Greece

Despite the success of Convolutional Neural Network-based Computer-Aided Diagnosis research, its clinical applications remain challenging. Accordingly, developing medical Artificial Intelligence (AI) fitting into a clinical environment requires identifying/bridging the gap between AI and Healthcare sides. Since the biggest problem in Medical Imaging lies in data paucity, confirming the clinical relevance for diagnosis of research-proven image augmentation techniques is essential. Therefore, we hold a clinically valuable AI-envisioning workshop among Japanese Medical Imaging experts, physicians, and generalists in Healthcare/Informatics. Then, a questionnaire survey for physicians evaluates our pathology-aware Generative Adversarial Network (GAN)-based image augmentation projects in terms of Data Augmentation and physician training. The workshop reveals the intrinsic gap between AI/Healthcare sides and solutions on Why (i.e., clinical significance/interpretation) and How (i.e., data acquisition, commercial deployment, and safety/feeling safe). This analysis confirms our pathology-aware GANs&#39; clinical relevance as a clinical decision support system and non-expert physician training tool. Our findings would play a key role in connecting inter-disciplinary research and clinical applications, not limited to the Japanese medical context and pathology-aware GANs.

AIAI 2020

Bridging the Gap between AI and Healthcare Sides: Towards Developing Clinically Relevant AI-Powered Diagnosis Systems

computer-aided diagnosis

generative adversarial networks

data augmentation

We are pleased to announce the Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI-24), which will be held in Vancouver, British Columbia at the Vancouver Convention Centre – West Building from 20-27 February, 2024.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-24 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

We expect for AAAI-24 to be an in-person conference – one author of all accepted papers will be expected to present work in person unless there are exceptional circumstances that prevent this.<br><br><br><br>

In order to access the AAAI-24 event page you need to register [here](https://aaai.org/aaai-conference/registration/)

AAAI 2024

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. 

Despite the success of Convolutional Neural Network-based Computer-Aided Diagnosis research, its clinical applications remain challenging. Accordingly, developing medical Artificial Intelligence (AI) fitting into a clinical environment requires identifying/bridging the gap between AI and Healthcare sides. Since the biggest problem in Medical Imaging lies in data paucity, confirming the clinical relevance for diagnosis of research-proven image augmentation techniques is essential. Therefore, we hold a clinically valuable AI-envisioning workshop among Japanese Medical Imaging experts, physicians, and generalists in Healthcare/Informatics. Then, a questionnaire survey for physicians evaluates our pathology-aware Generative Adversarial Network (GAN)-based image augmentation projects in terms of Data Augmentation and physician training. The workshop reveals the intrinsic gap between AI/Healthcare sides and solutions on Why (i.e., clinical significance/interpretation) and How (i.e., data acquisition, commercial deployment, and safety/feeling safe). This analysis confirms our pathology-aware GANs' clinical relevance as a clinical decision support system and non-expert physician training tool. Our findings would play a key role in connecting inter-disciplinary research and clinical applications, not limited to the Japanese medical context and pathology-aware GANs.

technical paper

AIAI 2020 (16th International Conference on Artificial Intelligence Applications and Innovations) should be held in Halkidiki, Greece. Due to current Covid-19 situation the conference will be held virtually. Underline will host a repository of video talks and preserve them for the future. Videos will be released on Underline one day after the conference.

Cancer, which involves the dysregulation of genes via multiple mechanisms, is unlikely to be fully explained by a single data type. By combining different "omes", researchers can increase the discovery of novel biomolecular associations with disease-related phenotypes. Investigation of functional relations among genes associated with the same disease condition may further help to develop more accurate disease relevant prediction models. In this work, we present an integrative framework called Data & Analytic Integrator (DAI), to explore the relationship between different omics via different mathematical formulations and algorithms. In particular, we investigate the combinatorial use of molecular knowledge identified from omics integration methods netDx, iDRW and SSL, by fusing the derived aggregated similarity matrices and by exploiting these in a semi-supervised learner. The analysis workflows were applied to real-life data for ovarian can-cer and underlined the benefits of joint data and analytic integration.

