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This paper presents a description about our adopted approach for the information retrieval and textual entailment tasks of the COLIEE 2017 competition. We address the information retrieval task by implementing a partial string matching and a topic clustering method. For the textual entailment task, we propose a Long Short-Term Memory (LSTM) - Convolutional Neural Network (CNN) model which utilizes word embeddings trained on the Google News vectors. We evaluated our approach for both tasks on the COLIEE 2017 dataset. The results demonstrate that the topic clustering method outperformed the partial string matching method in the information retrieval task. The performance of LSTM-CNN model was competitive with other textual entailment systems.

Textual Inference is a research trend in Natural Language Processing (NLP) that has recently received a lot of attention by the sci- entific community. Textual Entailment (TE) is a specific task in Textual Inference that aims at determining whether a hypothesis is entailed by a text. This paper employs the Child-Sum Tree-LSTM for solving the chal- lenging problem of textual entailment. Our approach is simple and able to generalize well without excessive parameter optimization. Evaluation done on SNLI, SICK and other TE datasets shows the competitiveness of our approach.

In this paper, we present a multi-featured supervised automatic keyword extraction system. We extracted salient semantic features which are descriptive of candidate keyphrases, a Random Forest classifier was used for training. The system achieved an accuracy of 58.3 % precision and has shown to outperform two top performing systems when benchmarked on a crowdsourced dataset. Furthermore, our approach achieved a personal best Precision and F-measure score of 32.7 and 25.5 respectively on the Semeval Keyphrase extraction challenge dataset. The paper describes the approaches used as well as the result obtained.1

This paper describes a system which uses entity and topic coherence for improved Text Segmentation (TS) accuracy. First, the Linear Dirichlet Allocation (LDA) algorithm was used to obtain topics for sentences in the document. We then performed entity mapping across a window in order to discover the transition of entities within sentences. We used the information obtained to support our LDA-based boundary detection for proper boundary adjustment. We report the significance of the entity coherence approach as well as the superiority of our algorithm over existing work.

Case-Based Reasoning (CBR) is a branch of AI that is employed to solving problems which emphasizes the use of previous solutions in solving similar new problems. This work presents TAMDS, a Temperament and Mood Detection system which employs Case-Based Reasoning technique. The proposed system is adapted to the field of psychology to help psychologists solve part of the problems in their complex domain. We have designed TAMDS to detect temperament and moods of individuals. A major aim of our system is to help individuals who are out of reach of a professional psychologist to manage their personality and moods because as humans, moods affect our perceptions, personal health, the way we view the world around us and the way we react to it.

Continuous miniaturization of mobile devices has greatly increased its adoption and use by people in various facets of our lives. This has also increased the popularity of face recognition and image processing. Face recognition is now being employed for security purpose opening up the need for further research in recent time. Image compression becomes useful in cases when images need to be transmitted across networks in a less costly way by increasing data volume while reducing transmission time. This work discusses our findings on image compression and its effect on face recognition systems. We studied and implemented three well known face recognition algorithms and observed their recognition accuracy when gallery / probe images were compressed and/or uncompressed as one would naturally expect. For compression purposes, we adopted the JPEG and JPEG2000 coding standard. The face recognition algorithms studied are PCA, ICA and LDA. As a form of an extensive research, experiments conducted include both in compressed and uncompressed domains where the three algorithms have been exhaustively analyzed. We statistically present the results obtained which showed no significant depreciation in the recognition accuracies.

We propose a task independent neural networks model, based on a Siamese-twin architecture. Our model specifically benefits from two forms of attention scheme, which we use to extract high level feature representation of the underlying texts, both at word level (intra-attention) as well as sentence level (inter-attention). The inter attention scheme uses one of the text to create a contextual interlock with the other text, thus paying attention to mutually important parts. We evaluate our system on three tasks, i.e. Textual Entailment, Paraphrase Detection and Answer-Sentence selection. We set a near state-of-the-art result on the textual entailment task with the SNLI corpus while obtaining strong performance across the other tasks that we evaluate our model on.

with the recent surge in acceptance of face recognition systems, more and more work is needed to perfect the existing grey areas. A major concern is the issue of illumination intensities in the images used as probe and images trained in the database. This paper presents the adoption and use of fuzzy histogram equalization in combating illumination variations in face recognition systems. The face recognition algorithm used is Principal Component Analysis, PCA. Histogram equalization was enhanced using some fuzzy rules in order to get an efficient light normalization. The algorithms were implemented and tested exhaustively with and without the application of fuzzy histogram equalization to test our approach. A good and considerable result was achieved.

Developing Countries are gradually transiting from cash to an electronic based economy by virtue of cashless policy implementation. With this development, cyber criminals and hackers who hitherto attacked businesses and individuals across the Atlantic now see this development as a new venture for their criminal acts and are thus re-directing their energies towards exploiting possible loopholes in the electronic payment system in order to perpetuate fraud. In this paper, we proposed an enhanced approach to detecting phishing attempts and preventing unauthorized online banking withdrawal and transfer. We employed the use of Semantics Content Analysis, Earth Mover Distance and Biometric Authentication with finger print to construct a model. We demonstrated the efficacy of the implemented model with the experiments conducted, a good and considerable result was achieved.

Image compression has received tremendous research input in recent times; however, only few works have been done with respect to face recognition regardless of the fact that, usually, face images need to be transmitted across networks for recognition purposes as seen in surveillance systems. This work analytically investigates the effects of image compression on recognition accuracies of three selected PCA-based algorithms. The algorithms investigated include standard PCA, MPCA and MPCALDA algorithms. As a form of an extensive research, experiments conducted include both in compressed and uncompressed domains where the three algorithms have been exhaustively analyzed. We statistically present the results obtained which shows no significant depreciation in the recognition accuracies.

This paper presents a deep learning based dialogue system which has been trained to answer user queries posed as questions during a conversation. The proposed system, though generative, takes advantage of domain specific knowledge for generating valid answers. The evaluation analysis shows that the proposed system obtained a promising result.