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This paper presents ATEM, a novel framework for studying topic evolution in scientific archives. ATEM is based on dynamic topic modeling and dynamic graph embedding techniques that explore the dynamics of content and citations of documents within a scientific corpus. ATEM explores a new notion of contextual emergence for the discovery of emerging interdisciplinary research topics based on the dynamics of citation links in topic clusters. Our experiments show that ATEM can efficiently detect emerging cross-disciplinary topics within the DBLP archive of over five million computer science articles.

The recent explosion in work on neural topic modeling has been criticized for optimizing automated topic evaluation metrics at the expense of actual meaningful topic identification. But human annotation remains expensive and time-consuming. We propose LLM-based methods inspired by standard human topic evaluations, in a family of metrics called Contextualized Topic Coherence (CTC). We evaluate both a fully automated version as well as a semi-automated CTC that allows human-centered evaluation of coherence while maintaining the efficiency of automated methods. We evaluate CTC relative to five other metrics on six topic models and find that it outperforms automated topic coherence methods, works well on short documents, and is not susceptible to meaningless but high-scoring topics.

This paper presents an algorithmic family of dynamic topic models called Aligned Neural Topic Models (ANTM), which combine novel data mining algorithms to provide a modular framework for discovering evolving topics. ANTM maintains the temporal continuity of evolving topics by extracting time-aware features from documents using advanced pre-trained Large Language Models (LLMs) and employing an overlapping sliding window algorithm for sequential document clustering. This overlapping sliding window algorithm identifies a different number of topics within each time frame and aligns semantically similar document clusters across time periods. This process captures emerging and fading trends across different periods and allows for a more interpretable representation of evolving topics. Experiments on four distinct datasets show that ANTM outperforms probabilistic dynamic topic models in terms of topic coherence and diversity metrics. Moreover, it improves the scalability and flexibility of dynamic topic models by being accessible and adaptable to different types of algorithms. Additionally, a Python package is developed for researchers and scientists who wish to study the trends and evolving patterns of topics in large-scale textual data.

As the number of Human-Centered Internet of Things (HCIoT) applications increases, the self-adaptation of its services and devices is becoming a fundamental requirement for addressing the uncertainties of the environment in decision-making processes. Self-adaptation of HCIoT aims to manage run-time changes in a dynamic environment and to adjust the functionality of IoT objects in order to achieve desired goals during execution. SMASH is a semantic-enabled multi-agent system for self-adaptation of HCIoT that autonomously adapts IoT objects to uncertainties of their environment. SMASH addresses the self-adaptation of IoT applications only according to the human values of users, while the behavior of users is not addressed. This article presents Q-SMASH: a multi-agent reinforcement learning-based approach for self-adaptation of IoT objects in human-centered environments. Q-SMASH aims to learn the behaviors of users along with respecting human values. The learning ability of Q-SMASH allows it to adapt itself to the behavioral change of users and make more accurate decisions in different states and situations., Comment: Submitted to wi-iat2021. arXiv admin note: text overlap with arXiv:2105.14915

Nowadays, IoT devices have an enlarging scope of activities spanning from sensing, computing to acting and even more, learning, reasoning and planning. As the number of IoT applications increases, these objects are becoming more and more ubiquitous. Therefore, they need to adapt their functionality in response to the uncertainties of their environment to achieve their goals. In Human-centered IoT, objects and devices have direct interactions with human beings and have access to online contextual information. Self-adaptation of such applications is a crucial subject that needs to be addressed in a way that respects human goals and human values. Hence, IoT applications must be equipped with self-adaptation techniques to manage their run-time uncertainties locally or in cooperation with each other. This paper presents SMASH: a multi-agent approach for self-adaptation of IoT applications in human-centered environments. In this paper, we have considered the Smart Home as the case study of smart environments. SMASH agents are provided with a 4-layer architecture based on the BDI agent model that integrates human values with goal-reasoning, planning, and acting. It also takes advantage of a semantic-enabled platform called Home'In to address interoperability issues among non-identical agents and devices with heterogeneous protocols and data formats. This approach is compared with the literature and is validated by developing a scenario as the proof of concept. The timely responses of SMASH agents show the feasibility of the proposed approach in human-centered environments., Comment: Submitted to PAAMS 2021

