A substantial disadvantage of traditional learning is that all students follow the same learning sequence, but not all of them have the same background of knowledge, the same preferences, the same learning goals, and the same needs. Traditional teaching resources, such as textbooks, in most cases pursue students to follow fixed sequences during the learning process, thus impairing their performance. Learning sequencing is an important research issue as part of the learning process because no fixed learning paths will be appropriate for all learners. For this reason, many research papers are focused on the development of mechanisms to offer personalization on learning paths, considering the learner needs, interests, behaviors, and abilities. In most cases, these researchers are totally focused on the student's preferences, ignoring the level of difficulty and the relation degree that exists between various concepts in a course. This research paper presents the possibility of constructing personalized learning paths using genetic algorithm-based model, encountering the level of difficulty and relation degree of the constituent concepts of a course. The experimental results shows that the genetic algorithm is suitable to generate optimal learning paths based on learning object difficulty level, duration, rating, and relation degree between each learning object as elementary parts of the sequence of the learning path. From these results compared to the quality of the traditional learning path, we observed that even the quality of the weakest learning path generated by our GA approach is in a favor compared to quality of the traditional learning path, with a difference of 3.59\%, while the highest solution generated in the end resulted 8.34\% in favor of our proposal compared to the traditional learning paths.

This article describes a novel approach to expand in run-time the knowledge base of an Artificial Conversational Agent. A technique for automatic knowledge extraction from the user's sentence and four methods to insert the new acquired concepts in the knowledge base have been developed and integrated into a system that has already been tested for knowledge-based conversation between a social humanoid robot and residents of care homes. The run-time addition of new knowledge allows overcoming some limitations that affect most robots and chatbots: the incapability of engaging the user for a long time due to the restricted number of conversation topics. The insertion in the knowledge base of new concepts recognized in the user's sentence is expected to result in a wider range of topics that can be covered during an interaction, making the conversation less repetitive. Two experiments are presented to assess the performance of the knowledge extraction technique, and the efficiency of the developed insertion methods when adding several concepts in the Ontology.

In a conventional domain adaptation person Re-identification (Re-ID) task, both the training and test images in target domain are collected under the sunny weather. However, in reality, the pedestrians to be retrieved may be obtained under severe weather conditions such as hazy, dusty and snowing, etc. This paper proposes a novel Interference Suppression Model (ISM) to deal with the interference caused by the hazy weather in domain adaptation person Re-ID. A teacherstudent model is used in the ISM to distill the interference information at the feature level by reducing the discrepancy between the clear and the hazy intrinsic similarity matrix. Furthermore, in the distribution level, the extra discriminator is introduced to assist the student model make the interference feature distribution more clear. The experimental results show that the proposed method achieves the superior performance on two synthetic datasets than the stateof-the-art methods. The related code will be released online https://github.com/pangjian123/ISM-ReID.

Having access to multi-modal cues (e.g. vision and audio) empowers some cognitive tasks to be done faster compared to learning from a single modality. In this work, we propose to transfer knowledge across heterogeneous modalities, even though these data modalities may not be semantically correlated. Rather than directly aligning the representations of different modalities, we compose audio, image, and video representations across modalities to uncover richer multi-modal knowledge. Our main idea is to learn a compositional embedding that closes the cross-modal semantic gap and captures the task-relevant semantics, which facilitates pulling together representations across modalities by compositional contrastive learning. We establish a new, comprehensive multi-modal distillation benchmark on three video datasets: UCF101, ActivityNet, and VGGSound. Moreover, we demonstrate that our model significantly outperforms a variety of existing knowledge distillation methods in transferring audio-visual knowledge to improve video representation learning. Code is released here: https://github.com/yanbeic/CCL.

Would you like to learn how to tell your robotics/AI story to the public? Robohub and AIhub are testing a new workshop to train you as the next generation of communicators. You will learn to quickly create your story and shape it to any format, from short tweets to blog posts and beyond. In addition, you will learn how to communicate about robotics/AI in a realistic way (avoiding the hype), and will receive tips from top communicators, science journalists and ealy career researchers. If you feel like being part of our beta testers, join this free workshop to experience how much impact science communication can have on your professional journey!

