Status Updates Research Articles

Roman Urdu Hate Speech Detection Using Transformer-Based Model for Cyber Security Applications.

Social media applications, such as Twitter and Facebook, allow users to communicate and share their thoughts, status updates, opinions, photographs, and videos around the globe. Unfortunately, some people utilize these platforms to disseminate hate speech and abusive language. The growth of hate speech may result in hate crimes, cyber violence, and substantial harm to cyberspace, physical security, and social safety. As a result, hate speech detection is a critical issue for both cyberspace and physical society, necessitating the development of a robust application capable of detecting and combating it in real-time. Hate speech detection is a context-dependent problem that requires context-aware mechanisms for resolution. In this study, we employed a transformer-based model for Roman Urdu hate speech classification due to its ability to capture the text context. In addition, we developed the first Roman Urdu pre-trained BERT model, which we named BERT-RU. For this purpose, we exploited the capabilities of BERT by training it from scratch on the largest Roman Urdu dataset consisting of 173,714 text messages. Traditional and deep learning models were used as baseline models, including LSTM, BiLSTM, BiLSTM + Attention Layer, and CNN. We also investigated the concept of transfer learning by using pre-trained BERT embeddings in conjunction with deep learning models. The performance of each model was evaluated in terms of accuracy, precision, recall, and F-measure. The generalization of each model was evaluated on a cross-domain dataset. The experimental results revealed that the transformer-based model, when directly applied to the classification task of the Roman Urdu hate speech, outperformed traditional machine learning, deep learning models, and pre-trained transformer-based models in terms of accuracy, precision, recall, and F-measure, with scores of 96.70%, 97.25%, 96.74%, and 97.89%, respectively. In addition, the transformer-based model exhibited superior generalization on a cross-domain dataset.

Minimizing age of information in the uplink multi-user networks via dynamic bandwidth allocation

Improving the information freshness is critical for the monitoring and controlling applications in the cellular Internet of Things (IoT). In this paper, we are interested in optimizing the bandwidth allocation dynamically to improve the information freshness of the short packet based uplink status updates, which is characterized by a recently proposed metric, age of information (AoI). We first design a status update scheme with channel distribution information (CDI). By relaxing the hard bandwidth constraint and introducing a Lagrangian multiplier, we first decouple the multi-MTCD bandwidth allocation problem into a single MTCD Markov decision process (MDP). Under the MDP framework, after variable substitution, we obtain the single-MTCD status update scheme by solving a linear programming problem. Then, we adjust the Lagrangian multiplier to make the obtained scheme satisfy the relaxed bandwidth constraint. Finally, a greedy policy is built on the proposed scheme to adjust the bandwidth allocation in each slot to satisfy the hard bandwidth constraint. In the unknown environment without CDI, we further design a bandwidth allocation scheme which only maximizes the expected sum AoI drop within each time slot. Simulation results show that in terms of AoI, the proposed schemes outperform the benchmark schemes.

A cross-sectional study assessing family satisfaction in the intensive care environment in Bahrain: opportunities for improvement

Background: Assessment of patients’ family experience and levels of satisfaction have become a measure of intensive care. The aim of this study was to assess the needs of patient’s family members in an Intensive Care Unit (ICU) and to identify areas for improvement in quality care. Methods: A prospective, cross-sectional, a questionnaire survey study was carried out with 100 randomly selected family members of 77 critically ill patients cared for in surgical and medical ICUs. The family satisfaction (FS-ICU-24) questionnaire was given to family members, and the responses were used to assess their experience of ICU care. Results: The responses of patients’ relatives exposed an overall level of satisfaction of 5.04 ± 1.104 on a scale of 1-6. Satisfaction with care was scored higher than satisfaction with decision-making. Family members reported low satisfaction with communication with physicians, the ICU atmosphere, and the waiting room atmosphere. Factors such as the completeness of the information received by relatives (p=0.007) and the ease of obtaining information (p=0.007) demonstrated a significant association with family satisfaction. Suggestions received from families with the high frequency where they need lengthier visiting hours, regular updates on medical reports, better care, and communication with ICU staff. Conclusions: Although overall family satisfaction was high, some areas emerged for improvement. The family members want to increase the length of visiting hours, frequency of patient status updates, communication with physicians, involvement in decision-making, and presence during medical visits. Considering the opportunity to improve ICU care, we propose periodic assessments of family satisfaction with ICU experiences.