Edge Computing in 5G and Beyond is a promising solution for ultra-low latency applications (e.g. Autonomous Vehicle, Augmented Reality, and Remote Surgery), which have an extraordinarily low tolerance for the delay and require fast data processing for a very high volume of data. The requirements of delay-sensitive applications (e.g. Low latency, proximity, and Location/Context-awareness) cannot be satisfied by Cloud Computing due to the high latency between User Equipment and Cloud. Nevertheless, Edge Computing in 5G and beyond can promise an ultra-high-speed caused by placing computation capabilities closer to endpoint devices, whereas 5G encourages the speed rate that is 200 times faster than 4G LTE-Advanced. This paper deeply investigates Edge Computing in 5G and characterizes it based on the requirements of ultra-low latency applications. As a contribution, we propose a hybrid architecture that takes advantage of novel and sustainable technologies (e.g. D2D communication, Massive MIMO, SDN, and NFV) and has major features such as scalability, reliability and ultra-low latency support. The proposed architecture is evaluated based on an agent-based simulation that demonstrates it can satisfy requirements and has the ability to respond to high volume demands with low latency., Comment: Submitted to Special Issue on Smart Edge Computing and IoT of IEEE Transaction on Computers

This research proposal aims to use cognitive methods to analyze the quality of roads based on the new proposed technology called Cognitive Internet of Vehicles (CIoV). By using Big Data corresponding to the collected data of autonomous vehicles, we can apply cognitive analytics to a huge amount of transportation data. This process can help us to create valuable information such as road quality from an immense volume of meaningless data. In this proposal, we are going to focus on the quality of roads for various business and commercial purposes. The proposed system can be used as an additional service of autonomous car companies or as a mobile application for ordinary usages. As a result, this system can reduce the usage of resources such as energy consumption of autonomous vehicles. Moreover, this technology benefits the next-generation of self-driving applications to improve their QoS.

This paper introduces the deployment of a group of Wireless Sensor and Actuator Network (WSAN) for Internet of Thing (IoT) systems in rural regions deployed by a drone dropping sensors and actuators at a certain position as a mesh of a hexagonal form. Nodes are heterogeneous in hardware and functionality thus not all nodes are able to transfer data directly to the base station. Primitive ones are only capable of collecting local data. However, ones that are more sophisticated are equipped with long-range radio telemetry and more computational power. Power optimization is one of the crucial factors in designing WSANs. Total power consumption must be minimized, as sensors are self-managed. It is not feasible to collect sensors on time bases and recharge the batteries. Therefore, energy consumption optimization and harvesting green energy are other factors that are considered. In this regard, protocols are designed in a way to support such requirements. The preprocessed data are first collected and combined by the leaders at each hexagonal cell. Then, the information packets are sent to the head clusters. Consequently, head clusters reprocess the received information and depict a better global view of the zone, using a variety of the received information. Finally, the processed information is sent to the nearest base station or a mobile drone., Comment: not agreed anymore

The Internet of Things (IoT) is a crucial component of Industry 4.0. Due to growing demands of customers, the current IoT architecture will not be reliable and responsive for next generation IoT applications and upcoming services. In this paper, the next generation IoT architecture based on new technologies is proposed in which the requirements of future applications, services, and generated data are addressed. Particularly, this architecture consists of Nano-chip, millimeter Wave (mmWave), Heterogeneous Networks (HetNet), device-todevice (D2D) communication, 5G-IoT, Machine-Type Communication (MTC), Wireless Network Function virtualization (WNFV), Wireless Software Defined Networks (WSDN), Advanced Spectrum Sharing and Interference Management (Advanced SSIM), Mobile Edge Computing (MEC), Mobile Cloud Computing (MCC), Data Analytics and Big Data. This combination of technologies is able to satisfy requirements of new applications. The proposed novel architecture is modular, efficient, agile, scalable, simple, and it is able to satisfy the high amount of data and application demands., Comment: 7 pages, 1 figure, 1 table, submitted to IEEE Global Communications Conference 2018