Improving Communication in Children With ASD

Raising a child with ASD is generally considered a challenging experience for families due to the pervasive difficulties in communication, social skills, and other adaptive behaviors encountered in these children. The family system restructures and adapts to accommodate the needs of the child with ASD. In this chapter, the author highlights the importance of communication skills for the development of children with ASD and summarizes the evidence-based individual interventions for improving them. Although there is robust evidence for the family's beneficial contribution to developing adaptive communication skills in children with ASD, there is still room for uplifting the existing programs in terms of accessibility, efficacy, and culture-based elements. In the final part of the chapter, the author provides recommendations for designing future family interventions addressed to communication skills in children with ASD and argues that culture-specific and systemic factors (such as support policies for children with disabilities) enhance program success.

DEVELOPMENT OF A CIRCUITRY AND CONSTRUCTIVE-TECHNOLOGICAL BASIS FOR COSMIC CHIPS

The article considers the development of adaptive communication and control systems for space and civil purposes to ensure the reliability of operation and increase the accuracy of control. To ensure the resistance of CMOS BIS to ionizing radiation, methods of implementing the isolation unit, the design and technology of working transistors that ensure high stability of their characteris-tics are used.

Investigating the effects of corpus and configuration on assistive input methods

<p>Assistive technologies aim to provide assistance to those who are unable to perform various tasks in their day-to-day lives without tremendous difficulty. This includes — amongst other things — communicating with others. Augmentative and adaptive communication (AAC) is a branch of assistive technologies which aims to make communicating easier for people with disabilities which would otherwise prevent them from communicating efficiently (or, in some cases, at all). The input rate of these communication aids, however, is often constrained by the limited number of inputs found on the devices and the speed at which the user can toggle these inputs. A similar restriction is also often found on smaller devices such as mobile phones: these devices also often require the user to input text with a smaller input set, which often results in slower typing speeds. Several technologies exist with the purpose of improving the text input rates of these devices. These technologies include ambiguous keyboards, which allow users to input text using a single keypress for each character and trying to predict the desired word; word prediction systems, which attempt to predict the word the user is attempting to input before he or she has completed it; and word auto-completion systems, which complete the entry of predicted words before all the corresponding inputs have been pressed. This thesis discusses the design and implementation of a system incorporating the three aforementioned assistive input methods, and presents several questions regarding the nature of these technologies. The designed system is found to outperform a standard computer keyboard in many situations, which is a vast improvement over many other AAC technologies. A set of experiments was designed and performed to answer the proposed questions, and the results of the experiments determine that the corpus used to train the system — along with other tuning parameters — have a great impact on the performance of the system. Finally, the thesis also discusses the impact that corpus size has on the memory usage and response time of the system.</p>

Investigating the effects of corpus and configuration on assistive input methods

<p>Assistive technologies aim to provide assistance to those who are unable to perform various tasks in their day-to-day lives without tremendous difficulty. This includes — amongst other things — communicating with others. Augmentative and adaptive communication (AAC) is a branch of assistive technologies which aims to make communicating easier for people with disabilities which would otherwise prevent them from communicating efficiently (or, in some cases, at all). The input rate of these communication aids, however, is often constrained by the limited number of inputs found on the devices and the speed at which the user can toggle these inputs. A similar restriction is also often found on smaller devices such as mobile phones: these devices also often require the user to input text with a smaller input set, which often results in slower typing speeds. Several technologies exist with the purpose of improving the text input rates of these devices. These technologies include ambiguous keyboards, which allow users to input text using a single keypress for each character and trying to predict the desired word; word prediction systems, which attempt to predict the word the user is attempting to input before he or she has completed it; and word auto-completion systems, which complete the entry of predicted words before all the corresponding inputs have been pressed. This thesis discusses the design and implementation of a system incorporating the three aforementioned assistive input methods, and presents several questions regarding the nature of these technologies. The designed system is found to outperform a standard computer keyboard in many situations, which is a vast improvement over many other AAC technologies. A set of experiments was designed and performed to answer the proposed questions, and the results of the experiments determine that the corpus used to train the system — along with other tuning parameters — have a great impact on the performance of the system. Finally, the thesis also discusses the impact that corpus size has on the memory usage and response time of the system.</p>

AFL: Adaptive Federated Learning Based on Personalized Model and Adaptive Communication

Federated learning (FL) has given indications of being effective as a architecture for distributed machine learning, which can ensure the data security for each client and train the global deep learning model. Due to the rapid development of this technology, issues remain to be fully explored. Among them, the robustness of the system, privacy protection and communication efficiency are the key factors affecting quality of FL. Here we propose a new FL model based on personalized model and adaptive communication, called Adaptive Federated Learning (AFL). Our model mainly uses two mechanisms: a) Each client trains the personalized local model according to its own local data set; b) Adaptive chain communication mode is adopted in federation aggregation to reduce the time spent in synchronizing training result. A large number of experiments on two public datasets: MNIST and CIFAR10 show that our model is more accurate by over 5% compared with the FedAvg, and is also faster by over 10% compared with Chain-PPFL, which provides a very important significance in theory and practical production.

An Adaptive Communication-Efficient Federated Learning to Resist Gradient-Based Reconstruction Attacks

The widely deployed devices in Internet of Things (IoT) have opened up a large amount of IoT data. Recently, federated learning emerges as a promising solution aiming to protect user privacy on IoT devices by training a globally shared model. However, the devices in the complex IoT environments pose great challenge to federate learning, which is vulnerable to gradient-based reconstruction attacks. In this paper, we discuss the relationships between the security of federated learning model and optimization technologies of decreasing communication overhead comprehensively. To promote the efficiency and security, we propose a defence strategy of federated learning which is suitable to resource-constrained IoT devices. The adaptive communication strategy is to adjust the frequency and parameter compression by analysing the training loss to ensure the security of the model. The experiments show the efficiency of our proposed method to decrease communication overhead, while preventing privacy data leakage.

Latte

Emerging software-defined and programmable networking technologies enable more adaptive communication infrastructures. However, leveraging these flexibilities and operating networks more adaptively is challenging, as the underlying infrastructure remains a complex distributed system that is a subject to delays, and as consistency properties need to be preserved transiently, even during network reconfiguration. Motivated by these challenges, we propose Latte, an automated approach to minimize the latency of network update schedules by avoiding unnecessary waiting times and exploiting concurrency, while at the same time provably ensuring a wide range of fundamental consistency properties like waypoint enforcement. To enable automated reasoning about the performance and consistency of software-defined networks during an update, we introduce the model of timed-arc colored Petri nets: an extension of Petri nets which allows us to account for time aspects in asynchronous networks, including characteristic timing behaviors, modeled as timed and colored tokens. This novel formalism may be of independent interest. Latte relies on an efficient translation of specific network update problems into timed-arc colored Petri nets. We show that the constructed nets can be analyzed efficiently via their unfolding into existing timed-arc Petri nets. We integrate Latte into the state-of-the-art model checking tool TAPAAL, and find that in many cases, we are able to reduce the latency of network updates by 90% or more.