Reflecting on Recurring Failures in IoT Development

As IoT systems are given more responsibility and autonomy, they offer greater benefits, but also carry greater risks. We believe this trend invigorates an old challenge of software engineering: how to develop high-risk software-intensive systems safely and securely under market pressures? As a first step, we conducted a systematic analysis of recent IoT failures to identify engineering challenges. We collected and analyzed 22 news reports and studied the sources, impacts, and repair strategies of failures in IoT systems. We observed failure trends both within and across application domains. We also observed that failure themes have persisted over time. To alleviate these trends, we outline a research agenda toward a Failure-Aware Software Development Life Cycle for IoT development. We propose an encyclopedia of failures and an empirical basis for system postmortems, complemented by appropriate automated tools

Contribution De L'apprentissage Automatique Et De La Fouille De Textes À La Construction De Systèmes D’information Pour Exploitants Agricoles

Les systèmes agricoles vont des techniques intensives aux interventions minimalistes, en passant par le semi-direct. Ces systèmes supposent une connaissance précise des pratiques agricoles par les exploitants et les techniciens. A cela, s’ajoute la maîtrise des nouvelles technologies, le contrôle des resistances aux traitements, l'acquisition des connaissances sur les variétés de semences, l'impact sur les sols, etc. Toute cette masse d'information est disponible sur internet : dans des articles scientifiques, des forums de discussions, dessites web spécialisés et lesréseaux sociaux. Ce sont des informations sous format texte, généralement mal structuré. L'objectif de ce travail est de donner une vue générale de la recherche sur la fouille de données textuelles en agriculture. Il présente les principales méthodes permettant l'extraction d'informations pertinentes et teste la fouille sur des données de Scopus, de Twitter et d'un site commercial spécialisé en produits agricoles. Un exemple de classification de données est détaillé, via les algorithmes d'apprentissage automatique. Le code informatique pour réaliser cette revue est sur Python. There are several techniques used in agricultural systems, from intensive to minimum intervention, no-tillage, and organic methods. Those systems suppose that the farmers have a precise and continuous knowledge of the methods used. Furthermore, the expertise of those new technologies, the control of the treatment resistance, the gain of knowledge on seed varieties and impact on the soil are aspects that should be taken into consideration by farmers who have to keep an eye on the novelties. All those information are available on the internet, in scientific publications, discussion forums, specialized websites, and social media. Resulting from the disorderliness of those text information, the goal of this work is to provide a global view on textual data mining for agriculture. It presents the main methods in extracting relevant information and tests it on data coming from Scopus, Twitter and a website for agricultural products, to illustrate the technique used. Coded in Python, this work provides an example of data classification via machine learning tools

LSTM Based Source Code Generation for Turkish Pseudo Code of Algorithm

Algorithmic thinking and programming abilities of students is controversial and popular issue in technological education programs in schools and universities. Students that have not best mathematical and analytical background may have difficulties in learning computer programing. Moreover, learning programming is highly difficult for a single individual to establish connection between discrete pseudo code of algorithm and source code. Another problem is required time to write a piece of program code. In order to solve this problem, there are some tools that tutor students to get analyze and realize relation between pseudo code and source code. In this study, we propose a deep learning method that is Long Short Term-Memory (LSTM) based source code generator from Turkish pseudo codes. For this purpose, we used Introduction to programming course exams in vocational high school as dataset to train LSTM. When users query a Turkish pseudo code of algorithm, C# source code is generated. In order to measure success of proposed system, generated source code and instructor’s source code is analyzed with text similarity methods. Results show that proposed system is useful for students to learn fundamental programming skills

Speech recognition, machine translation, and corpus analysis for identifying farmer demands and targeting digital extension

The increasing capabilities of Artificial Intelligence-augmented data analytics present significant opportunities for agricultural extension organizations operating in the Global South. In this project, we supported Farm Radio International (FRI) in investigating the possibility of automating the process of translating and analyzing farmers' voice message data. This report reviews several approaches to overcoming technical constraints and then presents a cutting-edge approach that utilizes innovations in unsupervised learning to deliver highly accurate speech recognition and machine translation in a diverse set of languages

What makes your movies more engaging for backers? A text analysis of Kickstarter projects

Crowdfunding has become an increasingly popular channel for entrepreneurs to raise funds from the crowd to support their startup projects. However, the percentage of crowdfunding projects that can reach their funding goals is relatively small among all crowdfunding websites. Although previous studies have examined various factors such as individual project attributes, social ties that might influence the fund-raising outcomes, the project pitches, as a key part of any crowdfunding proposal, have rarely been studied for their role in driving the crowdfunding success. In this research, we study a corpus of 1559 movie projects from Kickstarter up to the end of 2017. We use two natural language processing tools to mine the textual descriptions of projects and extract topical features and writing styles that might affect the outcomes of the fundraising campaigns. We find that the language used in the project description has surprising predictive power on the successful funding. A closer look at the words and writing styles shows that some general patterns are at work, depending on the fit between the pitch presentation and the genre of movies. For example, using some words related to “relativity” like “motion”, “space”, “time” could increase the chance of success for the action movies. While descriptive words are among the top predictors for the successful comedy movies, rational writing style might have an adverse effect. For thriller movies, social words (e.g., family, friend) are associated with higher likelihood of failure. Except for writing styles, topical features also matter. In our study, we identify both popular and outdated topics in each movie genres. These findings can help movie creators to use the most influential topical and writing features to promote their movie ideas and thus to improve the chance of funding success

A survey of the applications of text mining for agriculture

International audienceAgricultural researchers, in common with other domains, have recently began to have access to large collections of agricultural texts such as scientific papers and news stories. These texts can be analysed with text mining techniques to resolve agricultural problems or extract knowledge. Despite the potential of these techniques, text mining is a relatively underused technique in the agricultural domain. Therefore, this survey is intended to provide a current state of the art survey of the application of text mining techniques to agricultural problems