SYSTEMATIC LIST UPDATE OF THE CLASS GASTROPODA IN THE CENTRAL AND NORTHERN COAST OF SONORA, GULF OF CALIFORNIA

Diversity studies and research have included members of the class Gastropoda from the Gulf of California. Still, the majority have been researched in a rudimentary or secondary way, with literature not specific to the region causing discrepancies in the nomenclature of the taxa. This could affect the information on some taxa. It could also affect the malacological knowledge of the region. Therefore, an updated fauna inventory was built for 71 families of the class Gastropoda on the coast of Sonora based on latitudinally-referenced information of 3261 records of species names from the region (27° N – 31° N), obtained from official electronic databases and physical revision of malacological material from a museum. Depuration and taxonomic updates were performed when the obtained information was compared with the World Register of Marine Species platform. As a result, 26.64% of the species’ names showed status updates, resulting in 713 valid names of gastropods. The latitudinal distribution highlighted 27° N and 31° N with the greatest data and species diversity. The easy access to port cities (in the previously mentioned latitudes) makes them suitable points for scientific research since they have demonstrated a strong bias compared to intermediate latitudes.

Timeliness of Information for Computation-Intensive Status Updates in Task-Oriented Communications

Moving beyond just interconnected devices, the increasing interplay between communication and computation has fed the vision of real-time networked control systems. To obtain timely situational awareness, IoT devices continuously sample computation-intensive status updates, generate perception tasks and offload them to edge servers for processing. In this sense, the timeliness of information is considered as one major contextual attribute of status updates. In this paper, we derive the closed-form expressions of timeliness of information for computation offloading at both edge tier and fog tier, where two-stage tandem queues are exploited to abstract the transmission and computation process. Moreover, we exploit the statistical structure of Gauss-Markov process, which is widely adopted to model temporal dynamics of system states, and derive the closed-form expression for process-related timeliness of information. The obtained analytical formulas explicitly characterize the dependency among task generation, transmission and execution, which can serve as objective functions for system optimization. Based on the theoretical results, we formulate a computation offloading optimization problem at edge tier, where the timeliness of status updates is minimized among multiple devices by joint optimization of task generation, bandwidth allocation, and computation resource allocation. An iterative solution procedure is proposed to solve the formulated problem. Numerical results reveal the intertwined relationship among transmission and computation stages, and verify the necessity of factoring in the task generation process for computation offloading strategy design.

Joint Optimization of Freshness and Fidelity for Status Updates in a Periodical Decision System

Development of applications in real-time service promotes more stringent freshness and fidelity requirements for the status update and decision systems. However, the freshness and fidelity of status updates are mutually restrictive, as fresh information requires timely transmission whereas high quantization precision incurs long transmission time. This work improves trade-off between the freshness and fidelity of status updates at periodical decision epochs by selecting appropriate transmission time and quantization precision. Specifically, the age of information (AoI) metric is used to assess data freshness, while distortion that consists of both static and dynamic parts is used to characterize the fidelity of status updates. We first formulate a joint AoI and distortion optimization problem. Then, the optimal transmission time and quantization precision are theoretically analyzed. We further minimize the sum of AoI and distortion by using power allocation strategy which selects a proper budget SNR of the wireless channel. Numerical results show that by using the optimized transmission time, quantization precision, associated with transmit power allocation, the overall freshness and fidelity of status updates can be improved remarkably.

