Using New Technologies to Learn Programming Languages

Current eLearning systems are increasingly used by both students and professors, considering the various facilities they offer. In the field of computer science, these eLearning platforms need to provide integrated program editors with facilities for compiling and running them. We propose a creative architecture of an eLearning system for Python which comes with new facilities related to the possibility to create content (lessons, exercises, content, and tests) inside of this platform. Thus, the professors can fully prepare their lessons and homework on our CSP (computer science platform) platform via the web interface. Similarly, the students can access this content via the platform and can solve their homework in this special space. Depending on the number of users the allocated resources dynamically change in order to ensure the proper functioning of the application, trying to keep lower operative costs

Using Games and Smart Devices to Enhance Learning Geography and Music History

This paper presents the implementation of an educational game as a skill for Amazon’s software assistant Alexa. The main motivation behind this work comes from the fact that learning through games and smart devices is commonly better received by children. The application we discuss generates test questions by extracting and analyzing information from two major knowledge resources, DBpedia and Wikidata. The questions can be automatically adapted to the knowledge level of the player through Computer Adaptive Testing (CAT). Specifically designed for Alexa as a skill, with intents, slots and sample utterances, users can interact with our application through Alexa voice service or through any smartphone, allowing the game to be played from home, school or any other place with an Internet connection

A Speaker De-Identification System Based on Sound Processing

In the context of products employing speech recognition, where the speech signal is sent from the device to centralized servers that process data, or simply products that involve data storage on servers, privacy for audio data is an important issue, just as it is for other types of data. Ignoring privacy has consequences for both, speakers (information leaks) and server administrators (legal issues). In this paper, we propose a speaker de-identification solution based on sound processing, altering voice characteristics, along with an API. Our solution consisting of pitch shift and noise mix (the latter is an optional augmentation method) has a great speaker de-identification performance, without an important loss in terms of word intelligibility. It is worth mentioning that sometimes the recordings may not be easy to understand in the initial (i.e., not de-identified) form, due to the speaker’s pronunciation, talking speed, and other related factors

A Large‐Scale E‐voting System Based on Blockchain

E-voting systems are increasingly used, considering the various facilities they offer: casting and counting votes in real time. The current voting systems are currently the target of attempted fraud and this is a major problem globally, which has not been solved even to this day. In the field of computer science, these e-voting platforms need to provide integrated security, thus enhancing the scalability and performance of the blockchain‐based e‐voting system. Our aim is to develop a secure internet-based voting system to maximize user participation, by allowing them to vote from anywhere. This paper proposes a system architecture based on blockchain technology along with a web interface in order to securely authenticate the voters on the platform. It should be noted in addition that these two components can be used together or separately, depending on the application’s needs

Diversification in an image retrieval system based on text and image processing

In this paper we present an image retrieval system created within the research project MUCKE (Multimedia and User Credibility Knowledge Extraction), a CHIST-ERA research project where UAIC{\footnote{"Alexandru Ioan Cuza" University of Iasi}} is one of the partners{\footnote{Together with Technical University from Wienna, Austria, CEA-LIST Institute from Paris, France and BILKENT University from Ankara, Turkey}}. Our discussion in this work will focus mainly on components that are part of our image retrieval system proposed in MUCKE, and we present the work done by the UAIC group. MUCKE incorporates modules for processing multimedia content in different modes and languages (like English, French, German and Romanian) and UAIC is responsible with text processing tasks (for Romanian and English). One of the problems addressed by our work is related to search results diversification. In order to solve this problem, we first process the user queries in both languages and secondly, we create clusters of similar images

Secret Smart Contracts in Hierarchical Blockchains

This article presents the results of an implementation of a new platform based on swarm communication and executable choreographies. In our research of executable choreographies, we have come up with a more general model to implement smart contracts and a generic architecture of systems using hierarchical blockchain architecture. The novel concepts of secret smart contract and near-chain are introduced. The near-chain approach presents a new method to extend the hierarchical blockchain architecture and to improve performance, security and privacy characteristics of general blockchain-based systems. As such, we are subsequently defining and explaining why any extension of blockchain architectures should revolve around three essential dimensions: trustlessness, non-repudiation and tamper resistance. The hierarchical blockchain approach provides a novel perspective, as well as establishing off-chain storages (near-chains) as special types of hierarchical blockchains stored in a distributed file system. Furthermore, we are providing solutions to the difficult blockchain concerns regarding scalability, performance and privacy issues

Scalable System for Opinion Mining on Twitter Data. Dynamic Visualization for Data Related to Refugees’ Crisis and to Terrorist Attacks

Social networks such as Twitter or Facebook grew rapidly in popularity, and users use them to share opinions about topics of interest, to be part of the community or to post messages that are available everywhere. This paper presents a system created in order to process streamed data taken from Twitter and classify it into positive, negative or neutral. The results of these processing’s can be visualized in a suggestive manner on Google Maps, users can select the language of the tweets, can group tweets that present the same news and can even display a dynamic evolution of the news in terms of its appearance. With all this amount of information it is very opportune to do some data analysis to detect different types of events (and their locations) that happen worldwide, especially at the time when this data represents information related to refugee crisis or signals terrorist attacks

Multilingual Fine-Grained Named Entity Recognition

The “MultiCoNER II Multilingual Complex Named Entity Recognition” task\footnote[1]{\url{https://multiconer.github.io}} within SemEval 2023 competition focuses on identifying complex named entities (NEs), such as the titles of creative works (e.g., songs, books, movies), people with different titles (e.g., politicians, scientists, artists, athletes), different categories of products (e.g., food, drinks, clothing), and so on, in several languages. In the context of SemEval, our team, \textit{FII\_Better}, presented an exploration of a base transformer model’s capabilities regarding the task, focused more specifically on five languages (English, Spanish, Swedish, German, and Italian). We took DistilBERT (a distilled version of BERT) and BERT (Bidirectional Encoder Representations from Transformers) as two examples of basic transformer models, using DistilBERT as a baseline and BERT as the platform to create an improved model. In this process, we managed to get fair results in the chosen languages. We have managed to get moderate results in the English track (we ranked 17th out of 34), while our results in the other tracks could be further improved in the future (overall third to last)

Intelligent control of HVAC systems. Part I: Modeling and synthesis

This is the first part of a work on intelligent type control of Heating, Ventilating and Air-Conditioning (HVAC) systems. The study is performed from the perspective of giving a unitary control method to ensure high energy efficiency and air quality improving. To illustrate the proposed HVAC control technique, in this first part it is considered as benchmark problem a single thermal space HVAC system. The construction of the mathematical model is performed only with a view to obtain a framework of HVAC intelligent control validation by numerical simulations. The latter will be reported in a second part of the study