Learning End-to-End Goal-Oriented Dialog with Multiple Answers

In a dialog, there can be multiple valid next utterances at any point. The present end-to-end neural methods for dialog do not take this into account. They learn with the assumption that at any time there is only one correct next utterance. In this work, we focus on this problem in the goal-oriented dialog setting where there are different paths to reach a goal. We propose a new method, that uses a combination of supervised learning and reinforcement learning approaches to address this issue. We also propose a new and more effective testbed, permuted-bAbI dialog tasks, by introducing multiple valid next utterances to the original-bAbI dialog tasks, which allows evaluation of goal-oriented dialog systems in a more realistic setting. We show that there is a significant drop in performance of existing end-to-end neural methods from 81.5% per-dialog accuracy on original-bAbI dialog tasks to 30.3% on permuted-bAbI dialog tasks. We also show that our proposed method improves the performance and achieves 47.3% per-dialog accuracy on permuted-bAbI dialog tasks.Comment: EMNLP 2018. permuted-bAbI dialog tasks are available at - https://github.com/IBM/permuted-bAbI-dialog-task

Preparation and Characterisation of Polystyrene Grafted Sago Starch

Styrene grafting onto sago starch was carried out by using eerie ammonium nitrate (CAN) as a redox initiator. The parameters affecting the grafting reaction were investigated and the optimum conditions obtained are as follows: temperature, 50°C; nitric acid concentration, 0.01 M; amount of styrene, 0.35 mol; amount of CAN, 16.8 x 10-4 mol and reaction period, 2h. Percentages of grafting and grafting efficiency under the optimum condition were 53.92% and 73.21%, respectively. Reactions in the presence of nitrogen gas resulted in higher percentages of grafting and grafting efficiency. FTIR spectra analysis of the grafted chain and polystyrene was identical indicating that styrene was successfully grafted onto sago starch. TGA thermograms, DSC curves and SEM photographs of sago starch-g poly(styrene) and the original polymers (sago starch and polystyrene) were different which suggested that styrene was grafted onto sago starch. The bio-degradability study using a-amylase showed that the rate of degradation of gelatinised sago starch was higher than that of sago starch-gpoly( styrene). The highest rate of degradation of sago starch-gpoly(styrene) was obtained at 50 ppm of a-amylase concentration. Viscosity measurements showed that the intrinsic viscosity and the average molecular weight (Mv) increased with the increase in the percentage of grafted polystyrene. The Mv of the various percentages of grafted polystyrene were in the order of 104. The results obtained from the swelling of sago starch-gpoly(styrene) in polar and non polar solvents showed that the percentage of swelling at equilibrium and the swelling rate coefficient decreased in the following order: DMSO > water > acetone > cyclohexanone = CHCh > toluene = CCl4. Diffusions of the solvents onto the polymers were found to be of a Fickian only for DMSO

Feature Selection in UNSW-NB15 and KDDCUP’99 datasets

Machine learning and data mining techniques have been widely used in order to improve network intrusion detection in recent years. These techniques make it possible to automate anomaly detection in network traffics. One of the major problems that researchers are facing is the lack of published data available for research purposes. The KDD’99 dataset was used by researchers for over a decade even though this dataset was suffering from some reported shortcomings and it was criticized by few researchers. In 2009, Tavallaee M. et al. proposed a new dataset (NSL-KDD) extracted from the KDD’99 dataset in order to improve the dataset where it can be used for carrying out research in anomaly detection. The UNSW-NB15 dataset is the latest published dataset which was created in 2015 for research purposes in intrusion detection. This research is analysing the features included in the UNSW-NB15 dataset by employing machine learning techniques and exploring significant features (curse of high dimensionality) by which intrusion detection can be improved in network systems. Therefore, the existing irrelevant and redundant features are omitted from the dataset resulting not only faster training and testing process but also less resource consumption while maintaining high detection rates. A subset of features is proposed in this study and the findings are compared with the previous work in relation to features selection in the KDD’99 dataset

A Deterministic Model for Analyzing the Dynamics of Ant System Algorithm and Performance Amelioration through a New Pheromone Deposition Approach

Ant Colony Optimization (ACO) is a metaheuristic for solving difficult discrete optimization problems. This paper presents a deterministic model based on differential equation to analyze the dynamics of basic Ant System algorithm. Traditionally, the deposition of pheromone on different parts of the tour of a particular ant is always kept unvarying. Thus the pheromone concentration remains uniform throughout the entire path of an ant. This article introduces an exponentially increasing pheromone deposition approach by artificial ants to improve the performance of basic Ant System algorithm. The idea here is to introduce an additional attracting force to guide the ants towards destination more easily by constructing an artificial potential field identified by increasing pheromone concentration towards the goal. Apart from carrying out analysis of Ant System dynamics with both traditional and the newly proposed deposition rules, the paper presents an exhaustive set of experiments performed to find out suitable parameter ranges for best performance of Ant System with the proposed deposition approach. Simulations reveal that the proposed deposition rule outperforms the traditional one by a large extent both in terms of solution quality and algorithm convergence. Thus, the contributions of the article can be presented as follows: i) it introduces differential equation and explores a novel method of analyzing the dynamics of ant system algorithms, ii) it initiates an exponentially increasing pheromone deposition approach by artificial ants to improve the performance of algorithm in terms of solution quality and convergence time, iii) exhaustive experimentation performed facilitates the discovery of an algebraic relationship between the parameter set of the algorithm and feature of the problem environment.Comment: 4th IEEE International Conference on Information and Automation for Sustainability, 200

