APLIKASI PELAYANAN DAN PENYEDIA INFORMASI BERBASIS CHATBOT MENGGUNAKAN DEEP LEARNING DI UNIVERSITAS ISLAM MAJAPAHIT

Di Universitas Islam Majapahit, informasi bagi calon mahasiswa bisa didapatkan dengan cara datang ke kantor pelayanan informasi. Masalahnya, kantor pelayanan informasi memiliki keterbatasan jam dan hari kerja. Tentu menyulitkan bagi calon mahasiswa yang ingin mendapatkan informasi secara cepat. Aplikasi chatbot ini hadir sebagai salah satu solusi digunakan sebagai media layanan kepada siapa saja yang membutuhkan informasi secara cepat dan akurat. Siapapun bisa mengakses aplikasi kapanpun, dimanapun tanpa perlu khawatir terbatas pada jam dan hari kerja, dan mendapat respon yang diinginkan secara cepat. Menggunakan salah satu metode deep learning RNN (Recurrent Neural Network) yang merupakan jaringan saraf tiruan iteratif yang pemrosesannya dipanggil berulang kali untuk memproses input yang biasanya berupa data sekuensial.yang dan didukung oleh algoritma optimasi SGD (Stochastic Gradient Descent). Hasilnya model hidden layer 1 dengan 64 neuron dan hidden layer 2 32 neuron, trainingnya menghasilkan akurasi 93%, model 32x32 89% dan model 64x64 memiliki tingkat akurasi tertinggi dengan 94%

Deep Reinforcement Learning for Chatbots Using Clustered Actions and Human-Likeness Rewards

Training chatbots using the reinforcement learning paradigm is challenging due to high-dimensional states, infinite action spaces and the difficulty in specifying the reward function. We address such problems using clustered actions instead of infinite actions, and a simple but promising reward function based on human-likeness scores derived from human-human dialogue data. We train Deep Reinforcement Learning (DRL) agents using chitchat data in raw text—without any manual annotations. Experimental results using different splits of training data report the following. First, that our agents learn reasonable policies in the environments they get familiarised with, but their performance drops substantially when they are exposed to a test set of unseen dialogues. Second, that the choice of sentence embedding size between 100 and 300 dimensions is not significantly different on test data. Third, that our proposed human-likeness rewards are reasonable for training chatbots as long as they use lengthy dialogue histories of ≥10 sentences

A field-based recommender system for crop disease detection using machine learning

This study investigates crop disease monitoring with real-time information feedback to smallholder farmers. Proper crop disease diagnosis tools and information about agricultural practices are key to growth and development in the agricultural sector. The research was piloted in a rural community of smallholder farmers having 100 farmers participating in a system that performs diagnosis on cassava diseases and provides advisory recommendation services with real-time information. Here, we present a field-based recommendation system that provides real-time feedback on crop disease diagnosis. Our recommender system is based on question–answer pairs, and it is built using machine learning and natural language processing techniques. We study and experiment with various algorithms that are considered state-of-the-art in the field. The best performance is achieved with the sentence BERT model (RetBERT), which obtains a BLEU score of 50.8%, which we think is limited by the limited amount of available data. The application tool integrates both online and offline services since farmers come from remote areas where internet is limited. Success in this study will result in a large trial to validate its applicability for use in alleviating the food security problem in sub-Saharan Africa.</p

A Maturity Assessment Framework for Conversational AI Development Platforms

Conversational Artificial Intelligence (AI) systems have recently sky-rocketed in popularity and are now used in many applications, from car assistants to customer support. The development of conversational AI systems is supported by a large variety of software platforms, all with similar goals, but different focus points and functionalities. A systematic foundation for classifying conversational AI platforms is currently lacking. We propose a framework for assessing the maturity level of conversational AI development platforms. Our framework is based on a systematic literature review, in which we extracted common and distinguishing features of various open-source and commercial (or in-house) platforms. Inspired by language reference frameworks, we identify different maturity levels that a conversational AI development platform may exhibit in understanding and responding to user inputs. Our framework can guide organizations in selecting a conversational AI development platform according to their needs, as well as helping researchers and platform developers improving the maturity of their platforms.Comment: 10 pages, 10 figures. Accepted for publication at SAC 2021: ACM/SIGAPP Symposium On Applied Computin

Understanding eWhoring

In this paper, we describe a new type of online fraud, referred to as `eWhoring' by offenders. This crime script analysis provides an overview of the `eWhoring' business model, drawing on more than 6,500 posts crawled from an online underground forum. This is an unusual fraud type, in that offenders readily share information about how it is committed in a way that is almost prescriptive. There are economic factors at play here, as providing information about how to make money from `eWhoring' can increase the demand for the types of images that enable it to happen. We find that sexualised images are typically stolen and shared online. While some images are shared for free, these can quickly become `saturated', leading to the demand for (and trade in) more exclusive `packs'. These images are then sold to unwitting customers who believe they have paid for a virtual sexual encounter. A variety of online services are used for carrying out this fraud type, including email, video, dating sites, social media, classified advertisements, and payment platforms. This analysis reveals potential interventions that could be applied to each stage of the crime commission process to prevent and disrupt this crime type.This work was supported by the UK Engineering and Physical Sciences Research Council (EPSRC) [grant EP/M020320/1] and by the Comunidad de Madrid (Spain) under the project CYNAMON (P2018/TCS-4566), co-financed by European Structural Funds (ESF and FEDER)

Conversational artificial intelligence - demystifying statistical vs linguistic NLP solutions

yesThis paper aims to demystify the hype and attention on chatbots and its association with conversational artificial intelligence. Both are slowly emerging as a real presence in our lives from the impressive technological developments in machine learning, deep learning and natural language understanding solutions. However, what is under the hood, and how far and to what extent can chatbots/conversational artificial intelligence solutions work – is our question. Natural language is the most easily understood knowledge representation for people, but certainly not the best for computers because of its inherent ambiguous, complex and dynamic nature. We will critique the knowledge representation of heavy statistical chatbot solutions against linguistics alternatives. In order to react intelligently to the user, natural language solutions must critically consider other factors such as context, memory, intelligent understanding, previous experience, and personalized knowledge of the user. We will delve into the spectrum of conversational interfaces and focus on a strong artificial intelligence concept. This is explored via a text based conversational software agents with a deep strategic role to hold a conversation and enable the mechanisms need to plan, and to decide what to do next, and manage the dialogue to achieve a goal. To demonstrate this, a deep linguistically aware and knowledge aware text based conversational agent (LING-CSA) presents a proof-of-concept of a non-statistical conversational AI solution

Integrating Mindfulness: Enhancing Art and Design Student Well-being through AI Voice Assistants and Wearable Technology

Recognizing the specific constraints of their academic and creative work, this study focuses on how an AI-enhanced application may provide personalized support for mindfulness and well-being for art and design students. Using Research through Design and Research for Design approaches, it develops a user-centric application that allows for accessible and tailored mindfulness exercises via voice interactions. The project's scope was primarily focused on integrating voice assistant technology to determine its usefulness in engaging students in mindfulness activities. This study seeks to provide light on innovative approaches to improving well-being and creativity among students in creative fields by evaluating the app's usability and impact on facilitating mindfulness