a data-effective way. The proposed prompt-based learning QG approach is trained and evaluated on a general dataset SQuAD, and an educational dataset SciQ. Experiment results demonstrate that our approach outperforms existing best QG models by a vast margin in data-effective scenarios and could generate high-quality educational questions with as few as 1,000 training examples.
The Employment of a Machine Learning-Based Recommendation System to Maximize Netflix User Satisfaction

This study compared two content-based filtering methods for creating a Netflix recommendation system. Two functio601586ns were developed and evaluated, both of which take the name of a movie or TV show as input and return a list of similar movies or TV shows based on the filtering strategy. The first method compared movie or TV show descriptions based on keywords, while the second method considered several features such as title, director, cast, and genre to determine the degree of similarity between two movies or TV shows. Different metrics such as cosine similarity, Euclidean distance, and Pearson’s correlation were used to measure the degree of resemblance. The results of the study show that the technique in which data from various specific features has been used has proven to be more useful and trustworthy than the approach in which only description keywords have been employed. The study also reveals that the Naive Bayes model delivered superior results to the KNN model in terms of categorising movie and TV show features. These findings provide insight into the connection between movie and TV show features in terms of their similarity, which can be useful when selecting features for recommender systems. However, the study does not establish whether the content-based strategy is the optimal method for developing Netflix’s recommendation algorithm due to a lack of data on user interactions. Nonetheless, the recommendation systems produced accurate suggestions without spoilers, achieving the main goal of this study.
On the Use of Blogging in the Classroom of English for Specific Purposes in Times of COVID-19 to Promote Written Skills: A Collaborative Approach

This study presents the results of a blogging project that was carried out in UNED, the National Spanish University of Distance Education, in the course English for Tourism II, from the Degree in Tourism, during the lockdown in Spain in March-May 2020. This course, aimed at students who already possess a B1 level, intends to help them reach a B2 level. UNED university is traditionally based on blended learning, but due to the lockdown, that year was fully online. Blogging was implemented as an extra activity. The project consisted of the following: students were invited to write an entry for the course blog, created with the tool Blogger. They also had to comment on each other’s posts, and there was a phase of peer review. Both a quantitative and a qualitative analysis was carried out of their final exams and of a mixed-type final post questionnaire. Quantitative results show that the rate of success among the students who took part in this project was significantly higher than those who did not. Qualitative results show a rise in the students’ motivation. Thus, blogging proved to be an interesting resource to promote extra language practice in a natural and motivating way, and participants reported it was a good learning support during the lock-down period.
From Olympics to War – Pursuing Sarajevo Identity Using Sentiment Analysis

In the case of the 1984 Sarajevo Winter Olympic Games, the legacy evokes sentiments that range from hopefulness to dejection, playfulness to anxiety, since what was to follow only 8 years after this mega event was the most violent destruction of any city in modern European history. Using sentiment and data analysis the research analyses existing international academic discourse on the mentioned topics and questions their impact on the city identity. The results of the world cloud analysis show the almost identical ratio between Olympic games and war identity, with slightly higher association with Olympic games identity. Occurrence and bigram analysis confirm that and results in favor of the Olympic games. Finally, sentiment analysis shows that 87.5% of articles are classified as war sentiment. The presented study concludes that the academic discourse obscures the identity of Sarajevo, maintaining the status quo and polarizing it between the opposing memory debate of the two analyzed happenings.
Learning from Few Examples with Nonlinear Feature Maps

In this work we consider the problem of data classification in post-classical settings where the number of training examples consists of mere few data points. We explore the phenomenon and reveal key relationships between dimensionality of AI model’s feature space, non-degeneracy of data distributions, and the model’s generalisation capabilities. The main thrust of our present analysis is on the influence of nonlinear feature transformations mapping original data into higher- and possibly infinite-dimensional spaces on the resulting model’s generalisation capabilities. Subject to appropriate assumptions, we establish new relationships between properties of nonlinear feature transformation maps and the probabilities to learn successfully from few presentations.
Finding Eulerian Tours in Mazes Using a Memory-Augmented Fixed Policy Function

