Question Tagging via Graph-guided Ranking

With the increasing prevalence of portable devices and the popularity of community Question Answering (cQA) sites, users can seamlessly post and answer many questions. To effectively organize the information for precise recommendation and easy searching, these platforms require users to select topics for their raised questions. However, due to the limited experience, certain users fail to select appropriate topics for their questions. Thereby, automatic question tagging becomes an urgent and vital problem for the cQA sites, yet it is non-trivial due to the following challenges. On the one hand, vast and meaningful topics are available yet not utilized in the cQA sites; how to model and tag them to relevant questions is a highly challenging problem. On the other hand, related topics in the cQA sites may be organized into a directed acyclic graph. In light of this, how to exploit relations among topics to enhance their representations is critical. To settle these challenges, we devise a graph-guided topic ranking model to tag questions in the cQA sites appropriately. In particular, we first design a topic information fusion module to learn the topic representation by jointly considering the name and description of the topic. Afterwards, regarding the special structure of topics, we propose an information propagation module to enhance the topic representation. As the comprehension of questions plays a vital role in question tagging, we design a multi-level context-modeling-based question encoder to obtain the enhanced question representation. Moreover, we introduce an interaction module to extract topic-aware question information and capture the interactive information between questions and topics. Finally, we utilize the interactive information to estimate the ranking scores for topics. Extensive experiments on three Chinese cQA datasets have demonstrated that our proposed model outperforms several state-of-the-art competitors.

Low-overhead Hardware Supervision for Securing an IoT Bluetooth-enabled Device: Monitoring Radio Frequency and Supply Voltage

Over the past decade, the number of Internet of Things (IoT) devices increased tremendously. In particular, the Internet of Medical Things (IoMT) and the Industrial Internet of Things (IIoT) expanded dramatically. Resource restrictions on IoT devices and the insufficiency of software security solutions raise the need for smart Hardware-Assisted Security (HAS) solutions. These solutions target one or more of the three C’s of IoT devices: Communication, Control, and Computation. Communication is an essential technology in the development of IoT. Bluetooth is a widely-used wireless communication protocol in small portable devices due to its low energy consumption and high transfer rates. In this work, we propose a supervisory framework to monitor and verify the operation of a Bluetooth system-on-chip (SoC) in real-time. To verify the operation of the Bluetooth SoC, we classify its transmission state in real-time to ensure a secure connection. Our overall classification accuracy is measured as 98.7%. We study both power supply current (IVDD) and RF domains to maximize the classification performance and minimize the overhead of our proposed supervisory system.

Association of Night-Time Screen-Viewing with Adolescents’ Diet, Sleep, Weight Status, and Adiposity

Night-time screen-viewing (SV) contributes to inadequate sleep and poor diet, and subsequently excess weight. Adolescents may use many devices at night, which can provide additional night-time SV. Purpose: To identify night-time SV patterns, and describe differences in diet, sleep, weight status, and adiposity between patterns in a cross-sectional and longitudinal manner. Methods: Adolescents (10–16 y) reported devices they viewed at night and completed food recalls. Accelerometry, anthropometrics, and imaging were conducted to measure sleep, weight status, and adiposity, respectively. Latent class analysis was performed to identify night-time SV clusters. Linear regression analysis was used to examine associations between clusters with diet, sleep, weight status, and adiposity. Results: Amongst 273 adolescents (12.5 ± 1.9 y, 54% female, 59% White), four clusters were identified: no SV (36%), primarily cellphone (32%), TV and portable devices (TV+PDs, 17%), and multiple PDs (17%). Most differences in sleep and adiposity were attenuated after adjustment for covariates. The TV+PDs cluster had a higher waist circumference than the no SV cluster in cross-sectional analysis. In longitudinal analysis, the primarily cellphone cluster had less change in waist circumference compared to the no SV cluster. Conclusions: Directing efforts towards reducing night-time SV, especially TV and PDs, may promote healthy development.

Conceptualizing a Quantitative Measurement Suite to Evaluate Healthcare Teams

Background Current team assessment instruments in healthcare tend to involve rater-based evaluations that are susceptible to well-known biases. Recent advances in technology include portable devices to measure team-based activities. Consequently, the possibility exists to move away from rater-based assessments of team function by identifying quantitative measures to replace them. Aim This article aims to provide potential approaches to developing quantitative measurement suites involving large amounts of data to address the challenges of assessment presented by the complex nature of teamwork. Conclusion By addressing construct, measurement, and context components, we provide a practical approach to developing a suite to capture quantitative measurements that, through incorporation of social network analysis and aggregated other values, aligns with the Team Strategies & Tools to Enhance Performance and Patient SafetyTM (TeamSTEPPSTM) dimensions for fostering teamwork.

Challenge-Driven Printing Strategies Toward High-Performance Solid-State Lithium Batteries

Solid-state lithium batteries (SSLBs) are promising candidates for replacing traditional liquid-based Li-ion batteries and revolutionizing battery systems for electric vehicles and portable devices. However, longstanding issues such as form factors,...