Multi-omics data and analytics integration in ovarian cancer

The use of chatbots evolved rapidly in numerous fields in recent years. In this presentation, we first present a historical overview of the evolution of the international community's interest in chatbots. Next, we discuss the motivations that drive the use of chatbots, and we clarify chatbots' usefulness in a variety of areas. Moreover, we highlight the impact of social stereotypes on chat-bots design. After clarifying necessary technological concepts, we move on to a chatbot classification based on various criteria, such as the area of knowledge they refer to, the need they serve and others. Furthermore, we present the general architecture of modern chatbots while also mentioning the main platforms for their creation.

An Overview of Chatbot Technology

Word Embeddings are used widely in multiple Natural Language Processing (NLP) applications. They are coordinates associated with each word in a dictionary, inferred from statistical properties of these words in a large corpus. In this paper we introduce the notion of ``concept'' as a list of words that have shared semantic content. We use this notion to analyse the learnability of certain concepts, defined as the capability of a classifier to recognise unseen members of a concept after training on a random subset of it. We first use this method to measure the learnability of concepts on pretrained word embeddings. We then develop a statistical analysis of concept learnability, based on hypothesis testing and ROC curves, in order to compare the relative merits of various embedding algorithms using a fixed corpora and hyper parameters. We find that all embedding methods capture the semantic content of those word lists, but fastText performs better than the others.

On the Learnability of Concepts With Applications to Comparing Word Embedding Algorithms

The automation of fake news detection is the focus of a great deal of scientific research. With the rise of social media over the years, there has been a strong preference for users to be informed using their social media account, leading to a proliferation of fake news through them. This presentation of the paper entitled "Hong Kong Protests: Using Natural Language Processing for Fake News Detection on Twitter" evaluates the veracity of politically-oriented news and in particular the tweets about the recent event of Hong Kong protests, with the aid of a dataset recently published by Twitter. From this dataset, Chinese tweets are translated into English, which are kept along with originally English tweets. By utilizing a language-independent filtering process, relevant tweets are identified. To complete the dataset, tweets originating from valid sources are used as the real portion, with journalists rather than news agencies being considered, which constitutes a novel aspect of the methodology. Well-known Machine Learning algorithms are used to classify tweets, which are represented by a feature value vector that is extracted, selected and preprocessed from the datasets and mainly revolves around language use, with word entropy being a novel feature. The results derived from these algorithms highlight morphological, lexical and vocabulary differences between tweets spreading fake and real news, which are for the most part in accordance with past related work.

Hong Kong Protests: Using Natural Language Processing for Fake News Detection on Twitter

Nowadays, Intelligent Personal Agents include Natural Language Understanding (NLU) modules, that utilize Machine Learning(ML), which can be included in different kind of applications in order to enable the translation of users’ input into different kinds of actions, as well as ML modules that handle dialogue. This translation is attained by the matching of a user’s sentence with an intent contained in an Agent.This paper introduces the first generation of the CERTH Intelligent Per-sonal Agent (CIPA) which is based on the RASA1framework and utilizes two machine learning models for NLU and dialogue flow classification.Besides the architecture of CIPA—Generation A, a novel dialogue-story generator that is based on the idea of adjacency pairs is introduced. By utilizing on this novel-generator, the agent is able to create all the possible dialog trees in order to handle conversations without training on existing data in contrast with the majority of the current alternative solutions. CIPA supports multiple intents and it is capable of classifying complex sentences consisting of two user’s intents into two automatic operations from the part of the agent. The introduced CIPA—GenerationA has been deployed and tested in a real-world scenario at Centre’s ofResearch & Technology Hellas (CERTH) nZEB Smart Home2in two different domains, energy and health domain.