Resource Allocation for OFDM-Based Status Update Systems: A Timeliness Perspective

Increasingly more time-sensitive applications emerge in Internet of things (IoT), requiring fresh status updates. Efficient resource allocation is critical for improving the timeliness of information. This paper investigates how to allocate bandwidth and configure the transmission in an orthogonal frequency division multiplexing (OFDM)-based IoT systems for timely status updating. Specifically, a general resource allocation problem is formulated using age of information (AoI) as the timeliness metric. We first consider a static channel case where we propose a fixed time slot length policy, a fixed blocklength policy, an iterative optimization algorithm, and an approximate optimal policy to address the optimal resource allocation and transmission configuration. Then, we extend the study to quasi-static fading channel cases. In particular, sticking to the transmission rate and spectral efficiency, the four policies designated for static channel case are tailored for the quasi-static fading channel case. We show that the iterative algorithms can improve the timeliness of the system significantly. Besides, the approximate optimal policy achieves an AoI performance competitive with the iterative algorithm, while reduces the computation complexity in both channel cases.

Matching while learning: Wireless scheduling for age of information optimization at the edge

In this paper, we investigate the minimization of age of information (AoI), a metric that measures the information freshness, at the network edge with unreliable wireless communications. Particularly, we consider a set of users transmitting status updates, which are collected by the user randomly over time, to an edge server through unreliable orthogonal channels. It begs a natural question: with random status update arrivals and obscure channel conditions, can we devise an intelligent scheduling policy that matches the users and channels to stabilize the queues of all users while minimizing the average AoI? To give an adequate answer, we define a bipartite graph and formulate a dynamic edge activation problem with stability constraints. Then, we propose an online matching while learning algorithm (MatL) and discuss its implementation for wireless scheduling. Finally, simulation results demonstrate that the MatL is reliable to learn the channel states and manage the users' buffers for fresher information at the edge.

Improving Information Freshness via Backbone-Assisted Cooperative Access Points

Information freshness, characterized by Age of Information (AoI), is important for sensor applications involving timely status updates. In many cases, the wireless signals from one sensor can be received by multiple access points (APs). This article investigates the average AoI for cooperative APs (Co-APs), in which they can share information through a wired backbone network. We first study a basic backbone-assisted Co-AP system where APs share only decoded packets. Experimental results on software-defined radios (SDRs) indicate that Co-AP significantly improves the average AoI performance over a single-AP system. Next, we investigate an improved Co-AP system, called Soft-Co-AP. In addition to sharing decoded packets, Soft-Co-AP shares and collects soft information of packets that the APs fail to decode for further joint decoding. A critical issue in Soft-Co-AP is determining the number of quantization bits that represent the soft information (each soft bit) shared over the backbone. While more quantization bits per soft bit improves the joint decoding performance, it leads to higher backbone delay. We experimentally study the average AoI of Soft-Co-AP by evaluating the tradeoff between the backbone delay and the number of quantization bits. SDR experiments show that when the number of sensors is large, Soft-Co-AP further reduces the average AoI by 12% compared with Co-AP. Interestingly, good average AoI performance is usually achieved when the number of quantization bits per soft bit is neither too large nor too small.

Age of Processed Information Minimization Over Fading Multiple Access Channels

In many real-time applications, timely accessibility to actionable insights is an important requirement. We consider users equipped with sensors and actuators, where the sensors generate updates which need to be processed for driving the actuators. The processing task can be carried out locally or at an edge server located at a common base station (BS). The users access the BS via a fading multiple access channel (MAC) using a non-orthogonal multiple access technique. To measure the timeliness of processed information, we adopt a metric called age of processed information (AoPI), defined as the time elapsed since the generation of the last successfully processed packet available to the user. In this setting, we formulate an average AoPI minimization problem, when users are subjected to average power constraints. The BS has to decide when the users should generate status updates and whether to process them locally or at the edge server. We cast the problem as a constrained Markov decision process (CMDP) and obtain its solution via Lagrangian relaxation. We then obtain a simpler policy via Lyapunov optimization and bound its performance. Via numerical simulations, we finally illustrate properties of the CMDP solution and study behavior of average AoPIs under different policies when problem parameters are varied.

