Stanislao Lauria

Bio: Stanislao Lauria is an academic researcher from Brunel University London. The author has contributed to research in topics: Robot & Mobile robot. The author has an hindex of 16, co-authored 36 publications receiving 1142 citations. Previous affiliations of Stanislao Lauria include University of Reading & University of Plymouth.

Exponential stability of uncertain stochastic neural networks with mixed time-delays

Abstract: This paper is concerned with the global exponential stability analysis problem for a class of stochastic neural networks with mixed time-delays and parameter uncertainties. The mixed delays comprise discrete and distributed time-delays, the parameter uncertainties are norm-bounded, and the neural networks are subjected to stochastic disturbances described in terms of a Brownian motion. The purpose of the stability analysis problem is to derive easy-to-test criteria under which the delayed stochastic neural network is globally, robustly, exponentially stable in the mean square for all admissible parameter uncertainties. By resorting to the Lyapunov–Krasovskii stability theory and the stochastic analysis tools, sufficient stability conditions are established by using an efficient linear matrix inequality (LMI) approach. The proposed criteria can be checked readily by using recently developed numerical packages, where no tuning of parameters is required. An example is provided to demonstrate the usefulness of the proposed criteria.

Teaching Introductory Programming: A Quantitative Evaluation of Different Approaches

Abstract: Teaching programming to beginners is a complex task. In this article, the effects of three factors—choice of programming language, problem-solving training, and the use of formative assessment—on learning to program were investigated. The study adopted an iterative methodological approach carried out across 4 consecutive years. To evaluate the effects of each factor (implemented as a single change in each iteration) on students’ learning performance, the study used quantitative, objective metrics. The findings revealed that using a syntactically simple language (Python) instead of a more complex one (Java) facilitated students’ learning of programming concepts. Moreover, teaching problem solving before programming yielded significant improvements in student performance. These two factors were found to have variable effects on the acquisition of basic programming concepts. Finally, it was observed that effective formative feedback in the context of introductory programming depends on multiple parameters. The article discusses the implications of these findings, identifies avenues for further research, and argues for the importance of studies in computer science education anchored on sound research methodologies to produce generalizable results.

Training personal robots using natural language instruction

Abstract: As domestic robots become pervasive, uninitiated users will need a way to instruct them to adapt to their particular needs. The authors are designing a practical system that uses natural language to instruct a vision-based robot.

Corpus-Based Robotics: A Route Instruction Example

Abstract: In corpus-based robotics, the primitive functions built into a robot are determined by the functional content of human utterances spoken to the robot. In the example of route instructions treated in this paper, the 15 primitives found include functions such as turn(), cross(), landmark_is_located(), etc. These are natural primitives of human behaviour but complex robot functions, some of which are not normally thought of by roboticists. Primitives must cope robustly with a variety of environmental conditions and require autonomous navigation capabilities based for instance on visual landmark recognition and localisation, navigable space mapping and path planning. Thus, the requirement of human-robot interaction creates specific and demanding functional targets for robot designers. A major obstacle to human-robot communication lies probably in current robots’ perception capabilities. Furthermore, for human-robot communication to match the performance of human-human communication, the robot must also be provided with capabilities of re-interpreting and correcting instructions at execution time. Such a large autonomy raises wider issues of safety and control.

打磨Using Language,倡导新理念

Abstract: 人教版高中英语新课程教材中,语言运用（Using Language）是每个单元必不可少的部分,提供了围绕单元中心话题的听、说、读、写的综合性练习,是单元中心话题的延续和升华.如何设计Using Language部分的教学,使自己的教学模式既不落俗套,又能真正体现新课程标准所倡导的教学理念,正是广大一线英语教师一直努力探索的问题.

A survey on opinion mining and sentiment analysis

Abstract: With the advent of Web 2.0, people became more eager to express and share their opinions on web regarding day-to-day activities and global issues as well. Evolution of social media has also contributed immensely to these activities, thereby providing us a transparent platform to share views across the world. These electronic Word of Mouth (eWOM) statements expressed on the web are much prevalent in business and service industry to enable customer to share his/her point of view. In the last one and half decades, research communities, academia, public and service industries are working rigorously on sentiment analysis, also known as, opinion mining, to extract and analyze public mood and views. In this regard, this paper presents a rigorous survey on sentiment analysis, which portrays views presented by over one hundred articles published in the last decade regarding necessary tasks, approaches, and applications of sentiment analysis. Several sub-tasks need to be performed for sentiment analysis which in turn can be accomplished using various approaches and techniques. This survey covering published literature during 2002-2015, is organized on the basis of sub-tasks to be performed, machine learning and natural language processing techniques used and applications of sentiment analysis. The paper also presents open issues and along with a summary table of a hundred and sixty-one articles.