Ways of Applying Artificial Intelligence in Software Engineering

As Artificial Intelligence (AI) techniques have become more powerful and easier to use they are increasingly deployed as key components of modern software systems. While this enables new functionality and often allows better adaptation to user needs it also creates additional problems for software engineers and exposes companies to new risks. Some work has been done to better understand the interaction between Software Engineering and AI but we lack methods to classify ways of applying AI in software systems and to analyse and understand the risks this poses. Only by doing so can we devise tools and solutions to help mitigate them. This paper presents the AI in SE Application Levels (AI-SEAL) taxonomy that categorises applications according to their point of AI application, the type of AI technology used and the automation level allowed. We show the usefulness of this taxonomy by classifying 15 papers from previous editions of the RAISE workshop. Results show that the taxonomy allows classification of distinct AI applications and provides insights concerning the risks associated with them. We argue that this will be important for companies in deciding how to apply AI in their software applications and to create strategies for its use

Solar Irradiance Forecasting Using Dynamic Ensemble Selection

Solar irradiance forecasting has been an essential topic in renewable energy generation. Forecasting is an important task because it can improve the planning and operation of photovoltaic systems, resulting in economic advantages. Traditionally, single models are employed in this task. However, issues regarding the selection of an inappropriate model, misspecification, or the presence of random fluctuations in the solar irradiance series can result in this approach underperforming. This paper proposes a heterogeneous ensemble dynamic selection model, named HetDS, to forecast solar irradiance. For each unseen test pattern, HetDS chooses the most suitable forecasting model based on a pool of seven well-known literature methods: ARIMA, support vector regression (SVR), multilayer perceptron neural network (MLP), extreme learning machine (ELM), deep belief network (DBN), random forest (RF), and gradient boosting (GB). The experimental evaluation was performed with four data sets of hourly solar irradiance measurements in Brazil. The proposed model attained an overall accuracy that is superior to the single models in terms of five well-known error metrics

Synthesis and evaluation of the antifungal activity of 2-(substituted-amino)-4,5-dialkyl-thiophene-3- carbonitrile derivatives

Fifteen 2-[(substituted-benzylidene)-amino]-5-methyl-thiophene-3-carbonitrile (3a-g) and 2-[(substituted-benzylidene)-amino]-4,5-cycloalkyl-thiophene-3-carbonitrile derivatives (4a-h) were synthesized and screened for their in vitro antifungal activity against 42 clinical isolates of Candida (representing 4 different species) and 2 isolates of Criptococcus. The antifungal activities of these compounds were compared to fluconazole and amphotericin B as standard agents. All compounds presented fungicidal activity at different doses, but a few compounds showed moderate or poor antifungal activity when compared with the standard drugs. The Cryptococcus strains were more sensitive than those of the genus Candida, and compound 4d was the most active, with MFC values varying between 100-800 μg/mL. A preliminary SAR study demonstrated that the presence of a cycloalkyl ring linked to the thiophene moiety is essential for antifungal activity, and that the best antifungal candidates are cyclohexyl compounds (4d-f). The results suggest that thiophene derivatives may be interesting compounds for the further development of antifungal drugs.Colegio de Farmacéuticos de la Provincia de Buenos Aire

Motor development of children with attention deficit hyperactivity disorder

Objective: To compare both global and specific domains of motor development of children with attention deficit hyperactivity disorder (ADHD) with that of typically developing children. Methods: Two hundred children (50 children with clinical diagnoses of ADHD, according to the DSM-IV-TR and 150 typically developing controls), aged 5 to 10 years, participated in this crosssectional study. The Motor Development Scale was used to assess fine and global motricity, balance, body schema, and spatial and temporal organization. Results: Between-group testing revealed statistically significant differences between the ADHD and control groups for all domains. The results also revealed a deficit of nearly two years in the motor development of children with ADHD compared with the normative sample. Conclusion: The current study shows that ADHD is associated with a delay in motor development when compared to typically developing children. The results also suggested difficulties in certain motor areas for those with ADHD. These results may point to plausible mechanisms underlying the relationship between ADHD and motor difficulties