Bridge Correlational Neural Networks for Multilingual Multimodal Representation Learning

Recently there has been a lot of interest in learning common representations for multiple views of data. Typically, such common representations are learned using a parallel corpus between the two views (say, 1M images and their English captions). In this work, we address a real-world scenario where no direct parallel data is available between two views of interest (say, $V_1$ and $V_2$) but parallel data is available between each of these views and a pivot view ($V_3$). We propose a model for learning a common representation for $V_1$, $V_2$ and $V_3$ using only the parallel data available between $V_1V_3$ and $V_2V_3$. The proposed model is generic and even works when there are $n$ views of interest and only one pivot view which acts as a bridge between them. There are two specific downstream applications that we focus on (i) transfer learning between languages $L_1$,$L_2$,...,$L_n$ using a pivot language $L$ and (ii) cross modal access between images and a language $L_1$ using a pivot language $L_2$. Our model achieves state-of-the-art performance in multilingual document classification on the publicly available multilingual TED corpus and promising results in multilingual multimodal retrieval on a new dataset created and released as a part of this work.Comment: Published at NAACL-HLT 201

A Correlational Encoder Decoder Architecture for Pivot Based Sequence Generation

Interlingua based Machine Translation (MT) aims to encode multiple languages into a common linguistic representation and then decode sentences in multiple target languages from this representation. In this work we explore this idea in the context of neural encoder decoder architectures, albeit on a smaller scale and without MT as the end goal. Specifically, we consider the case of three languages or modalities X, Z and Y wherein we are interested in generating sequences in Y starting from information available in X. However, there is no parallel training data available between X and Y but, training data is available between X & Z and Z & Y (as is often the case in many real world applications). Z thus acts as a pivot/bridge. An obvious solution, which is perhaps less elegant but works very well in practice is to train a two stage model which first converts from X to Z and then from Z to Y. Instead we explore an interlingua inspired solution which jointly learns to do the following (i) encode X and Z to a common representation and (ii) decode Y from this common representation. We evaluate our model on two tasks: (i) bridge transliteration and (ii) bridge captioning. We report promising results in both these applications and believe that this is a right step towards truly interlingua inspired encoder decoder architectures.Comment: 10 page

Fractionation of microbial populations in a PHA accumulating mixed culture and associated PHA content and composition

The uniformity of PHA composition and content across groups of organisms in mixed cultures was considered. An activated sludge microbial community, with an average PHA content of 20 wt%, was fractioned by Percoll assisted buoyant density separation. The microbial community in the two principal fractions was characterised using amplicon pyrosequencing. While organisms were common to both fractions, the relative abundances of species were found to be different between the two fractions. The average PHA content in one of the fractions was found to be higher (24 wt%) than the other (16 wt%); separation was considered to be in part driven by the density difference associated with PHA content, but also by other factors such as cell dimension and cellular morphology. But while differences in PHA content were observed, the PHA composition in both fractions was found to be approximately the same (43-44 mol% HV), which shows that distinct groups of microbial populations within mixed cultures may generate PHA with similar average copolymer composition

Designing for designers: Towards the development of accessible ICT products and services using the VERITAS framework

Among key design practices which contribute to the development of inclusive ICT products and services is user testing with people with disabilities. Traditionally, this involves partial or minimal user testing through the usage of standard heuristics, employing external assisting devices, and the direct feedback of impaired users. However, efficiency could be improved if designers could readily analyse the needs of their target audience. The VERITAS framework simulates and systematically analyses how users with various impairments interact with the use of ICT products and services. Findings show that the VERITAS framework is useful to designers, offering an intuitive approach to inclusive design.The work presented in this article forms part of VERITAS, which is funded by the European Commission's 7th Framework Programme (FP7) (grant agreement # 247765 FP7-ICT-2009.7.2)

Examining the Impact of Leader-Member Exchanges on Creativity and Innovation in Information Technology Projects: A Pathway to Competitive Advantage

Using a qualitative single case study, I explored the influence of Leader-Member Exchange (LMX) quality on creativity and innovation in IT projects within Federal Government IT divisions in the Northeastern United States. I examined leadership challenges in gaining employee support for IT modernization, and how trust, communication, digital competency, and organizational culture shaped engagement. Guided by social exchange theory and self-determination theory, I used data triangulation through semistructured interviews, observations, and surveys with 16 participants. The results of thematic analysis revealed 5 key themes: (a) trust and transparency in leadership, (b) leadership competency, (c) resistance to change and organizational barriers, (d) employee autonomy, and (e) emotional intelligence. High-quality LMX relationships enhanced motivation, reduced resistance, and reflected innovation through trust, empowerment, and psychological safety. In contrast, low-quality LMX relationships led to skepticism, disengagement, and reluctance to adopt IT changes. A notable finding was the impact of digital leadership competency on employee confidence in IT transformation. Leaders lacking technical proficiency struggled to guide teams effectively, limiting success. The results of the study’s conclusions offer recommendations for IT leadership development, including mentorship, technical training, and trust-building strategies. In addition, the results reflected the incorporation of a biblical leadership perspective rooted in stewardship, integrity, and servant leadership. These findings contribute to scholarships and provide practical guidance for improving leadership effectiveness in public sector IT transformation, filling a gap in the literature on leadership behaviors and organizational readiness for innovation