Distribution of the Age of Gossip in Networks.

We study a general setting of gossip networks in which a source node forwards its measurements (in the form of status updates) about some observed physical process to a set of monitoring nodes according to independent Poisson processes. Furthermore, each monitoring node sends status updates about its information status (about the process observed by the source) to the other monitoring nodes according to independent Poisson processes. We quantify the freshness of the information available at each monitoring node in terms of Age of Information (AoI). While this setting has been analyzed in a handful of prior works, the focus has been on characterizing the average (i.e., marginal first moment) of each age process. In contrast, we aim to develop methods that allow the characterization of higher-order marginal or joint moments of the age processes in this setting. In particular, we first use the stochastic hybrid system (SHS) framework to develop methods that allow the characterization of the stationary marginal and joint moment generating functions (MGFs) of age processes in the network. These methods are then applied to derive the stationary marginal and joint MGFs in three different topologies of gossip networks, with which we derive closed-form expressions for marginal or joint high-order statistics of age processes, such as the variance of each age process and the correlation coefficients between all possible pairwise combinations of age processes. Our analytical results demonstrate the importance of incorporating the higher-order moments of age processes in the implementation and optimization of age-aware gossip networks rather than just relying on their average values.

Short Packet-Based Status Updates in Cognitive Internet of Things: Analysis and Optimization

In the cellular Internet of Things (IoT), some machine type communication devices (MTCDs) are used to sample and send status information of monitored targets to help base station (BS) perform monitoring or controlling tasks. However, due to limited spectrum resources, the MTCDs often cannot occupy separate spectrum resources. This paper studies a cognitive IoT where the MTCDs exploit the spectrum resource vacated by cellular users to send status packets to the BS. The timeliness of status updates is characterized by the recently proposed metric, age of information (AoI). In the IoT, the packets sent by the MTCDs are often short, which means it is necessary to analyze the status updates under short packet communication theory (SPC). In this case, we adopt the automatic repeat request protocol to retransmit the erroneous packets, and the preemption policy is exploited to improve the status update performance. We first carefully analyze the evolution of AoI in the cognitive IoT, and adopt a recursive method to obtain the closed-form expression of the average peak AoI. Then, we analyze age-energy tradeoff, and propose a scheme of jointly optimizing transmit power, packet length, and spectrum sharing to reduce the average peak AoI of the network. The simulation results verify the correctness of the theoretical analysis, and show that the proposed scheme outperforms the benchmark schemes.

A Digital Product Passport for Critical Raw Materials Reuse and Recycling

The reuse and recycling of critical raw materials is limited, as waste electrical and electronic recycling focuses on base and precious metals, and device component reuse is in its infancy. To help to address this issue this paper provides the conceptual design of a Digital Product Passport based circular supply management system. To enable the recovery of critical raw materials at component and material levels for reuse and recycling. The works include an assessment of existing critical raw materials information management and an information needs identification survey, with 10 manufacturers, producer responsibility organisations, collectors and recyclers. The needs were used to generate 14 key product information management processes and exchanges that when implemented form a Digital Product Passport based circular supply management system. Information managed via a physical-digital linkage through individual product tags includes product registrations, materials declarations, life cycle status updates, the sorting of products at collection points based on critical raw material contents, and flagging of products for critical raw materials component extraction. A dataspace-based IT systems architecture is proposed for the implementation of the supply management system taking into account global and European information standards. Finally, key challenges to implement such an IT architecture are discussed.

