bluuebunny/crossref_metadata_2025 huggingface dataset:
Secure Distributed Mobile Volunteer Computing with Android
Authors: Bibi Iram , Akhunzada Adnan , Malik Jahanzaib , Khan Muhammad Khurram , Dawood Muhammad
Published: September 2021

Volunteer Computing provision of seamless connectivity that enables convenient and rapid deployment of greener and cheaper computing infrastructure is extremely promising to complement next-generation distributed computing systems. Undoubtedly, without tactile Internet and secure VC ecosystems, harnessing its full potentials and making it an alternative viable and reliable computing infrastructure is next to impossible. Android-enabled smart devices, applications, and services are inevitable for Volunteer computing. Contrarily, the progressive developments of sophisticated Android malware may reduce its exponential growth. Besides, Android malwares are considered the most potential and persistent cyber threat to mobile VC systems. To secure Android-based mobile volunteer computing, the authors proposed MulDroid, an efficient and self-learning autonomous hybrid (Long-Short-Term Memory, Convolutional Neural Network, Deep Neural Network) multi-vector Android malware threat detection framework. The proposed mechanism is highly scalable with well-coordinated infrastructure and self-optimizing capabilities to proficiently tackle fast-growing dynamic variants of sophisticated malware threats and attacks with 99.01% detection accuracy. For a comprehensive evaluation, the authors employed current state-of-the-art malware datasets (Android Malware Dataset, Androzoo) with standard performance evaluation metrics. Moreover, MulDroid is compared with our constructed contemporary hybrid DL-driven architectures and benchmark algorithms. Our proposed mechanism outperforms in terms of detection accuracy with a trivial tradeoff speed efficiency. Additionally, a 10-fold cross-validation is performed to explicitly show unbiased results.
Malware Classification Based on Multilayer Perception and Word2Vec for IoT Security
Authors: Qiao Yanchen , Zhang Weizhe , Du Xiaojiang , Guizani Mohsen
Published: September 2021

With the construction of smart cities, the number of Internet of Things (IoT) devices is growing rapidly, leading to an explosive growth of malware designed for IoT devices. These malware pose a serious threat to the security of IoT devices. The traditional malware classification methods mainly rely on feature engineering. To improve accuracy, a large number of different types of features will be extracted from malware files in these methods. That brings a high complexity to the classification. To solve these issues, a malware classification method based on Word2Vec and
Multilayer Perception (MLP)
is proposed in this article. First, for one malware sample, Word2Vec is used to calculate a word vector for all bytes of the binary file and all instructions in the assembly file. Second, we combine these vectors into a 256x256x2-dimensional matrix. Finally, we designed a deep learning network structure based on MLP to train the model. Then the model is used to classify the testing samples. The experimental results prove that the method has a high accuracy of 99.54%.
Virtual Reality Aided High-Quality 3D Reconstruction by Remote Drones
Authors: Zhang Di , Xu Feng , Pun Chi-Man , Yang Yang , Lan Rushi , Wang Liejun , Li Yujie , Gao Hao
Published: September 2021

Artificial intelligence including deep learning and 3D reconstruction methods is changing the daily life of people. Now, an unmanned aerial vehicle that can move freely in the air and avoid harsh ground conditions has been commonly adopted as a suitable tool for 3D reconstruction. The traditional 3D reconstruction mission based on drones usually consists of two steps: image collection and offline post-processing. But there are two problems: one is the uncertainty of whether all parts of the target object are covered, and another is the tedious post-processing time. Inspired by modern deep learning methods, we build a telexistence drone system with an onboard deep learning computation module and a wireless data transmission module that perform incremental real-time dense reconstruction of urban cities by itself. Two technical contributions are proposed to solve the preceding issues. First, based on the popular depth fusion surface reconstruction framework, we combine it with a visual-inertial odometry estimator that integrates the inertial measurement unit and allows for robust camera tracking as well as high-accuracy online 3D scan. Second, the capability of real-time 3D reconstruction enables a new rendering technique that can visualize the reconstructed geometry of the target as navigation guidance in the HMD. Therefore, it turns the traditional path-planning-based modeling process into an interactive one, leading to a higher level of scan completeness. The experiments in the simulation system and our real prototype demonstrate an improved quality of the 3D model using our artificial intelligence leveraged drone system.
Incremental Group-Level Popularity Prediction in Online Social Networks
Authors: Wang Jingjing , Jiang Wenjun , Li Kenli , Wang Guojun , Li Keqin
Published: September 2021