Salesforce today rolled out the next generation of Service Cloud, including enhancements to Cloud Voice and Einstein Bots. The company says that the features and products are intended to address the new reality brought about by the coronavirus pandemic. Over the past year, service agents moved quickly to work from home but were forced to rely on legacy technology that wasn't designed to manage distributed workforces. Today, parts of the world are beginning to reopen, but these reopenings are raising questions around updated policies, protocols, and safety measures. This adds a new level of challenge for agents, who are already contending with increased workloads.

The Center for Connected Autonomy and Artificial Intelligence is housed in the state-of-the-art Engineering East building on the Boca Raton campus. Artificial intelligence technologies are quickly evolving and changing every aspect of industry in the United States and globally. Artificial intelligence enables autonomy by robotic mobility and control learned through examples and computational decision-making and estimation from data using past training data experience. It has the ability to process large amounts of data much faster and make predictions more accurately than humanly possible. To rapidly advance the field of artificial intelligence and autonomy, Florida Atlantic University's College of Engineering and Computer Science recently unveiled its "Center for Connected Autonomy and Artificial Intelligence" (CCA-AI), a cutting-edge center designed to accelerate the development of innovative artificial intelligence and autonomy solutions.

About program The Post Graduate Diploma programs in Artificial Intelligence and Machine Learning are aimed to transform graduates to industry ready professionals, skilled in the above-mentioned areas, ready to be hired. The program will include intensive coursework and hand-on projects crafted by experts, to prepare market ready workforce who will address the next generation problems in some of the most trending areas of the industry. About program The Post Graduate Diploma programs in Artificial Intelligence and Machine Learning are aimed to transform graduates to industry ready professionals, skilled in the above-mentioned areas, ready to be hired. The program will include intensive coursework and hand-on projects crafted by experts, to prepare market ready workforce who will address the next generation problems in some of the most trending areas of the industry. The Post Graduate Diploma programs in Artificial Intelligence and Machine Learning are aimed to transform graduates to industry ready professionals, skilled in the above-mentioned areas, ready to be hired.

Reinforcement learning techniques achieved human-level performance in several tasks in the last decade. However, in recent years, the need for interpretability emerged: we want to be able to understand how a system works and the reasons behind its decisions. Not only we need interpretability to assess the safety of the produced systems, we also need it to extract knowledge about unknown problems. While some techniques that optimize decision trees for reinforcement learning do exist, they usually employ greedy algorithms or they do not exploit the rewards given by the environment. This means that these techniques may easily get stuck in local optima. In this work, we propose a novel approach to interpretable reinforcement learning that uses decision trees. We present a two-level optimization scheme that combines the advantages of evolutionary algorithms with the advantages of Q-learning. This way we decompose the problem into two sub-problems: the problem of finding a meaningful and useful decomposition of the state space, and the problem of associating an action to each state. We test the proposed method on three well-known reinforcement learning benchmarks, on which it results competitive with respect to the state-of-the-art in both performance and interpretability. Finally, we perform an ablation study that confirms that using the two-level optimization scheme gives a boost in performance in non-trivial environments with respect to a one-layer optimization technique.

In recent years, with the increasing popularity of "Smart Technology", the number of Internet of Things (IoT) devices and systems have surged significantly. Various IoT services and functionalities are based on the analytics of IoT streaming data. However, IoT data analytics faces concept drift challenges due to the dynamic nature of IoT systems and the ever-changing patterns of IoT data streams. In this article, we propose an adaptive IoT streaming data analytics framework for anomaly detection use cases based on optimized LightGBM and concept drift adaptation. A novel drift adaptation method named Optimized Adaptive and Sliding Windowing (OASW) is proposed to adapt to the pattern changes of online IoT data streams. Experiments on two public datasets show the high accuracy and efficiency of our proposed adaptive LightGBM model compared against other state-of-the-art approaches. The proposed adaptive LightGBM model can perform continuous learning and drift adaptation on IoT data streams without human intervention.