Task Offloading and Scheduling Strategy for Intelligent Prosthesis in Mobile Edge Computing Environment

Traditional intent recognition algorithms of intelligent prosthesis often use deep learning technology. However, deep learning’s high accuracy comes at the expense of high computational and energy consumption requirements. Mobile edge computing is a viable solution to meet the high computation and real-time execution requirements of deep learning algorithm on mobile device. In this paper, we consider the computation offloading problem of multiple heterogeneous edge servers in intelligent prosthesis scenario. Firstly, we present the problem definition and the detail design of MEC-based task offloading model for deep neural network. Then, considering the mobility of amputees, the mobility-aware energy consumption model and latency model are proposed. By deploying the deep learning-based motion intent recognition algorithm on intelligent prosthesis in a real-world MEC environment, the effectiveness of the task offloading and scheduling strategy is demonstrated. The experimental results show that the proposed algorithms can always find the optimal task offloading and scheduling decision.

Creating a Conversational Agent in Psychotherapy Using Natural Language Processing: Recognizing User Intents (Preprint)

BACKGROUND In recent years there has been a growth of psychological chatbots performing important functions from checking symptoms to providing psychoeducation and guiding self-help exercises. Technologically these chatbots are based on traditional decision-tree algorithms with limited keyword recognition. A key challenge to the development of conversational artificial intelligence is intent recognition or understanding the goal that the user wants to accomplish. The user query on psychological topic is often emotional, highly contextual and non goal-oriented, and therefore may contain vague, mixed or multiple intents. OBJECTIVE In this study we attempt to identify and categorize user intents with relation to psychological topics. METHODS We collected a dataset of 43 000 logs from the iCognito Anti-depression chatbot which consists of user answers to the chatbot questions about the reason of their emotional distress. The data was labeled manually. The BERT model was used for classification. RESULTS We have identified 24 classes of user intents that can be grouped into larger categories, such as: a) intents to improve emotional state; b) intents to improve interpersonal relations; c) intents to improve physical condition; d) intents to solve practical problems; e) intents to make a decision; f) intents to harm oneself or commit suicide; g) intent to blame or criticize oneself. CONCLUSIONS This classification may be used for the development of conversational artificial intelligence in the field of psychotherapy.

Motion Intent Recognition in Intelligent Lower Limb Prosthesis Using One-Dimensional Dual-Tree Complex Wavelet Transforms

The motion intent recognition via lower limb prosthesis can be regarded as a kind of short-term action recognition, where the major issue is to explore the gait instantaneous conversion (known as transitional pattern) between each two adjacent different steady states of gait mode. Traditional intent recognition methods usually employ a set of statistical features to classify the transitional patterns. However, the statistical features of the short-term signals via the instantaneous conversion are empirically unstable, which may degrade the classification accuracy. Bearing this in mind, we introduce the one-dimensional dual-tree complex wavelet transform (1D-DTCWT) to address the motion intent recognition via lower limb prosthesis. On the one hand, the local analysis ability of the wavelet transform can amplify the instantaneous variation characteristics of gait information, making the extracted features of instantaneous pattern between two adjacent different steady states more stable. On the other hand, the translation invariance and direction selectivity of 1D-DTCWT can help to explore the continuous features of patterns, which better reflects the inherent continuity of human lower limb movements. In the experiments, we have recruited ten able-bodied subjects and one amputee subject and collected data by performing five steady states and eight transitional states. The experimental results show that the recognition accuracy of the able-bodied subjects has reached 98.91%, 98.92%, and 97.27% for the steady states, transitional states, and total motion states, respectively. Furthermore, the accuracy of the amputee has reached 100%, 91.16%, and 90.27% for the steady states, transitional states, and total motion states, respectively. The above evidence finally indicates that the proposed method can better explore the gait instantaneous conversion (better expressed as motion intent) between each two adjacent different steady states compared with the state-of-the-art.

Creating an AI-Conversational Agent in Psychotherapy: Recognizing User Intents

In recent years there has been a growth of psychological chatbots performing important functions from checking symptoms to providing psychoeducation and guiding self-help exercises. Technologically these chatbots are based on traditional decision-tree algorithms with limited keyword recognition. A key challenge to the development of conversational artificial intelligence is intent recognition or understanding the goal that the user wants to accomplish. The user query on psychological topic is often emotional, highly contextual and non goal-oriented, and therefore may contain vague, mixed or multiple intents. In this study we made an attempt to identify and categorize user intents with relation to psychological topics using the database of 43 000 messages from iCognito Anti-depression chatbot. We have identified 24 classes of user intents that can be grouped into larger categories, such as: a) intents to improve emotional state; b) intents to improve interpersonal relations; c) intents to improve physical condition; d) intents to solve practical problems; e) intents to make a decision; f) intents to harm oneself or commit suicide; g) intent to blame or criticize oneself. This classification may be used for the development of conversational artificial intelligence in the field of psychotherapy.

