ABSTRACT Artificial intelligence (AI) technology has brought groundbreaking changes to the healthcare domain. Specifically, the integration of a medical dialogue system (MDS) has facilitated interactions with patients, identifying meaningful information such as symptoms and medications from their dialogue history to generate appropriate responses. However, shortcomings arise when MDS lacks access to the patient's cumulative history or prior domain knowledge, resulting in the generation of inaccurate responses. To address this challenge, we propose a medical domain‐incremental task‐oriented dialogue generator using experience replay (MeDi‐TODER) that applies the continual learning technique to the medical task‐oriented dialogue generator. By strategically sampling and storing exemplars from previous domains and rehearsing it as it learns, the model effectively retains knowledge and can respond to the novel domains. Extensive experiments demonstrated that MeDi‐TODER significantly outperforms other models that lack continual learning in both natural language generation and natural language understanding. Notably, our proposed method achieves the highest scores, surpassing the upper‐class benchmarks.

Summary The rapid growth of the Internet of Things (IoT) has led to its widespread adoption in various industries, enabling enhanced productivity and efficient services. Integrating IoT systems with existing enterprise application systems has become common practice. However, this integration necessitates reevaluating and reworking current Enterprise Architecture (EA) models and Expert Systems (ES) to accommodate IoT and cloud technologies. Enterprises must adopt a multifaceted view and automate various aspects, including operations, data management, and technology infrastructure. Machine Learning (ML) is a powerful IoT and smart automation tool within EA. Despite its potential, a need for dedicated work focuses on ML applications for IoT services and systems. With IoT being a significant field, analyzing IoT‐generated data and IoT‐based networks is crucial. Many studies have explored how ML can solve specific IoT‐related challenges. These mutually reinforcing technologies allow IoT applications to leverage sensor data for ML model improvement, leading to enhanced IoT operations and practices. Furthermore, ML techniques empower IoT systems with knowledge and enable suspicious activity detection in smart systems and objects. This survey paper conducts a comprehensive study on the role of ML in IoT applications, particularly in the domains of automation and security. It provides an in‐depth analysis of the state‐of‐the‐art ML approaches within the context of IoT, highlighting their contributions, challenges, and potential applications.

Due to the increase in world population, arable land has been reduced. Consequently, the concept of urban greenhouses is on the rise. Smart greenhouses need to monitor physical parameters for the healthy growth of plants from remote locations. A digital twin is a representation of physical assets in the digital world, and this emerging technology has opened up opportunities for efficient system development for Industry 4.0. The digital twin receives real-time operational data to monitor the asset in the digital domain. It performs real-time processing, data analysis, and machine learning to predict optimized decisions. In the era of next-generation mobile networks, IoT devices can communicate and perform their remote operations in a timely manner. In smart greenhouse technology, the digital twin could be a revolutionary substitute for real-time remote monitoring and process management. However, there has been limited work on digital twin-driven smart greenhouse technology. In this paper, a process management framework is developed that can be interpreted as a machine learning and cloud-based data-driven digital twin for smart greenhouses. The proposed framework consists of three layers: the physical, fog, and cloud layers. The physical greenhouse measurements are monitored using a highly immersive cloud-based, real-time 3D environment. We present an example architecture using commercial cloud and open-source tools to verify the proof of concept. Additionally, different ML techniques are utilized to predict the operational requirements for smart greenhouses.

In recent years, the Internet has facilitated the emergence of social media platforms as significant channels for individuals to express their thoughts and engage in instantaneous interactions. However, the reliance on online reviews has also given rise to deceptive practices, where anonymous spammers generate fake reviews to manipulate the perception of a product. Ensuring the integrity of the online review system requires identifying and mitigating fake reviews. While existing machine learning (ML)- and neural network (NN)-based sentiment analysis methods can detect deceptive reviews, they often suffer from long training times, high computational resource requirements, and memory constraints. This study aims to overcome these limitations by introducing a transformer-based “deceptive bidirectional encoder representations from transformers (DeceptiveBERT) model.” This model utilizes contextual representations to enhance the precision of deceptive review identification. Transfer learning is employed to leverage knowledge from a pre-existing BERT base-uncased word embedding model, enabling efficient feature extraction. The proposed model incorporates a combination of classification layers to categorize reviews into two distinct categories: deceptive and truthful. Additionally, the study addresses the challenge of imbalanced datasets by utilizing three separate datasets and implementing appropriate methodologies for dataset curation. The effectiveness of the DeceptiveBERT model was evaluated through experimentation. The results demonstrate its efficacy, with the model achieving accuracy rates of 75%, 84.79%, and 81.08% on the Ott, YelpNYC, and YelpZip datasets, respectively.

In 2015, the Korean government launched a major software education initiative. The urgency to rally the nation around the 4th Industrial Revolution is the driving force. It consists of two major components. One is to enhance software (computer science) education in universities, and another is to make coding education mandatory in elementary and middle schools.

