A New Model for Semiautomatic Student Source Code Assessment

Programming courses at university and high school level, and competitions in informatics (programming), often require fast assessment of the received programming tasks solutions. This problem is usually solved by the use of automated systems that check the produced output for some test cases for every solution.In this paper, we present a new model for semiautomatic student source codes assessment for a given programming task, based on our approach of representation of the program codes as vectors. It represents a human and computer collaborative effort. Our research on the use of these vectors in data mining analysis of the source codes, with the achieved excellent results on the number of correctly clustered items, is a solid foundation for the proposed model. At the end, we present the results of the preliminary testing of the model

Short text evaluation with neural network

The aim of this paper is to present a technique, which uses machine learning to process the short text answers with Hungarian language. The processing is based on a special neural network, the convolutional neural network, which can efficiently process short text answer. To achieve precise classification for training and recall grammatically consistent answers and the conversion of the text to the input are inevitable. To convert the input, continuous bag of words and Skip-Gram models will be used, resulting in a model that will be able to evaluate the Hungarian short text answers

SchedMail: Sender-Assisted Message Delivery Scheduling to Reduce Time-Fragmentation

Although early efforts aimed at dealing with large amounts of emails focused on filtering out spam, there is growing interest in prioritizing non-spam emails, with the objective of reducing information overload and time fragmentation experienced by recipients. However, most existing approaches place the burden of classifying emails exclusively on the recipients' side, either directly or through recipients' email service mechanisms. This disregards the fact that senders typically know more about the nature of the contents of outgoing messages before the messages are read by recipients. This thesis presents mechanisms collectively called SchedMail which can be added to popular email clients, to shift a part of the user efforts and computational resources required for email prioritization to the senders' side. Particularly, senders declare the urgency of their messages, and recipients specify policies about when different types of messages should be delivered. Recipients also judge the accuracy of sender-side urgency, which becomes the basis for learned reputations of senders; these reputations are then used to interpret urgency declarations from the recipients' perspectives. In order to experimentally evaluate the proposed mechanisms, a proof-of-concept prototype was implemented based on a popular open source email client K-9 Mail. By comparing the amount of email interruptions experienced by recipients, with and without SchedMail, the thesis concludes that SchedMail can effectively reduce recipients' time fragmentation, without placing demands on email protocols or adding significant computational overhead

Information Outlook, September 1997

Volume 1, Issue 9https://scholarworks.sjsu.edu/sla_io_1997/1008/thumbnail.jp

Evaluating Perceived Trust From Procedurally Animated Gaze

Adventure role playing games (RPGs) provide players with increasingly expansive worlds, compelling storylines, and meaningful fictional character interactions. Despite the fast-growing richness of these worlds, the majority of interactions between the player and non-player characters (NPCs) still remain scripted. In this paper we propose using an NPC’s animations to reflect how they feel towards the player and as a proof of concept, investigate the potential for a straightforward gaze model to convey trust. Through two perceptual experiments, we find that viewers can distinguish between high and low trust animations, that viewers associate the gaze differences specifically with trust and not with an unrelated attitude (aggression), and that the effect can hold for different facial expressions and scene contexts, even when viewed by participants for a short (five second) clip length. With an additional experiment, we explore the extent that trust is uniquely conveyed over other attitudes associated with gaze, such as interest, unfriendliness, and admiration

A Strategic Day-ahead Bidding Strategy and Operation for Battery Energy Storage System by Reinforcement Learning

The Battery Energy Storage System (BESS) plays an essential role in the smart grid, and the ancillary market offers a high revenue. It is important for BESS owners to maximise their profit by deciding how to balance between the different offers and bidding with the rivals. Therefore, this paper formulates the BESS bidding problem as a Markov Decision Process(MDP) to maximise the total profit from the e Automation Generation Control (AGC) market and the energy market, considering the factors such as charging/discharging losses and the lifetime of the BESS. In the proposed algorithm, function approximation technology is introduced to handle the continuous massive bidding scales and avoid the dimension curse. As a model-free approach, the proposed algorithm can learn from the stochastic and dynamic environment of a power market, so as to help the BESS owners to decide their bidding and operational schedules profitably. Several case studies illustrate the effectiveness and validity of the proposed algorithm.</p