Status Prediction and Data Aggregation for AoI-Oriented Short-Packet Transmission in Industrial IoT

Age of information (AoI) is an effective performance metric for time-critical industrial Internet of things (IIoT) applications. We investigate status prediction and data aggregation with prediction error awareness, to enhance the AoI performance for short-packet transmission (SPT) in time-critical IIoT. A predict-compare (PredComp) transmission scheme is proposed, where proactive transmission termination is employed in case of prediction error, by comparing the predicted and real updates at source. It is proved to achieve a significant average AoI performance gain over the case without prediction, even under high prediction error probability. In addition, a predict-aggregate-compare (PredAggComp) transmission scheme is proposed, where two status updates are predicted with different prediction horizons and aggregated by utilizing their time correlation. That allows a good tradeoff between the prediction accuracy and the transmission error probability. A closed-form threshold that the PredAggComp scheme outperforms the PredComp scheme is derived. Moreover, prediction horizon adaptation is conducted to minimize the average AoI of the proposed transmission schemes. Simulation results verify the analytical results and show the superiority of the proposed PredComp and PredAggComp schemes, with an average AoI reduction of up to 64% over the case without prediction.

Illocutionary Acts Of The 100 Covid-19 Health Protocol Campaign Posters On Facebook Status Updates (A Netnographic Study)

This study aims to identify the types of illocutionary speech acts contained in 100 poster designs containing appeals for the prevention of Covid-19 and the application of health protocols posted on social media Facebook. This study uses a qualitative descriptive method by applying the data collection techniques of Miles, Huberman, and Saldana, namely data condensation, data display, and conclusion. This study uses Searle's theory of illocutionary speech acts to analyze the collected data. The theory includes the types of illocutionary speech acts, namely directive, assertive, expressive, commissive, and declarative. The results of this study indicate that of the 100 poster designs analyzed, there were 64 posters containing directive speech acts, 27 posters containing assertive speech acts, 8 posters containing expressive speech acts, and 1 poster containing commissive speech acts. The large number of posters containing declarative speech acts indicates that the poster is aimed at hearers or audiences to do or carry out something related to the prevention of Covid-19 and the application of strict health protocols. This is in line with the purpose of the poster, which is persuasive, which means it can influence people who see or read it.

A corpus-based pragmatic analysis of Jordanians Facebook status updates during COVID-19

This study investigates the communicative functions of status updates on Facebook during COVID-19. For this purpose, a corpus of 500 status updates was collected from 100 Facebook users for 90 consecutive days. Subsequently, the data were characterized into five speech acts drawing heavily on Searle's speech act framework, prominent among which are expressives and assertives. Data analysis revealed that status updates could be considered a substantial medium for understanding intended human communication. Various types of speech acts were used with different frequencies and percentages, although people were inclined mostly to use expressive speech acts. The sociocultural variations in conjunction with forming and constructing identities were reflected in the status updates manifested in the current situation of the pandemic, which makes Jordanians appear more humorous than before. This research is significant because studying aspects of a language helps in understanding the hidden motivations, beliefs, ideas, attitudes and identities along with the social, cultural, and political factors, which in turn provides logical solutions for certain problems.

Age of Information for the Cellular Internet of Things: Challenges, Key Techniques, and Future Trends

With the rapid development of cellular Internet of Things (CIoT), more and more real-time applications appear in our lives, which require timely status updates. To exactly characterize the information freshness, the concept of age of information (AoI) has received considerable interest. In this article, we start by introducing the characteristics and requirements of status updates in CIoT, and some AoI related functions are listed. However, it is nontrivial to realize real-time status updates over wireless fading channels, and we analyze the challenges for meeting AoI requirements. Next, we provide a survey on a variety of status update optimization methods and techniques. In particular, we introduce the prediction technique into the status update systems and propose a novel prediction-based status update scheme. Some numerical simulations are provided to show the performance gain obtained by the proposed scheme. Finally, we discuss some promising directions for future research in this emerging area of study.