Predicting the popularity of web contents in online social networks is essential for many applications. However, existing works are usually under non-incremental settings. In other words, they have to rebuild models from scratch when new data occurs, which are inefficient in big data environments. It leads to an urgent need for incremental prediction, which can update previous results with new data and conduct prediction incrementally. Moreover, the promising direction of group-level popularity prediction has not been well treated, which explores fine-grained information while keeping a low cost. To this end, we identify the problem of incremental group-level popularity prediction, and propose a novel model
IGPP
to address it. We first predict the group-level popularity incrementally by exploiting the incremental CANDECOMP/PARAFCAC (CP) tensor decomposition algorithm. Then, to reduce the cumulative error by incremental prediction, we propose three strategies to restart the CP decomposition. To the best of our knowledge, this is the first work that identifies and solves the problem of incremental group-level popularity prediction. Extensive experimental results show significant improvements of the
IGPP
method over other works both in the prediction accuracy and the efficiency.
Adaptive Management of Volatile Edge Systems at Runtime With Satisfiability
Authors: Avasalcai Cosmin , Tsigkanos Christos , Dustdar Schahram
Published: September 2021

Edge computing offers the possibility of deploying applications at the edge of the network. To take advantage of available devices’ distributed resources, applications often are structured as microservices, often having stringent requirements of low latency and high availability. However, a decentralized edge system that the application may be intended for is characterized by high volatility, due to devices making up the system being unreliable or leaving the network unexpectedly. This makes application deployment and assurance that it will continue to operate under volatility challenging. We propose an adaptive framework capable of deploying and efficiently maintaining a microservice-based application at runtime, by tackling two intertwined problems: (i) finding a microservice placement across device hosts and (ii) deriving invocation paths that serve it. Our objective is to maintain correct functionality by satisfying given requirements in terms of end-to-end latency and availability, in a volatile edge environment. We evaluate our solution quantitatively by considering performance and failure recovery.
A Language-independent Network to Analyze the Impact of COVID-19 on the World via Sentiment Analysis
Authors: Yadav Ashima , Vishwakarma Dinesh Kumar
Published: September 2021

Towards the end of 2019, Wuhan experienced an outbreak of novel coronavirus, which soon spread worldwide, resulting in a deadly pandemic that infected millions of people around the globe. The public health agencies followed many strategies to counter the fatal virus. However, the virus severely affected the lives of the people. In this paper, we study the sentiments of people from the top five worst affected countries by the virus, namely the USA, Brazil, India, Russia, and South Africa. We propose a deep language-independent
Multilevel Attention-based Conv-BiGRU network (MACBiG-Net)
, which includes embedding layer, word-level encoded attention, and sentence-level encoded attention mechanisms to extract the positive, negative, and neutral sentiments. The network captures the subtle cues in a document by focusing on the local characteristics of text along with the past and future context information for the sentiment classification. We further develop a COVID-19 Sentiment Dataset by crawling the tweets from Twitter and applying topic modeling to extract the hidden thematic structure of the document. The classification results demonstrate that the proposed model achieves an accuracy of 85%, which is higher than other well-known algorithms for sentiment classification. The findings show that the topics which evoked positive sentiments were related to frontline workers, entertainment, motivation, and spending quality time with family. The negative sentiments were related to socio-economic factors like racial injustice, unemployment rates, fake news, and deaths. Finally, this study provides feedback to the government and health professionals to handle future outbreaks and highlight future research directions for scientists and researchers.
Pushing the Limits of Long Range Wireless Sensing with LoRa
Authors: Xie Binbin , Yin Yuqing , Xiong Jie
Published: September 2021

Wireless sensing is an exciting new research area which enables a large variety of applications ranging from coarse-grained daily activity recognition to fine-grained vital sign monitoring. While promising in many aspects, one critical issue is the limited sensing range because weak reflection signals are used for sensing. Recently, LoRa signals are exploited for wireless sensing, moving a big step towards long-range sensing. Although promising, there is still a huge room for improvement. In this work, we qualitatively characterize the relationship between target movements and target-induced signal variations, and propose signal processing methods to enlarge the induced signal variation to achieve a longer sensing range. Experiment results show that the proposed system (1) pushes the contact-free sensing range of human walking from the state-of-the-art 50 m to 120 m; (2) achieves a sensing range of 75 m for fine-grained respiration sensing; and (3) demonstrates human respiration sensing even through seven concrete walls.
SwingNet
Authors: Jia Hong , Hu Jiawei , Hu Wen
Published: September 2021