MyrrorBot : A Digital Assistant Based on Holistic User Models for Personalized Access to Online Services

In this article, we present MyrrorBot , a personal digital assistant implementing a natural language interface that allows the users to: (i) access online services, such as music, video, news, and food recommendation s, in a personalized way, by exploiting a strategy for implicit user modeling called holistic user profiling ; (ii) query their own user models, to inspect the features encoded in their profiles and to increase their awareness of the personalization process. Basically, the system allows the users to formulate natural language requests related to their information needs. Such needs are roughly classified in two groups: quantified self-related needs (e.g., Did I sleep enough? Am I extrovert? ) and personalized access to online services (e.g., Play a song I like ). The intent recognition strategy implemented in the platform automatically identifies the intent expressed by the user and forwards the request to specific services and modules that generate an appropriate answer that fulfills the query. In the experimental evaluation, we evaluated both qualitative (users’ acceptance of the system, usability) as well as quantitative (time required to complete basic tasks, effectiveness of the personalization strategy) aspects of the system, and the results showed that MyrrorBot can improve the way people access online services and applications. This leads to a more effective interaction and paves the way for further development of our system.

A smarthome conversational agent performing implicit demand-response application planning

In recent years, the growing use of Intelligent Personal Agents in different human activities and in various domains led the corresponding research to focus on the design and development of agents that are not limited to interaction with humans and execution of simple tasks. The latest research efforts have introduced Intelligent Personal Agents that utilize Natural Language Understanding (NLU) modules and Machine Learning (ML) techniques in order to have complex dialogues with humans, execute complex plans of actions and effectively control smart devices. To this aim, this article introduces the second generation of the CERTH Intelligent Personal Agent (CIPA) which is based on the RASA framework and utilizes two machine learning models for NLU and dialogue flow classification. CIPA-Generation B provides a dialogue-story generator that is based on the idea of adjacency pairs and multiple intents, that are classifying complex sentences consisting of two users’ intents into two automatic operations. More importantly, the agent can form a plan of actions for implicit Demand-Response and execute it, based on the user’s request and by utilizing AI Planning methods. The introduced CIPA-Generation B has been deployed and tested in a real-world scenario at Centre’s of Research & Technology Hellas (CERTH) nZEB SmartHome in two different domains, energy and health, for multiple intent recognition and dialogue handling. Furthermore, in the energy domain, a scenario that demonstrates how the agent solves an implicit Demand-Response problem has been applied and evaluated. An experimental study with 36 participants further illustrates the usefulness and acceptance of the developed conversational agent-based system.

A Novel FPGA-Based Intent Recognition System Utilizing Deep Recurrent Neural Networks

In recent years, systems that monitor and control home environments, based on non-vocal and non-manual interfaces, have been introduced to improve the quality of life of people with mobility difficulties. In this work, we present the reconfigurable implementation and optimization of such a novel system that utilizes a recurrent neural network (RNN). As demonstrated in the real-world results, FPGAs have proved to be very efficient when implementing RNNs. In particular, our reconfigurable implementation is more than 150× faster than a high-end Intel Xeon CPU executing the reference inference tasks. Moreover, the proposed system achieves more than 300× the improvements, in terms of energy efficiency, when compared with the server CPU, while, in terms of the reported achieved GFLOPS/W, it outperforms even a server-tailored GPU. An additional important contribution of the work discussed in this study is that the implementation and optimization process demonstrated can also act as a reference to anyone implementing the inference tasks of RNNs in reconfigurable hardware; this is further facilitated by the fact that our C++ code, which is tailored for a high-level-synthesis (HLS) tool, is distributed in open-source, and can easily be incorporated to existing HLS libraries.

Early Classification of Intent for Maritime Domains Using Multinomial Hidden Markov Models

The need for increased maritime security has prompted research focus on intent recognition solutions for the naval domain. We consider the problem of early classification of the hostile behavior of agents in a dynamic maritime domain and propose our solution using multinomial hidden Markov models (HMMs). Our contribution stems from a novel encoding of observable symbols as the rate of change (instead of static values) for parameters relevant to the task, which enables the early classification of hostile behaviors, well before the behavior has been finalized. We discuss our implementation of a one-versus-all intent classifier using multinomial HMMs and present the performance of our system for three types of hostile behaviors (ram, herd, block) and a benign behavior.