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Topic Review
Alundra 2: A New Legend Begins
Alundra 2: A New Legend Begins (アランドラ2 魔進化の謎, Arandora 2 Ma Shinka no Nazo, Alundra 2: The Mystery of Magic Evolution) is an action role-playing game developed by Matrix Software for the Sony PlayStation. It was published by SCEI in Japan and Activision worldwide. Unlike its predecessor, Alundra, Alundra 2 features a 3D look which opens up a new world of puzzles. Also, despite its title, Alundra 2 is a standalone sequel, and has no ties with the original. It has a whole new story with a different set of characters, including the main character, Flint. Compared to the darker storyline of Alundra, Alundra 2 has a more light-hearted storyline.
  • 686
  • 03 Nov 2022
Topic Review
Visual Simultaneous Localization and Mapping
Visual Simultaneous Localization and Mapping (VSLAM) methods refer to the SLAM approaches that employ cameras for pose estimation and map reconstruction and are preferred over Light Detection And Ranging (LiDAR)-based methods due to their lighter weight, lower acquisition costs, and richer environment representation.
  • 685
  • 30 Dec 2022
Topic Review
IoT Privacy Preservation Using Blockchain
IoT uses a large number of devices and most of these devices are resource-constrained. Blockchain being light-weighted is a great solution for privacy preservation in resource-constrained devices. The privacy aspect of blockchain comes from its ability to provide transparency in a distributed network.
  • 684
  • 15 Sep 2021
Topic Review
Deformation Theory
In mathematics, deformation theory is the study of infinitesimal conditions associated with varying a solution P of a problem to slightly different solutions Pε, where ε is a small number, or vector of small quantities. The infinitesimal conditions are therefore the result of applying the approach of differential calculus to solving a problem with constraints. One might think, in analogy, of a structure that is not completely rigid, and that deforms slightly to accommodate forces applied from the outside; this explains the name. Some characteristic phenomena are: the derivation of first-order equations by treating the ε quantities as having negligible squares; the possibility of isolated solutions, in that varying a solution may not be possible, or does not bring anything new; and the question of whether the infinitesimal constraints actually 'integrate', so that their solution does provide small variations. In some form these considerations have a history of centuries in mathematics, but also in physics and engineering. For example, in the geometry of numbers a class of results called isolation theorems was recognised, with the topological interpretation of an open orbit (of a group action) around a given solution. Perturbation theory also looks at deformations, in general of operators.
  • 684
  • 02 Dec 2022
Topic Review
Predicting the Evolution of Syntenies
Syntenies are genomic segments of consecutive genes identified by a certain conservation in gene content and order. The notion of conservation may vary from one definition to another, the more constrained requiring identical gene contents and gene orders, while more relaxed definitions just require a certain similarity in gene content, and not necessarily in the same order. Regardless of the way they are identified, the goal is to characterize homologous genomic regions, i.e., regions deriving from a common ancestral region, reflecting a certain gene co-evolution that can enlighten important functional properties.
  • 683
  • 02 Jun 2021
Topic Review
Comparison of Document Markup Languages
The following tables compare general and technical information for a number of document markup languages. Please see the individual markup languages' articles for further information.
  • 683
  • 23 Oct 2022
Topic Review
Information Assurance
Information assurance (IA) is the practice of assuring information and managing risks related to the use, processing, storage, and transmission of information. Information assurance includes protection of the integrity, availability, authenticity, non-repudiation and confidentiality of user data. IA encompasses not only digital protections but also physical techniques. These protections apply to data in transit, both physical and electronic forms, as well as data at rest . IA is best thought of as a superset of information security (i.e. umbrella term), and as the business outcome of information risk management.
  • 683
  • 21 Nov 2022
Topic Review
Technological Aspects for Pleasant Learning
The teaching–learning process, at each educational level, is often an open problem for educators and researchers related to the stated topic. Researchers combine emerging technologies to formulate learning tools in order to understand the abstract contents of the subjects; however, the problem still persists. A technological learning tool would be effective when projected into an educational model that looks at motivation, usability, engagement, and technological acceptability. Some of these aspects could be attributed through the use of augmented reality and games. 
  • 682
  • 22 Apr 2021
Topic Review
Studio One
Studio One is a digital audio workstation (DAW) application, used to create, record, mix and master music and other audio, with functionality also available for video. Initially developed as a successor to the KRISTAL Audio Engine, it was acquired by PreSonus and first released in 2009 for macOS and Microsoft Windows. In addition to the commercial editions of the software (known as Studio One Artist and Studio One Professional), PreSonus also distributes a free edition, with reduced functionality (known as Studio One Prime). The Professional edition is also available as part of the PreSonus Sphere monthly subscription program.
  • 682
  • 21 Oct 2022
Topic Review
Training, Test, and Validation Sets
In machine learning, the study and construction of algorithms that can learn from and make predictions on data is a common task. Such algorithms work by making data-driven predictions or decisions,:2 through building a mathematical model from input data. The data used to build the final model usually comes from multiple datasets. In particular, three data sets are commonly used in different stages of the creation of the model. The model is initially fit on a training dataset, that is a set of examples used to fit the parameters (e.g. weights of connections between neurons in artificial neural networks) of the model. The model (e.g. a neural net or a naive Bayes classifier) is trained on the training dataset using a supervised learning method (e.g. gradient descent or stochastic gradient descent). In practice, the training dataset often consist of pairs of an input vector and the corresponding "answer" vector (or scalar), which is commonly denoted as the target (or label). The current model is run with the training dataset and produces a result, which is then compared with the target, for each input vector in the training dataset. Based on the result of the comparison and the specific learning algorithm being used, the parameters of the model are adjusted. The model fitting can include both variable selection and parameter estimation. Successively, the fitted model is used to predict the responses for the observations in a second dataset called the validation dataset. The validation dataset provides an unbiased evaluation of a model fit on the training dataset while tuning the model's hyperparameters (e.g. the number of hidden units in a neural network). Validation datasets can be used for regularization by early stopping: stop training when the error on the validation dataset increases, as this is a sign of overfitting to the training dataset. This simple procedure is complicated in practice by the fact that the validation dataset's error may fluctuate during training, producing multiple local minima. This complication has led to the creation of many ad-hoc rules for deciding when overfitting has truly begun. Finally, the test dataset is a dataset used to provide an unbiased evaluation of a final model fit on the training dataset.. When the data in the test dataset has never been used in training (for example in cross-validation), the test dataset is also called a holdout dataset.
  • 682
  • 03 Nov 2022
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