Applying an Intelligent Personal Agent on a Smart Home using a Novel Dialogue Generator

Nowadays, the fashion industry is moving towards fast fashion, offering a large selection of garment products in a quicker and cheaper manner. To this end, the fashion designers are required to come up with a wide and diverse amount of fashion products in a short time frame. At the same time, the fashion retailers are oriented towards using technology, in order to design and provide products tailored to their consumers’ needs, in sync with the newest fashion trends. In this work, we propose an artificial intelligence system which operates as a personal assistant to a fashion product designer. The system’s architecture and all its components are presented, with emphasis on the data collection and data clustering subsystems. In our use case scenario, datasets of garment products are retrieved from two different sources and are transformed into a specific format by making use of Natural Language Processes. The two datasets are clustered separately using different mixed-type clustering algorithms and comparative results are provided, highlighting the usefulness of the clustering procedure in the clothing product recommendation problem.

Towards fashion recommendation: An AI system for clothing data retrieval and analysis

This presentation documents the efforts in implementing lyric generation machine learning models in the Greek language for the genre of Éntekhno music. To accomplish this, we used three different Long Short-Term Memory Recurrent Neural Network approaches. The first method utilizes word-level bi-directional network models, the second method expands on the first by learning the word embeddings on the initial layer of the network, while the last method is based on a char-level network model. Our experimental procedure, which utilized a high sample of human judges, shows that texts of lyrics generated by our models are of high quality and are not that easily distinguishable from actual lyrics.

Greek Lyrics Generation

Question Answering (QA) and Question Generation (QG) have been subjects of an intensive study in recent years and much progresshas been made in both areas. However, works on combining these twotopics mainly focus on how QG can be used to improve QA results. Through existing Natural Language Processing (NLP) techniques, we have implemented a tool that addresses these two topics separately. We further use them jointly in a pipeline. Thus, our goal is to understand how these modules can help each other. For QG, our methodology employs a detailed analysis of the relevant content of a sentence through Part-of-speech (POS) tagging and Named Entity Recognition (NER). Ensuring loose coupling with the QA task, in the latter we use Information Retrieval to rank sentences that might contain relevant information regarding a certain question, together with Open Information Retrievalto analyse the sentences. In its current version, the QG tool takes a sentence to formulate a simple question. By connecting QG with the QA component, we provide a means to effortlessly generate a test set for QA. While our current QA approach shows promising results, when enhancing the QG component we will, in the future, provide questions for which a more elaborated QA will be needed. The generated QA datasets contribute to QA evaluation, while QA proves to be an important technique for assessing the ambiguity of the questions.

Exploring NLP and Information Extraction to jointly address Question Generation and Answering

With image processing, robots acquired visual perception skills; enabling them to become autonomous. Since the emergence of Artificial Intelligence (AI), sophis-ticated tasks such as object identification have become possible through inferenc-ing Artificial Neural Networks (ANN). Be that as it may, Autonomous Mobile Robots (AMR) are Embedded Systems (ESs) with limited on-board resources. Thus, efficient techniques in ANN inferencing are required for real-time perfor-mance. This paper presents the process of optimizing ANNs inferencing using tensor-based optimization on embedded Graphical Processing Unit (GPU) with Computer Unified Device Architecture (CUDA) platform for parallel acceleration on ES. This research evaluates renowned network, namely, You-Only-Look-Once (YOLO), on NVIDIA Jetson TX2 System-On-Module (SOM). The find-ings of this paper display a significant improvement in inferencing speed in terms of Frames-Per-Second (FPS) up to 3.5 times the non-optimized inferencing speed. Furthermore, the current CUDA model and TensorRT optimization tech-niques are studied, comments are made on its implementation for inferencing, and improvements are proposed based on the results acquired. These findings will contribute to ES developers and industries will benefit from real-time perfor-mance inferencing for AMR automation solutions

Tensor-based CUDA Optimization for ANN Inferencing using Parallel Acceleration on Embedded GPU

Authorship attribution attempts to identify the authors behind texts and has important applications mainly in cyber-security, digital humanities and social media analytics. An especially challenging but very realistic scenario is cross-domain attribution where texts of known authorship (training set) differ from texts of disputed authorship (test set) in topic or genre. In this paper, we modify a successful authorship verification approach based on a multi-headed neural network language model and combine it with pre-trained language models. Based on experiments on a controlled corpus covering several text genres where topic and genre is specifically controlled, we demonstrate that the proposed approach achieves very promising results. We also demonstrate the crucial effect of the normalization corpus in cross-domain attribution.

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