Strategies for Student Evaluation in The Online Context

In this paper we conducted an investigation on the performance of the students during the second semester of the academic year 2020-2021. We looked at the performance results obtained by students on the laboratory work, practical and final exams while we were forced by the Covid pandemic to move entirely into an online education system. Our focus was to determine the impact of a consistent behaviour (or lack of it) on the final student performance. We determined that, even in an online setting, a good involvement (in terms of attendance and good performance) guarantees good final results. The investigations were performed using the Formal Concept Analysis, which is a very powerful instrument already used by us in previous research in order to detect student behaviour in using an e-learning portal. Another set of results showed that the change of the final mark computation formula to be based in a higher proportion on the lab work was closer to the actual overall performance of students

Comparison of Gradient-Based Edge Detectors Applied on Mammograms

Breast cancer is one of the most common types of cancer amongst women, but it is also one of the most frequently cured cancers. Because of this, early detection is crucial, and this can be done through mammography screening. With the increasing need of an automated interpretation system, a lot of methods have been proposed so far and, regardless of the algorithms, they all share a step: pre-processing. That is, identifying the image orientation, detecting the breast and eliminating irrelevant parts. This paper aims to describe, analyze, compare and evaluate six of the most commonly used edge detection operators: Sobel, Roberts Cross, Prewitt, Farid and Simoncelli, Scharr and Canny. We detail the algorithms, their implementations and the metrics used for evaluation and continue by comparing the operators both visually and numerically, finally concluding that Canny best suit our needs.

A Review and Analysis of The Existing Literature on Monochromatic Photography Colorization Using Deep Learning

It is universally known that, through the process of colorization, one aims at converting a monochrome image into one of color, usually because it was taken by the limited technology of previous decades. Our work introduces the problem, summarizes the general deep learning solutions, and discusses the experimental results obtained from open-source repositories. Although the surveyed methods can be applied to other fields, solely the content of photography is being considered. Our contribution stands in the analysis of colorization in photography by examining used datasets and methodologies for evaluation, data processing activities, and the infrastructure demanded by these systems. We curated some of the most promising papers, published between 2016 and 2021, and centered our observations around software reliability, and key advancements in solutions employing Generative Adversarial Networks and Neural Networ

Automatic Face Shape Classification Via Facial Landmark Measurements

This paper tackles the sensitive subject of face shape identification via near neutral-pose 2D images of human subjects. The possibility of extending to 3D facial models is also proposed, and would alleviate the need for the neutral stance. Accurate face shape classification serves as a vital building block of any hairstyle and eye-wear recommender system. Our approach is based on extracting relevant facial landmark measurements and passing them through a naive Bayes classifier unit in order to yield the final decision. The literature on this subject is particularly scarce owing to the very subjective nature of human face shape classification. We wish to contribute a robust and automatic system that performs this task and highlight future development directions on this matter.

Deep Reinforcement Learning from Self-Play in No-limit Texas Hold'em Poker

Imperfect information games describe many practical applications found in the real world as the information space is rarely fully available. This particular set of problems is challenging due to the random factor that makes even adaptive methods fail to correctly model the problem and find the best solution. Neural Fictitious Self Play (NFSP) is a powerful algorithm for learning approximate Nash equilibrium of imperfect information games from self-play. However, it uses only crude data as input and its most successful experiment was on the in-limit version of Texas Hold’em Poker. In this paper, we develop a new variant of NFSP that combines the established fictitious self-play with neural gradient play in an attempt to improve the performance on large-scale zero-sum imperfect information games and to solve the more complex no-limit version of Texas Hold’em Poker using powerful handcrafted metrics and heuristics alongside crude, raw data. When applied to no-limit Hold’em Poker, the agents trained through self-play outperformed the ones that used fictitious play with a normal-form single-step approach to the game. Moreover, we showed that our algorithm converges close to a Nash equilibrium within the limited training process of our agents with very limited hardware. Finally, our best self-play-based agent learnt a strategy that rivals expert human level.

Towards a Support System for Digital Mammogram Classification

Cancer is the illness of the 21th century. With the development of technology some of these lesions became curable, if they are in an early stage. Researchers involved with image processing started to conduct experiments in the field of medical imaging, which contributed to the appearance of systems that can detect and/or diagnose illnesses in an early stage. This paper’s aim is to create a similar system to help the detection of breast cancer. First, the region of interest is defined using filtering and two methods, Seeded Region Growing and Sliding Window Algorithm, to remove the pectoral muscle. The region of interest is segmented using k-means and further used together with the original image. Gray-Level Run-Length Matrix features (in four direction) are extracted from the image pairs. To filter the important features from resulting set Principal Component Analysis and a genetic algorithm based feature selection is used. For classification K-Nearest Neighbor, Support Vector Machine and Decision Tree classifiers are experimented. To train and test the system images of Mammographic Image Analysis Society are used. The best performance is achieved features for directions {45◦ , 90◦ , 135◦ }, applying GA feature selection and DT classification (with a maximum depth of 30). This paper presents a comprehensive analysis of the different combinations of the algorithms mentioned above, where the best performence repored is 100% and 59.2% to train and test accuracies respectively.

Constructing Unrooted Phylogenetic Trees with Reinforcement Learning

With the development of sequencing technologies, more and more amounts of sequence data are available. This poses additional challenges, such as processing them is usually a complex and time-consuming computational task. During the construction of phylogenetic trees, the relationship between the sequences is examined, and an attempt is made to represent the evolutionary relationship. There are several algorithms for this problem, but with the development of computer science, the question arises as to whether new technologies can be exploited in these areas of computational biology. In the following publication, we investigate whether the reinforced learning model of machine learning can generate accurate phylogenetic trees based on the distance matrix.

BibTeX for the Romanian Language

BibTeX was created by Oren Patashnik and Leslie Lamport in 1985 according to https://en.wikipedia.org/wiki/BibTeX and turned out to be a very useful software product. Nevertheless most of the present scientific paper in mathematics and computer science, and not only, are written in English. Our aim was to offer to the Romanian scientific paper writers a variant of the BibTEX whose output agrees with the Romanian language grammar.

Leader Election in a Cluster using Zookeeper

This paper presents an algorithm for flexible and fast leader election in distributed systems using Apache Zookeeper for configuration management. The algorithm proposed in this paper is designed for applications that do not use symmetric nodes so they need a specialized election process or for applications that require a more flexible approach in the leader election process. The algorithm proposes a different approach as it allows assigning prioritizations for servers in the cluster that are candidates to become a leader. The algorithm is flexible as it takes into consideration during the leader election process of the different server settings and roles, network properties, communication latency or specific application requirements.

Performance Benchmarking for NoSQL Database Management Systems

NoSQL database management systems are very diverse and are known to evolve very fast. With so many NoSQL database options available nowadays, it is getting harder to make the right choice for certain use cases. Also, even for a given NoSQL database management system, performance may vary significantly between versions. Database performance benchmarking shows the actual performance for different scenarios on different hardware configurations in a straightforward and precise manner. This paper presents a NoSQL database performance study in which two of the most popular NoSQL database management systems (MongoDB and Apache Cassandra) are compared, and the analyzed metric is throughput. Results show that Apache Cassandra outperformes MongoDB in an update heavy scenario only when the number of operations is high. Also, for a read intensive scenario, Apache Cassandra outperformes MongoDB only when both number of operations and degree of parallelism are high.