Age of Information With Hybrid-ARQ: A Unified Explicit Result

Delivering timely status updates in a timeliness-critical communication system is of paramount importance to assist accurate and efficient decision making. Therefore, the topic of analyzing Age of Information (AoI) has aroused new research interest. This paper contributes to new results in this area by systematically analyzing the AoI of two types of Hybrid Automatic Repeat reQuest (HARQ) techniques that have been newly standardized in the Release-16 5G New Radio (NR) specifications, namely reactive HARQ and proactive HARQ. Under a code-based status update system with non-trivial coding delay, transmission delay, propagation delay, decoding delay, and feedback delay, we derive unified closed-form average AoI and average Peak AoI expressions for reactive HARQ and proactive HARQ, respectively. Based on the obtained explicit expressions, we formulate an AoI minimization problem to investigate the age-optimal codeblock assignment strategy in the finite block-length (FBL) regime. Through case studies and analytical results, we provide comparative insights between reactive HARQ and proactive HARQ from a perspective of freshness of information. The numerical results and optimization solutions show that proactive HARQ draws its strength from both age performance and system robustness, thus enabling the potential to provide new system advancement of a freshness-critical status update system.

Joint Offloading and Trajectory Optimization for Complex Status Updates in UAV-Assisted Internet of Things

Unmanned aerial vehicles (UAVs) can utilize multiaccess edge computing (MEC) to help Internet of Things (IoT) devices complete the complex status update by efficient offloading and proper trajectory design. However, considering that IoT devices usually communicate with UAVs in the finite blocklength regime, the uplink transmission cannot be error free. Due to the nonzero packet error probability (PEP), it is difficult to evaluate the instantaneous system performance as in the case with small blocklength. Moreover, the PEP is simultaneously coupled with the offloading parameters and trajectories of UAVs, which makes the performance optimization even more challenging. To this end, we first derive the analytical expressions of the average peak Age of Information (AoI), the average energy consumption of IoT devices and the average energy consumption of UAVs. Then, we formulate a joint optimization problem aiming to minimize the weighted sum of the three performance metrics by jointly optimizing the offloading parameters and the UAV trajectories. By dividing the original problem into multiple subproblems, an alternating optimization-based algorithm is proposed to solve it suboptimally. Simulation results validate the effectiveness of our proposed algorithm and reveal that by properly setting the transmit power and computing capacity of IoT devices, the desired tradeoff among the three performance metrics can be achieved and thus the system performance can be improved effectively.

Age-Optimal Network Coding HARQ Scheme for Satellite-Based Internet of Things

Satellite-based Internet of Things (S-IoT) is viewed as an efficient solution to provide timely status updates to the terrestrial user equipment (UE), due to its ubiquitous coverage and broadband access capability inherited from high throughput satellite (HTS). However, the conventional hybrid automatic repeat request (HARQ) cannot guarantee the freshness of status update transmission, because the reliable transmission needs the retransmission of the lost packets, which deteriorates the freshness due to the nontrivial propagation delay and high bit error rate (BER) of the satellite–territory link (STL). In this article, we propose an age-optimal network coding HARQ (NC HARQ) scheme with the metric of information timeliness, i.e., Age of Information (AoI) to realize timely status updates in S-IoT. First, we model the STL as a shadowed Rician (SR) fading channel and derive the closed-form expressions of BER. Then, we propose a fixed interval NC inserted HARQ (f-NC HARQ) scheme, where the NC packets are inserted in the information packets with fixed interval to accelerate the recovery of lost information packets and derive the expressions of Peak AoI (PAoI) and average end-to-end delay. Furthermore, we propose an adaptive NC inserted HARQ (A-NC HARQ) scheme for the drastic variations in the SR fading channel, where the transmission of the status update is modeled as a partially observable Markov decision process (POMDP) problem and solved by a low complexity improved fast informed bound (iFIB) algorithm. Simulation results validate the accuracy of our theoretical derivations and show that the A-NC HARQ scheme can achieve the lowest PAoI and average end-to-end delay.