Integrating character building into teaching to enhance the students environmental awareness

Due to the growing environmental destruction, it is becoming necessary for people to start to care about the place they live. One way of doing this is through education by making the students aware of their environmental problems. However, the character education in Indonesia   focuses more on improving the quality within and between individuals, and seems to neglect the characters, which shows relationship with environment. Creating students who are environmentally aware and able to take participation to protect their environment basically helps them to posses good moral values. In short, possessing characters which signifies awareness to environmental problems and ability to protect the environment is as important as possessing desirable characters that can help students to succeed in live. This paper discusses about the characters building within Indonesian context, environmental education, language learning and environmental education, and some proposed models to integrate environmental education into teaching

Is my configuration any good: checking usability in an interactive sensor-based activity monitor

We investigate formal analysis of two aspects of usability in a deployed interactive, configurable and context-aware system: an event-driven, sensor-based homecare activity monitor system. The system was not designed from formal requirements or specification: we model the system as it is in the context of an agile development process. Our aim was to determine if formal modelling and analysis can contribute to improving usability, and if so, which style of modelling is most suitable. The purpose of the analysis is to inform configurers about how to interact with the system, so the system is more usable for participants, and to guide future developments. We consider redundancies in configuration rules defined by carers and participants and the interaction modality of the output messages.Two approaches to modelling are considered: a deep embedding in which devices, sensors and rules are represented explicitly by data structures in the modelling language and non-determinism is employed to model all possible device and sensor states, and a shallow embedding in which the rules and device and sensor states are represented directly in propositional logic. The former requires a conventional machine and a model-checker for analysis, whereas the latter is implemented using a SAT solver directly on the activity monitor hardware. We draw conclusions about the role of formal models and reasoning in deployed systems and the need for clear semantics and ontologies for interaction modalities

NewsGPT: ChatGPT Integration for Robot-Reporter

The integration of large language models (LLMs) with social robots has emerged as a promising avenue for enhancing human-robot interactions at a time when news reports generated by artificial intelligence (AI) are gaining in credibility. This integration is expected to intensify and become a more productive resource for journalism, media, communication, and education. In this paper a novel system is proposed that integrates AI's generative pretrained transformer (GPT) model with the Pepper robot, with the aim of improving the robot's natural language understanding and response generation capabilities for enhanced social interactions. By leveraging GPT's powerful language processing capabilities, this system offers a comprehensive pipeline that incorporates voice input recording, speech-to-text transcription, context analysis, and text-to-speech synthesis action generation. The Pepper robot is enabled to comprehend user queries, generate informative responses with general knowledge, maintain contextually relevant conversations, and act as a more domain-oriented news reporter. It is also linked with a news resource and powered with a Google search capability. To evaluate the performance of the framework, experiments were conducted involving a set of diverse questions. The robot's responses were assessed on the basis of eight criteria, including relevance, context, and fluency. Despite some identified limitations, this system contributes to the field of journalism and human-robot interaction by showcasing the potential of integrating LLMs with social robots. The proposed framework opens up opportunities for improving the conversational capabilities of robots, enabling interactions that are smoother, more engaging, and more context aware

Modeling contextual information in neural machine translation

Machine translation has provided impressive translation quality for many language pairs. The improvements over the past few years are largely due to the introduction of neural networks to the field, resulting in the modern sequence-to-sequence neural machine translation models. NMT is at the core of many largescale industrial tools for automatic translation such as Google Translate, Microsoft Translator, Amazon Translate and many others. Current NMT models work on the sentence-level, meaning they are used to translate individual sentences. However, for most practical use-cases, a user is interested in translating a document. In these cases, an MT tool splits a document into individual sentences and translates them independently. As a result, any dependencies between the sentences are ignored. This is likely to result in an incoherent document translation, mainly because of inconsistent translation of ambiguous source words or wrong translation of anaphoric pronouns. For example, it is undesirable to translate “bank” as a “financial bank” in one sentence and then later as a “river bank”. Furthermore, the translation of, e.g., the English third person pronoun “it” into German depends on the grammatical gender of the English antecedent’s German translation. NMT has shown that it has impressive modeling capabilities, but is nevertheless unable to model discourse-level phenomena as it needs access to contextual information. In this work, we study discourse-level phenomena in context-aware NMT. To facilitate the particular studies of interest, we propose several models capable of incorporating contextual information into standard sentence-level NMT models. We direct our focus on several discourse phenomena, namely, coreference (anaphora) resolution, coherence and cohesion. We discuss these phenomena in terms of how well can they be modeled by context-aware NMT, how can we improve upon current state-of-the-art as well as the optimal granularity at which these phenomena should be modeled. We further investigate domain as a factor in context-aware NMT. Finally, we investigate existing challenge sets for anaphora resolution evaluation and provide a robust alternative. We make the following contributions: i) We study the importance of coreference (anaphora) resolution and coherence for context-aware NMT by making use of oracle information specific to these phenomena. ii) We propose a method for improving performance on anaphora resolution based on curriculum learning which is inspired by the way humans organize learning. iii) We investigate the use of contextual information for better handling of domain information, in particular in the case of modeling multiple domains at once and when applied to zero-resource domains. iv) We present several context-aware models to enable us to examine the specific phenomena of interest we already mentioned. v) We study the optimal way of modeling local and global context and present a model theoretically capable of using very large document context. vi) We study the robustness of challenge sets for evaluation of anaphora resolution in MT by means of adversarial attacks and provide a template test set that robustly evaluates specific steps of an idealized coreference resolution pipeline for MT

Handling Homographs in Neural Machine Translation

Homographs, words with different meanings but the same surface form, have long caused difficulty for machine translation systems, as it is difficult to select the correct translation based on the context. However, with the advent of neural machine translation (NMT) systems, which can theoretically take into account global sentential context, one may hypothesize that this problem has been alleviated. In this paper, we first provide empirical evidence that existing NMT systems in fact still have significant problems in properly translating ambiguous words. We then proceed to describe methods, inspired by the word sense disambiguation literature, that model the context of the input word with context-aware word embeddings that help to differentiate the word sense be- fore feeding it into the encoder. Experiments on three language pairs demonstrate that such models improve the performance of NMT systems both in terms of BLEU score and in the accuracy of translating homographs.Comment: NAACL201

Context-Aware Planning and Environment-Aware Memory for Instruction Following Embodied Agents

Accomplishing household tasks such as 'bringing a cup of water' requires planning step-by-step actions by maintaining knowledge about the spatial arrangement of objects and the consequences of previous actions. Perception models of the current embodied AI agents, however, often make mistakes due to a lack of such knowledge but rely on imperfect learning of imitating agents or an algorithmic planner without knowledge about the changed environment by the previous actions. To address the issue, we propose CPEM (Context-aware Planner and Environment-aware Memory) to incorporate the contextual information of previous actions for planning and maintaining spatial arrangement of objects with their states (e.g., if an object has been moved or not) in an environment to the perception model for improving both visual navigation and object interaction. We observe that CPEM achieves state-of-the-art task success performance in various metrics using a challenging interactive instruction following benchmark both in seen and unseen environments by large margins (up to +10.70% in unseen env.). CPEM with the templated actions, named ECLAIR, also won the 1st generalist language grounding agents challenge at Embodied AI Workshop in CVPR'23.Comment: ICCV 202