Deep learning for computer vision: a comparison between convolutional neural networks and hierarchical temporal memories on object recognition tasks

In recent years, Deep Learning techniques have shown to perform well on a large variety of problems both in Computer Vision and Natural Language Processing, reaching and often surpassing the state of the art on many tasks. The rise of deep learning is also revolutionizing the entire field of Machine Learning and Pattern Recognition pushing forward the concepts of automatic feature extraction and unsupervised learning in general. However, despite the strong success both in science and business, deep learning has its own limitations. It is often questioned if such techniques are only some kind of brute-force statistical approaches and if they can only work in the context of High Performance Computing with tons of data. Another important question is whether they are really biologically inspired, as claimed in certain cases, and if they can scale well in terms of "intelligence". The dissertation is focused on trying to answer these key questions in the context of Computer Vision and, in particular, Object Recognition, a task that has been heavily revolutionized by recent advances in the field. Practically speaking, these answers are based on an exhaustive comparison between two, very different, deep learning techniques on the aforementioned task: Convolutional Neural Network (CNN) and Hierarchical Temporal memory (HTM). They stand for two different approaches and points of view within the big hat of deep learning and are the best choices to understand and point out strengths and weaknesses of each of them. CNN is considered one of the most classic and powerful supervised methods used today in machine learning and pattern recognition, especially in object recognition. CNNs are well received and accepted by the scientific community and are already deployed in large corporation like Google and Facebook for solving face recognition and image auto-tagging problems. HTM, on the other hand, is known as a new emerging paradigm and a new meanly-unsupervised method, that is more biologically inspired. It tries to gain more insights from the computational neuroscience community in order to incorporate concepts like time, context and attention during the learning process which are typical of the human brain. In the end, the thesis is supposed to prove that in certain cases, with a lower quantity of data, HTM can outperform CNN.

Studio, analisi e progettazione di un prototipo di infrastructure as code

La tesi inserita in un periodo di forte transizione dai sistemi On­premises a sistemi Cloud ha avuto l'esigenza di risolvere alcune problematiche legate alla definizione delle infrastrutture. Come poter scalare le risorse all'evenienza ricreando gli stessi ambienti, monitorandoli e mettendo in sicurezza i dati critici delle applicazioni? La tesi ha risposto proprio a questa domanda definendo un nuovo paradigma nel concepire le infrastrutture chiamato Infrastructure as Code. La tesi ha approfondito le pratiche e le metodologie che si sono legate maggiormente all'Infrastructure as Code tra le quali Version Control, Configuration Management, Continuous Integration e Continuous Delivery. La tesi inoltre ha previsto la realizzazione di un prototipo finale nato dallo studio del flusso di sviluppo software aziendale, definendo gli ambienti in accordo ai sistemi di Version Control e Configuration Management, applicando pratiche di integrazione continua per giungere ad una deployment pipeline funzionale.

A Trustability Metric for Code Search based on Developer Karma

The promise of search-driven development is that developers will save time and resources by reusing external code in their local projects. To efficiently integrate this code, users must be able to trust it, thus trustability of code search results is just as important as their relevance. In this paper, we introduce a trustability metric to help users assess the quality of code search results and therefore ease the cost-benefit analysis they undertake trying to find suitable integration candidates. The proposed trustability metric incorporates both user votes and cross-project activity of developers to calculate a "karma" value for each developer. Through the karma value of all its developers a project is ranked on a trustability scale. We present JBENDER, a proof-of-concept code search engine which implements our trustability metric and we discuss preliminary results from an evaluation of the prototype.

Evaluating Code Duplication to Identify Rich Business Objects from Data Transfer Objects

Java Enterprise Applications (JEAs) are complex software systems written using multiple technologies. Moreover they are usually distributed systems and use a database to deal with persistence. A particular problem that appears in the design of these systems is the lack of a rich business model. In this paper we propose a technique to support the recovery of such rich business objects starting from anemic Data Transfer Objects (DTOs). Exposing the code duplications in the application's elements using the DTOs we suggest which business logic can be moved into the DTOs from the other classes.

Spy: A Flexible Code Profiling Framework

Code profiling is an essential activity to increase software quality. It is commonly employed in a wide variety of tasks, such as supporting program comprehension, determining execution bottlenecks, and assessing code coverage by unit tests. Spy is an innovative framework to easily build profilers and visualize profiling information. The profiling information is obtained by inserting dedicated code before or after method execution. The gathered profiling information is structured in line with the application structure in terms of packages, classes, and methods. Spy has been instantiated on four occasions so far. We created profilers dedicated to test coverage, time execution, type feedback, and profiling evolution across version. We also integrated Spy in the Pharo IDE. Spy has been implemented in the Pharo Smalltalk programming language and is available under the MIT license.

Alternative DNA base-pairs: from efforts to expand the genetic code to potential material applications

The complementary Watson-Crick base-pairs, A:T and G:C, have long been recognized as pivotal to both the stability of the DNA double helix and replication/transcription. Recently, the replacement of the Watson-Crick base-pairs with other molecular entities has received considerable attention. In this tutorial review we highlight different approaches used to replace natural base-pairs and equip them with novel function. We also discuss the advantages that non-natural base-pairs convey with respect to practical applications.