This paper describes a simple memory augmentation technique that employs tabular Q-learning to solve binary cell structured mazes with exits generated randomly at the start of each solution attempt. A standard tabular Q-learning can solve any maze with continuous learning; however, if the learning is stopped and the policy is frozen, the agent will not adapt to solve newly generated exits. To avoid using Recurrent Neural Networks RNNs to solve memory-required tasks, we designed and implemented a simple external memory to remember the agent’s cell visit history. This memory also expands the state information to hold more information, assisting tabular Q-learning in distinguishing its path from entering and exiting a maze corridor. Experiments on five maze problems of varying complexity are presented. The maze has two and four predefined exits; the exit will be randomly assigned at the start of each solution attempt. The results show that tabular Q-learning with a frozen policy can outperform standard deep-learning algorithms without incorporating RNNs into the model structure.
Investigating Instagram Privacy Through Memory Forensics

Online social media networking such as Instagram, Facebook, and Twitter are very popular among users to connect with each other, and share photos, stories, etc. On the other hand, these media can also be used to commit cybercrime. Forensics investigation tools, methodology, and techniques can be used to uncover crimes committed through these media. This research focuses on the investigation of Instagram activities through memory forensics. We access Instagram accounts through the web browser on a Windows 10 machine. Then we employ memory forensics to retrieve and analyze the artifacts left on the device’s memory due to the use of Instagram. We demonstrate that memory forensics is more effective in retrieving traces of Instagram account’s activities artifacts. The artifacts may include personal information such as login credentials, names, phone numbers, email addresses, etc., as well as Instagram activities such as posting pictures, tagging others, leaving comments, and liking other people’s posts. In addition, we propose a process for investigators to carry out memory forensics for retrieving traces of Instagram activities.
Use of Augmented Reality to Present Archaeological Contents

Modern society has taken big steps in digital environment, which has caused ever growing usage of digital maps. However, conventional paper maps still remain very much present, especially in presenting tourist information about some city, region or even state. Irrespective of whether the maps are digital or paper maps, map producers are faced with the problem of including all necessary information on maps, and their users are facing the challenge of clearly reading and understanding their contents. The technology of augmented reality has been developed in order to remove such problems. The application ArheVinkovci has been developed for the purpose of re-searching the possibilities of the augmented reality technology on a concrete example of the archaeological sites in the town Vinkovci and it was tested in order to figure out the advantages and disadvantages of such technology. The paper presents the approach to the development of the application along with the problems encountered in the process, as well as the results of application testing in real conditions and the possibilities offered by this technology. For this purpose, two hypotheses have been set: 1. Augmented reality technology can be used to produce carto-graphic representations; 2. Existing analogue cartographic representations can be complemented with additional cartographic content using augmented reality technology.
An Approach to Mobile App Design and Development Combining Design Thinking, User Experience, and Iterative-Incremental Development

Mobile applications have grown at an accelerated pace, closely following the technological development of mobile devices, however, on some occasions such applications do not meet customer expectations. Agile methodologies have been used in order to face the complexity in the development process, as well as cope with the changing environment and the fast delivery that the market demands. According to the literature, one of the recurring problems is that the developers do not actually consider the real needs of the users, therefore, efforts have been made to apply hybrid development models. This paper presents a methodology proposal that integrates design thinking, user experience and iterative-incremental software development with the aim of developing competitive products that offer an adequate user experience, contemplating the user as the main axis. The methodology involves 7 phases: empathize, define, analyze and ideate, design, prototype, evaluate and refine, which are described in detail. In addition, the article presents the results of the development of two mobile applications, the first addressed the stray dogs problem in a city, the second focuses of improving the communicative functionality of customers in a cafeteria, through the use of augmented reality. Both applications were verified through usability tests, they were also evaluated with respect to their initial requirements. The results of this research can help developers when considering a software creation alternative that improves the proposed solutions and is more user oriented.
Credit Card Fraud Payments Detection Using Machine Learning Classifiers on Imbalanced Data Set Optimized by Feature Selection