On-line data center applications render challenging network latency demands to meet their service level requirements. These applications, however, frequently suffer from increased latency due to the packet loss and queueing delay at the network switches. These are mainly as a result of the momentary massive bursts by the Partition/Aggregation application traffic patterns, which causes incast network congestion at the network switches. In this paper, we propose a scalable congestion control protocol, called SCCP. Our scheme effectively limits the data rate of the TCP senders by leveraging the Software Defined Networking (SDN) switches, so that the total utilization does not exceed the bottleneck link capacity. Furthermore, SCCP can be easily deployed to the existing SDN data center switches by extending the OpenFlow specifications. Our Open vSwitch-based prototype experiments and ns-3 simulations show that SCCP is scalable for up to hundreds of concurrent flows traversing through the data center network switch port.

With the increasing move towards wide band operation in recent Wi-Fi networks, the frequency diversity awareness has become critical for throughput optimization. To exploit frequency diversity in Wi-Fi channels, the access point should measure the channel quality and coordinate the channel contention for all the stations. However, there is a tradeoff between achieving the frequency diversity gain and sustaining protocol efficiency because the channel estimation and coordination consume time and frequency resource that ideally should be used for data transfer. In this paper, we present Diversity-aware Wi-Fi (D-Fi), a novel PHY/MAC protocol, that efficiently exploits frequency diversity. In particular, D-Fi leverages an OFDM-based Bloom filter that synergistically integrates two operations: (i) the channel quality estimation and (ii) the contention based channel allocation. D-Fi also employs a machine learning (ML) method to resolve the false-positive ambiguity caused by the Bloom filter. Furthermore, we develop a decentralized algorithm, called Kε-greedy, based on the Multi-Armed Bandit (MAB) framework, so that it achieves sub-optimal performance by studying the gain for exploring new channel quality information. We implement the prototype of D-Fi on the USRP/GNURadio to validate the feasibility of our work. The experiments and trace-driven simulations show that D-Fi provides up to 3 x throughput improvement compared to the existing solutions.

In recent years, the data center networks commonly accommodate applications such as MapReduce and web search that inherently shows the incast communication pattern; multiple workers simultaneously transmit TCP data to a single aggregator. In this environment, the TCP performance is significantly degraded in terms of goodput and query completion time, as a result of the severe packet loss at Top of Rack (ToR) switches. The TCP senders aggressively transmit packets causing throughput collapse even though the network pipe size, i.e., bandwidth-delay product, is extremely small. In this paper, we introduce a novel end-to-end congestion control algorithm called IA-TCP that avoids the TCP incast congestion problem effectively. IA-TCP employs the rate-based algorithm at the aggregator node, which controls both the window size of workers and ACK delay. Through extensive NS-2 simulations, we validate that our algorithm is scalable in terms of the number of workers achieving enhanced goodput and zero timeouts.

In the drive-thru Internet access systems, vehicles connect to road-side access points (APs) to use IP-based services, such as web-browsing, e-mail, and file download, in addition to the customized vehicular applications. However, the mobility of vehicles and the limited coverage of APs result in the short connectivity duration and low throughput, thus leading to low availability of Internet to vehicle services. Vehicle-to-vehicle (V2V) relay support is an attractive backup solution that can address these limitations by extending the coverage. To fully realize the benefit of V2V relay support, however, the vehicle that gives the best performance must be selected as relay, yet the dynamic wireless channel conditions and the high speed of vehicles render relay selection a challenging problem. In this paper, we evaluate several relay strategies in an analytic framework to compute the resulting overall network capacity with fading channels. We then propose and devise an efficient opportunistic relay protocol that exploits multiuser diversity and effectively copes with the dynamic channel. Through both capacity analysis and Qualnet simulations, we show that the opportunistic relay scheme significantly outperforms others.

The IEEE 802.11 standard for wireless local area networks (WLANs) employs a medium access control (MAC), called distributed coordination function (DCF), which is based on carrier sense multiple access with collision avoidance (CSMA/CA). The collision avoidance mechanism utilizes the random backoff prior to each frame transmission attempt. The random nature of the backoff reduces the collision probability, but cannot completely eliminate collisions. It is known that the throughput performance of the 802.11 WLAN is significantly compromised as the number of stations increases. In this paper, we propose a novel distributed reservation-based MAC protocol, called early backoff announcement (EBA), which is backward compatible with the legacy DCF. Under EBA, a station announces its future backoff information in terms of the number of backoff slots via the MAC header of its frame being transmitted. All the stations receiving the information avoid collisions by excluding the same backoff duration when selecting their future backoff value. Through extensive simulations, EBA is found to achieve a significant increase in the throughput performance as well as a higher degree of fairness compared to the 802.11 DCF.