Klasifikasi Intent untuk Task-Oriented Dialog System Menggunakan Arsitektur Long Short-Term Memory (Studi Kasus : E-commerce)

Penggunaan internet di Indonesia telah mengalami perkembangan yang cukup pesat setiap tahunnya. Sebagian besar layanan internet yang digunakan oleh masyarakat Indonesia adalah aplikasi chatting. Aplikasi chatting saat ini diminati oleh berbagai kalangan, yang memungkinkan penggunanya untuk dapat bertukar pesan secara bebas. Perusahaan e-commerce dapat menggunakan aplikasi chatting sebagai sarana bertukar pesan dengan pelanggan. Kesigapan penjual dalam menanggapi pesan pelanggan dapat menumbuhkan persepsi positif terhadap kualitas pelayanan yang diberikan. Namun, masalah muncul ketika jumlah pesan yang masuk dalam sehari terlampau banyak. Dengan sumber daya manusia yang terbatas, membalas satu persatu pesan dapat menyita banyak waktu. Task-oriented dialog agents adalah program yang dapat berkomunikasi dengan pengguna dengan bahasa alami dan didesain khusus untuk menyelesaikan tugas tertentu. Sehingga penggunaannya memiliki potensi besar bagi penjual atau pemilik usaha untuk meningkatkan responsifitas mereka dalam membalas pesan pelanggan pada aplikasi chatting. Arsitektur task-oriented dialog terdiri dari modul Natural Language Understanding (NLU), Dialog Manager, dan Natural Language Generation (NLG). Modul NLU digunakan untuk mengubah masukan pengguna ke dalam bentuk semantik. Long Short-Term Memory (LSTM) merupakan salah satu arsitektur Recurrent Neural Network (RNN) yang terbukti bekerja dengan baik pada tugas klasifikasi intent pada modul NLU. Sehingga penelitian ini menggunakan arsitektur tersebut untuk melakukan klasifikasi intent pada percakapan bahasa Indonesia dengan domain e-commerce. Dalam penerapannya LSTM membutuhkan masukan berupa vektor kata agar proses training untuk menjadi suatu model dapat dilakukan. Sehingga dalam penelitian ini menggunakan pretrained word embedding model untuk melakukan representasi vektor kata. Hasil yang diharapkan dalam penelitian ini adalah model klasifikasi intent dengan nilai akurasi tertinggi untuk percakapan bahasa Indonesia dengan domain e-commerce. ================================================================================================================================ Internet usage in Indonesia has grown quite rapidly every year. Most of internet services used by Indonesian are chat applications. Chat applications are currently used by various group of people, which allow them to be able to exchange messages seamlessly. E-commerce companies can use chat applications to communicate with their customers. Responsiveness of sellers in replying to customer messages can have an effect on feedback regarding the quality of services provided. Problems arise when there are too many messages received by the seller in a day. With limited human resources, replying to those messages one by one can take a lot of time. Task-oriented dialog agents are programs that can communicate with users using natural languages and are specifically designed to complete certain tasks. So that its use has great potential for sellers or business owners to increase their responsiveness in replying to customer messages on chat applications. The task-oriented dialog architecture consists of Natural Language Understanding (NLU), Dialog Manager, and Natural Language Generation (NLG) modules. The NLU module is used to convert user input into semantic form. Long Short-Term Memory (LSTM) is one of the Recurrent Neural Network (RNN) architectures that has proven to work well in the intent classification task of the NLU module. So that this study uses LSTM to classify the intent in Indonesian conversations within the e-commerce domain. In its implementation, LSTM requires input in the form of a word vector so that it can be processed for model training. To do a word vector representations, this study uses a pretrained word embedding model. The expected results in this study are the intent classification model with the highest accuracy for Indonesian conversations within the e-commerce domain

