Tech Offers

Crowd Simulation

Crowd Simulation is an AI library that is equipped with a comprehensive set of tools to enable easy creations of group behavioural animations that are provided with flexible controls over the formation shape and movements of groups or flocks, which is extremely useful and valuable for rapid digital games development and simulation.

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Concept-Level Sentiment Analytics

1. OUR SOLUTIONS

Users do not need to change their OS, UI or IDE: our APIs are easy to use and to embed in any framework. Our company offer fine-grained solutions to many subtasks of sentiment analysis, e.g., polarity detection, aspect extraction, subjectivity detection, temporal tagging, named-entity recognition, concept extraction, personality recognition, and sarcasm detection, and they are available in different domains, modalities, and languages.

2. OUR TRANSPARENCY

We show you what data is collected and how each of them is classified. Most companies, instead, adopt a black-box strategy in which they only show you the classification results. This way users can never be sure about how accurate their analysis really is because they usually do not disclose neither the data nor the techniques adopted for classifying such data (which, in most cases, are rather obsolete).

3. OUR APPROACH

NLP research is evolving very fast, and the only way to be up-to-date with it is to be fully immersed in academia. We are not just a business company but also a research lab. We know the current and future trends of NLP, and we always embed the latest techniques in our APIs. Unlike most companies (which tend to focus only on one facet of the problem), we take a very multidisciplinary approach to sentiment analysis.

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Learning Analytics System for Step Based Learning Activities

Learning analytics systems collect, measure and report data about learners to support instructors to understand students’ learning and make informed interventions to optimize learning. Existing technologies mainly make use of log data collected from the learning environment and aggregate statistics such as no. of access to learning resources, learning activity attempts and performances. Those statistics will be used as features of students’ learning to derive learning patterns or train predictive models. While the features related to resource accesses and performances are insightful, the data that captures the process of how students solve the problems in learning activities could provide teachers more insight on students’ mastery of the knowledge and problem-solving strategies. In our learning analytics system for step-based learning activities, we collect, analyze and visualize students’ problem-solving steps and patterns. By correlating the analysis result with resource accesses and performance, teachers, as well as students themselves, can have a more informed intervention/reaction on how to improve learning and achieve learning outcome.

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Malay-English Machine Translation System

This is a tool to translate an English sentence into Malay and vice versa. Developing a translation tool for low-resource languages like Malay has always been a challenge. The main challenge comes from the fact that machine translation systems typically rely on a huge amount of sentence-parallel data, and creating such datasets is an expensive process. In our work, we collected parallel datasets from various sources including News, OpenSubtitiles (OPUS), etc. Therefore, our corpus is quite generic and covers both texts and conversations. The second challenge is to train a Machine Learning model. Neural Machine Translation (NMT) is a recently proposed deep learning architecture that has quickly become the standard approach. It offers an end-to-end architecture with better generalization. In the last few years, researchers have proposed many techniques to improve NMT, including work on handling rare words and using attention mechanisms to align input and output words. Our translation system utilizes the most up-to-date NMT architecture, namely the transformer net and the seq2seq architecture. To train our model we used OpenNMT-py framework, which is a standard in the MT community for its robust and modular implementation.

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Intelligent Internet of Things (IoT) Data Management with Blockchain

The blockchain data management framework for the open, multi-party, and decentralized industrial & commercial markets. Blockchain has been introduced to support distributed, peer-to-peer (P2P) database in which no single entity maintains data and transactions of the system. Instead, all parties in the network can generate, verify, secure, and share the data with privacy and integrity.

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A System for Extracting Named Entities in News Articles

Our technology is a software system for extracting named entities (people, companies, locations) mentioned in news articles, together with readers’ comments. The system implements a fast and effective named entity recognition and linking techniques. It also offers an interactive visualization of the results and the linking process, with associations of the named entities.

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