Computer engineering

Recent studies of student problem-solving behavior have shown stable behavior patterns within student groups. In this work, we study patterns of student behavior in a richer self-organized practice context where student worked with a combination of problems to solve and worked examples to study. We model student behavior in the form of vectors of micro-patterns and examine student behavior stability in various ways via these vectors. To discover and examine global behavior patterns associated with groups of students, we cluster students according to their behavior patterns and evaluate these clusters in accordance with student performance.

Recent studies have shown that students follow stable behavioral patterns while learning in online educational systems. These behavioral patterns can further be used to group the students into different clusters. However, as these clusters include both highand low-performance students, the relation between the behavioral patterns and student performance is yet to be clarified. In this work, we study the relationship between students’ learning behaviors and their performance, in a self-organized online learning system that allows them to freely practice with various problems and worked examples. We represent each student’s behavior as a vector of highsupport sequential micro-patterns. Then, we discover both the prevalent behavioral patterns in each group and the shared patterns across groups using discriminative non-negative matrix factorization. Our experiments show that we can successfully detect such common and specific patterns in students’ behavior that can be further interprete...

The purpose of protein-protein interaction network clustering is to find major modules for cells function. Various clustering algorithms are used but only some of them take advantage of intelligent computational methods. In this work we introduced a particle swarm optimization method to find dense sub-graphs in protein-protein interaction networks. The experimental results proved that our method have advantages over previous methods upon modulari- ty measure proposed by Grivan and Newman.

Nowadays, distributed systems are prevalent and practical in network environments. In distributed systems, pattern recognition help to extract information from network nodes. Meanwhile, data mining in such systems needs resource consideration in terms of storage and computational time. The primary requirement of these systems is a scalable mechanism to distribute the tasks on several databases. Moreover, to do a centralized process, relocating data from all nodes or partial nodes to a central node has confidential risks and traffic overhead. Therefore, distributed data mining in distributed environments needs systematic and structural techniques. In this paper, we propose a new algorithm to extract frequent itemsets in Wireless Sensor Networks. Through this algorithm, nodes frequent local itemsets are obtained with a Bitwise approach, and nodes are classified into clusters by using the Low Energy-Adaptive Clustering Hierarchy (LEACH) algorithm. Connecting the head cluster is perform...

In this article, we present a method to validate a multi-lingual (English, Spanish, Russian, and Farsi) corpus on imageability ratings automatically expanded from MRCPD (Liu et al., 2014). We employed the corpus (Brysbaert et al., 2014) on concreteness ratings for our English MRCPD+ validation because of lacking human assessed imageability ratings and high correlation between concreteness ratings and imageability ratings (e.g. r = .83). For the same reason, we built a small corpus with human imageability assessment for the other language corpus validation. The results show that the automatically expanded imageability ratings are highly correlated with human assessment in all four languages, which demonstrate our automatic expansion method is valid and robust. We believe these new resources can be of significant interest to the research community, particularly in natural language processing and computational sociolinguistics.

This manuscript describes the algorithms and contribution of Poseidon 2011 to acquire the optimum and efficient solutions for rescue agent simulation problems. In this team, because of recent changes of the server, new WorldGraph structure and Communication system were implemented. Each section, explains new algorithms of this year in details. This version of Poseidon team is based on the sample code of the server with extensive changes in structure of agents and WorldModel. In Poseidon 2011, we have used computational geometry and artificial intelligence algorithms for solving the problems of rescue agent simulation.

Translating from English, a morphologically poor language, into morphologically rich languages such as Persian comes with many challenges. In this paper, we present an approach to rich morphology prediction using a parallel corpus. We focus on the verb conjugation as the most important and problematic phenomenon in the context of morphology in Persian. We define a set of linguistic features using both English and Persian linguistic information, and use an English-Persian parallel corpus to train our model. Then, we predict six morphological features of the verb and generate inflected verb form using its lemma.

This paper proposes ELEXR, a novel metric to evaluate machine translation (MT). In our proposed method, we extract lexical co-occurrence relationships of a given reference translation (Ref) and its corresponding hypothesis sentence using hyperspace analogue to language space matrix. Then, for each term appearing in these two sentences, we convert the co-occurrence information into a conditional probability distribution. Finally, by comparing the conditional probability distributions of the words held in common by Ref and the candidate sentence (Cand) using Kullback-Leibler divergence, we can score the hypothesis. ELEXR can evaluate MT by using only one Ref assigned to each Cand without incorporating any semantic annotated resources like WordNet. Our experiments on eight language pairs of WMT 2011 submissions show that ELEXR outperforms baselines, TER and BLEU, on average at system-level correlation with human judgments. It achieves average Spearman’s rho correlation of about 0.78, Kendall’s tau correlation of about 0.66 and Pearson’s correlation of about 0.84, corresponding to improvements of about 0.04, 0.07 and 0.06 respectively over BLEU, the best baseline.

Morphological analysis is an essential process in translating from a morphologically poor language such as English into a morphologically rich language such as Persian. In this paper, first we analyze the output of a rule-based machine translation (RBMT) and categorize its errors. After that, we use a statistical approach to rich morphology prediction using a parallel corpus to improve the quality of RBMT. The results of error analysis show that Persian morphology comes with many challenges especially in the verb conjugation. In our approach, we define a set of linguistic features using both English and Persian linguistic information obtained from an English-Persian parallel corpus, and make our model. In our experiments, we generate inflected verb form with the most common feature values as a baseline. The results of our experiments show an improvement of almost 2.6% absolute BLEU score on a test set containing 16 K sentences.

Heterogeneous Stock (HS) rats are a genetically diverse outbred rat population that is widely used for studying genetics of behavioral and physiological traits. Mapping Quantitative Trait Loci (QTL) associated with transcriptional changes would help to identify mechanisms underlying these traits. We generated genotype and transcriptome data for five brain regions from 88 HS rats. We identified 21,392 cis-QTLs associated with expression and splicing changes across all five brain regions and validated their effects using allele specific expression data. We identified 80 cases where eQTLs were colocalized with genome-wide association study (GWAS) results from nine physiological traits. Comparing our dataset to human data from the Genotype-Tissue Expression (GTEx) project, we found that the HS rat data yields twice as many significant eQTLs as a similarly sized human dataset. We also identified a modest but highly significant correlation between genetic regulatory variation among orthol...