An Experimental and Theoretical Study of Pile Foundations Embedded in Sand Soil

This study aimed to examine the load carrying capacity of model instrumented piles embedded in sand soil, and to develop and verify reliable, highly efficient predictive models to fully correlate the non-linear relationship of pile load-settlement behaviour using a new, self-tuning artificial intelligence (AI) approach. In addition, a new methodology has been developed, in which the most effective pile bearing capacity design parameters can be precisely determined. To achieve this, a series of comprehensive experimental pile load tests were carried out on precast concrete piles, steel closed-ended piles and steel open-ended piles, comprised of three slenderness ratios of 12, 17 and 25, using an innovative calibrated testing rig, designed and manufactured at Liverpool John Moores University. The model piles were tested in a large pile testing chamber at a range of different densities of sand; loose (18%), medium (51%) and dense (83%). It is worth noting that novel structural fibres were utilised and optimised for different volume fractions to enhance the mechanical performance of concrete piles. The obtained results revealed that the higher the values of the of the pile effective length, Lc (embedded length of pile), sand density, and the soil-pile angle of shearing resistance, the higher the axial load magnitudes to reach the yield limit. This can be attributed to the increase in the end bearing point and mobilised shaft resistance. In addition, the plastic mechanism occurring in the surrounding soil was identified as the leading cause for the presence of nonlinearity in the pile-load tests. Furthermore, a new enhanced self-tuning supervised Levenberg-Marquardt (LM) training algorithm, based on a MATLAB environment, was introduced and applied in this process. The proposed algorithm was trained after conducting a comprehensive statistical analysis, the key objectives being to identify and yield reliable information from the most effective input parameters, highlight the relative importance “Beta values” and the statistical significance “Sig values” of each model input variable (IV) on the model output. To assess the accuracy and the efficiency of the employed algorithm, different measuring performance indicators (MPI), suggested in the open literature, were utilised. Common statistical performance indexes, i.e., root mean square error (RMSE), Pearson’s moment correlation coefficient (p), coefficient of determination (R), and mean square error (MSE) for each model were determined. Based on the graphical and numerical comparisons between the experimental and predicted load-settlement values, the results revealed that the optimum models of the LM training algorithm fully characterised load-settlement response with remarkable agreement. Additionally, the proposed algorithm successfully outperformed the conventional approaches, demonstrating the feasibility of the current study. New design charts have been developed to calculate the individual contribution of the most significant pile bearing capacity design parameters “the earth pressure coefficient (K) and the bearing capacity factor (N )”. The improved approach takes into account the change in sand relative density, pile material type, and the pile slenderness ratios. It is therefore a significant improvement over most conventional design methods recommended in the existing design procedures, which do not consider the influence of the most significant parameters that govern the pile bearing capacity design process

Conversação homem-máquina. Caracterização e avaliação do estado actual das soluções de speech recognition, speech synthesis e sistemas de conversação homem-máquina

A comunicação verbal humana é realizada em dois sentidos, existindo uma compreensão de ambas as partes que resulta em determinadas considerações. Este tipo de comunicação, também chamada de diálogo, para além de agentes humanos pode ser constituído por agentes humanos e máquinas. A interação entre o Homem e máquinas, através de linguagem natural, desempenha um papel importante na melhoria da comunicação entre ambos. Com o objetivo de perceber melhor a comunicação entre Homem e máquina este documento apresenta vários conhecimentos sobre sistemas de conversação Homemmáquina, entre os quais, os seus módulos e funcionamento, estratégias de diálogo e desafios a ter em conta na sua implementação. Para além disso, são ainda apresentados vários sistemas de Speech Recognition, Speech Synthesis e sistemas que usam conversação Homem-máquina. Por último são feitos testes de performance sobre alguns sistemas de Speech Recognition e de forma a colocar em prática alguns conceitos apresentados neste trabalho, é apresentado a implementação de um sistema de conversação Homem-máquina. Sobre este trabalho várias ilações foram obtidas, entre as quais, a alta complexidade dos sistemas de conversação Homem-máquina, a baixa performance no reconhecimento de voz em ambientes com ruído e as barreiras que se podem encontrar na implementação destes sistemas