Development of Flexible, Conductive and Biocompatible Chitosan-Based Miniaturized Bioelectrodes for Enzymatic Glucose Biofuel Cells

The need for new clean energy sources for portable devices in biomedical, agro-food industry and environmental related sectors boosts scientists towards the development of new strategies for energy harvesting for their application in biodevices development. In this sense, enzymatic biofuel cells (BFCs) have gained much attention in the last years. This work faces the challenge of develop new generation of BFCs able to be adapted to remote and personal monitoring devices within the framework of wearable technologies. To this aim, one of the main challenges consists of the development of conductive and biocompatible electrodes, which constitute a challenge itself due to the non-conductive capabilities of most of the biocompatible supports. Additionally, bioelectrodes may achieve good mechanical properties and resilience in order to be suitable for the envisioned application, which involves exposure to deformation during long-term use. Furthermore, it is desirable that the systems developed are versatile enough to be adapted to miniaturized supports for new personal wearable devices development. In the present work, self-standing chitosan-carbon black membranes have been synthesized and modified with suitable enzymes for the assembly of an enzymatic glucose BFC. The membranes have been adapted to be integrated in miniaturized interdigitated gold electrodes as the step forward to miniaturized systems, modified with enzymes and metallic particles clusters and tested for energy harvesting from glucose solutions. The miniaturized system produces a power density of 0.64 µW/cm2 that is enhanced to 2.75 µW/cm2 in the presence of the metallic clusters, which constitute a 76% incensement. Such preliminary demonstrations highlight the good response of metals in bioelectrode configuration. However, energy harvesting real application of the developed miniaturized electrodes need still improvements but pave the way for the use of BFC as an energy source in wearable technologies due to their good mechanical, electrical and biocompatible properties.

Wireless Networking-Driven Healthcare Approaches in Combating COVID-19

Since its outbreak, the coronavirus (COVID-19) pandemic has caused havoc on people’s lives. All activities were paused due to the virus’s spread across the continents. Researchers have been working hard to find new medication treatments for the COVID-19 pandemic. The World Health Organization (WHO) recommends that safety and self-measures play a major role in preventing the virus from spreading from one person to another. Wireless technology is playing a critical role in avoiding viral propagation. This technology mainly comprises of portable devices that assist self-isolated patients in adhering to safe precautionary measures. Government officials are currently using wireless technologies to identify infected people at large gatherings. In this research, we gave an overview of wireless technologies that assisted the general public and healthcare professionals in maintaining effective healthcare services during COVID-19. We also discussed the possible challenges faced by them for effective implementation in day-to-day life. In conclusion, wireless technologies are one of the best techniques in today’s age to effectively combat the pandemic.

Handheld Spectral Sensing Devices Should Not Mislead Consumers as Far as Non-Authentic Food Is Concerned: A Case Study with Adulteration of Milk Powder

With the rising trend of consumers being offered by start-up companies portable devices and applications for checking quality of purchased products, it appears of paramount importance to assess the reliability of miniaturized sensors embedded in such devices. Here, eight sensors were assessed for food fraud applications in skimmed milk powder. The performance was evaluated with dry- and wet-blended powders mimicking adulterated materials by addition of either ammonium sulfate, semicarbazide, or cornstarch in the range 0.5–10% of profit. The quality of the spectra was assessed for an adequate identification of the outliers prior to a deep assessment of performance for both non-targeted (soft independent modelling of class analogy, SIMCA) and targeted analyses (partial least square regression with orthogonal signal correction, OPLS). Here, we show that the sensors have generally difficulties in detecting adulterants at ca. 5% supplementation, and often fail in achieving adequate specificity and detection capability. This is a concern as they may mislead future users, particularly consumers, if they are intended to be developed for handheld devices available publicly in smartphone-based applications.

Att-BiL-SL: Attention-Based Bi-LSTM and Sequential LSTM for Describing Video in the Textual Formation

With the advancement of the technological field, day by day, people from around the world are having easier access to internet abled devices, and as a result, video data is growing rapidly. The increase of portable devices such as various action cameras, mobile cameras, motion cameras, etc., can also be considered for the faster growth of video data. Data from these multiple sources need more maintenance to process for various usages according to the needs. By considering these enormous amounts of video data, it cannot be navigated fully by the end-users. Throughout recent times, many research works have been done to generate descriptions from the images or visual scene recordings to address the mentioned issue. This description generation, also known as video captioning, is more complex than single image captioning. Various advanced neural networks have been used in various studies to perform video captioning. In this paper, we propose an attention-based Bi-LSTM and sequential LSTM (Att-BiL-SL) encoder-decoder model for describing the video in textual format. The model consists of two-layer attention-based bi-LSTM and one-layer sequential LSTM for video captioning. The model also extracts the universal and native temporal features from the video frames for smooth sentence generation from optical frames. This paper includes the word embedding with a soft attention mechanism and a beam search optimization algorithm to generate qualitative results. It is found that the architecture proposed in this paper performs better than various existing state of the art models.

Searching and Rescuing Victims in Emergency: A Comprehensive Survey

The rapid progress in urban construction and informatization, especially the recent ubiquity of portable devices and intelligent infrastructures, has rapidly aggravated the concentration of pedestrians in built environments. These smart environments, which have been driven close to their limitation in daily operations, can be vulnerable during emergency situations and amplify the losses in lives and property. However, like every coin has two sides, the extra resources brought by smart equipments enlarge the optimisation space of an emergency search and rescue process and provide opportunities for new system frameworks and algorithms. In this paper, we review the current state and opportunities of the pedestrian search and rescue problem. key factors including system design, pedestrian dynamic modelling, and algorithm development are reviewed and summarised.