Card payment fraud is a global problem that continues today. This study uses machine learning algorithms to predict the outcome of regular and fraudulent transactions on real-world credit card payment transactions. The performance of algorithms is evaluated through accuracy, sensitivity, specificity, Matthews Correlation Coefficient, and Receiver Operating Characteristic (ROC) Area rates. Three different experiments have been conducted. In the first stage, we used the original dataset. In the second stage, Synthetic Minority Oversampling Technique (SMOTE) is introduced to an imbalanced dataset to increase the performance of algorithms. Finally, we optimized the algorithms with feature selection methods in the last stage. In the second stage, we succeeded in increasing the specificity rate from 0.77 to 1.00 by using a voting classifier. In the third stage, we managed to keep the rate at a rate of 1.00 while decreasing the number of features from 31 to 5 features subset.
Evaluation of Accuracy: A Comparative Study Between Finger Pointing and Stylus Pointing Using Mid-Air Input

This paper is based on finding the accuracy difference between the Finger pointing and Stylus pointing using LEAP Motion. Experimentation has been conducted on customized software specifically developed for testing purposes. Experiments have been performed using LEAP Motion device with three different groups of users which include Computing, Non-computing and Laymen. All users were asked to perform two types of testing i.e. Finger pointing and Stylus pointing. All of them performed the experiments under same conditions. After doing detailed experimentation, analysis has been conducted on the obtained results for finding out whether finger pointing is better or stylus pointing as a midair gesture input. The significance of doing this research is that if the results of finger pointing or stylus pointing appear to be much accurate then it can be introduced in different fields.
Building an Artist’s Profile in Javascript: Integrating Data Analytics and the New York Metropolitan Museum of Art Open Dataset to Visualize Elements of an Artist’s Life

Museums around the world are making certain datasets public. This research introduces four data analytics built as Javascript visualizations of public domain art from the Metropolitan Museum of Art, New York City. The visualizations focus on Albrecht Dürer as the author work is in the public domain. The Javascript visualizations are housed online as public access. The challenge to research and build visualizations without using traditional data science libraries are described in the paper. From the research, four resulting visualizations emerged as follows: 2-qualitative, 1-quantitative, and 1-mobile. The novel visualizations for data analytics were custom built during the research process as described in the paper along with lessons learned and future work.
On the Use of Generative Adversarial Networks to Generate Face Images from Voice Neural Embeddings

Nowadays, the automatic detection of objects, either in images or in videos are topics of high interest in the development of technologies related to the deep learning approach, one of them is the field of forensic acoustics. Forensic acoustics is related to a security approach where the technology is used to try resolve policies’ issues. In this work, considering that Generative Adversarial Networks (GANs) have been used successfully in the reconstruction or generation images, the recognition of the face of people from its audio or video recordings has been studied using GANs. Due the novelty of this research, there are not many databases to evaluate the goodness of the models, so, an images-audio database has been created, containing both, images and audio of people who appear in videos of the YouTube platform. These images and corresponding audio embeddings have been used to train the proposed models based on GANs. The objective of this work is to generate the image of the face of a person considering only its audio voice signal as feature, that is, generate a face like the owner of the voice. The metric used to evaluate the efficiency of the proposed technique has been the “Peak Signal to Noise Ratio” metric (PSNR) which is able to determine if an image could be considered as a human face. Up to 28.39 dB of PSNR has been obtained when the images generated from its voice embeddings were evaluated, presenting up to 30% of relative improvement comparing to the same technique that use noise as feature.
Interactive Narrative Design and Storytelling, Use Case with Geospatial Data

Storytelling, as one of the main concepts of social behaviour, which existed in various shapes throughout history, is an essential part of human interaction with others. How one design and tell the story depends on the purpose of the story and the audience. Storytelling with the use of geospatial data is an additional challenge, but also helps to create an exciting user experience and provides better understanding of some complex structures or hidden data connections. This paper describes patterns that can help authors design a story using geospatial data and its usage in geographic visualization and data-driven storytelling. Also, its aim is to describe the design process of data storytelling system and an example of implementation which uses these patterns as assistance in it. It shows how these patterns can be used for designing stories, how that affects the tourism industry and what benefits such stories bring.