Sports analytics in the wild (i.e., ubiquitously) is a thriving industry. Swing tracking is a key feature in sports analytics. Therefore, a centimeter-level tracking resolution solution is required. Recent research has explored deep neural networks for sensor fusion to produce consistent swing-tracking performance. This is achieved by combining the advantages of two sensor modalities (IMUs and depth sensors) for golf swing tracking. Here, the IMUs are not affected by occlusion and can support high sampling rates. Meanwhile, depth sensors produce significantly more accurate motion measurements than those produced by IMUs. Nevertheless, this method can be further improved in terms of accuracy and lacking information for different domains (e.g., subjects, sports, and devices). Unfortunately, designing a deep neural network with good performance is time consuming and labor intensive, which is challenging when a network model is deployed to be used in new settings. To this end, we propose a network based on Neural Architecture Search (NAS), called SwingNet, which is a regression-based automatic generated deep neural network via stochastic neural network search. The proposed network aims to learn the swing tracking feature for better prediction automatically. Furthermore, SwingNet features a domain discriminator by using unsupervised learning and adversarial learning to ensure that it can be adaptive to unobserved domains. We implemented SwingNet prototypes with a smart wristband (IMU) and smartphone (depth sensor), which are ubiquitously available. They enable accurate sports analytics (e.g., coaching, tracking, analysis and assessment) in the wild.

Our comprehensive experiment shows that SwingNet achieves less than 10 cm errors of swing tracking with a subject-independent model covering multiple sports (e.g., golf and tennis) and depth sensor hardware, which outperforms state-of-the-art approaches.
Smartphone-Based Tapping Frequency as a Surrogate for Perceived Fatigue
Authors: Barrios Liliana , Oldrati Pietro , Hilty Marc , Lindlbauer David , Holz Christian , Lutterotti Andreas
Published: September 2021

Fatigue is a common symptom in various diseases, including multiple sclerosis (MS). The current standard method to assess fatigue is through questionnaires, which has several shortcomings; questionnaires are subjective, prone to recall bias, and potentially confounded by other symptoms like stress and depression. Thus, there is an unmet medical need to develop objective and reliable methods to evaluate fatigue. Our study seeks to develop an objective and ubiquitous monitoring tool for assessing fatigue. Leveraging a smartphone-based rapid tapping task, we conducted a two-week in-the-wild study with 35 MS patients. We explore the association between tapping derived metrics and perceived fatigue assessed with two standard clinical scales: fatigue severity scale (FSS) and fatigue scale for motor and cognitive function (FSMC). Our novel smartphone-based fatigue metric, mean tapping frequency, objectively ranks perceived fatigue with a mean AUCROC = .76, CI = [.71, .81] according to the FSMC, and a mean AUCROC = .81, CI = [.76, .86] according to the FSS. These results demonstrate that our approach is feasible and valid in uncontrolled environments. In this work, we provide a promising tool for objective fatigue monitoring to be used in clinical trials and routine medical care.
SenseCollect
Authors: Chen Wenqiang , Lin Shupei , Thompson Elizabeth , Stankovic John
Published: September 2021

On-body sensor-based human activity recognition (HAR) lags behind other fields because it lacks large-scale, labeled datasets; this shortfall impedes progress in developing robust and generalized predictive models. To facilitate researchers in collecting more extensive datasets quickly and efficiently we developed SenseCollect. We did a survey and interviewed student researchers in this area to identify what barriers are making it difficult to collect on-body sensor-based HAR data from human subjects. Every interviewee identified data collection as the hardest part of their research, stating it was laborious, consuming and error-prone. To improve HAR data resources we need to address that barrier, but we need a better understanding of the complicating factors to overcome it. To that end we conducted a series of control variable experiments that tested several protocols to ascertain their impact on data collection. SenseCollect studied 240+ human subjects in total and presented the findings to develop a data collection guideline. We also implemented a system to collect data, created the two largest on-body sensor-based human activity datasets, and made them publicly available.
Overthere
Authors: Seo Hyunggoog , Kim Jaedong , Seo Kwanggyoon , Kim Bumki , Noh Junyong
Published: September 2021

An absolute mid-air pointing technique requires a preprocess called registration that makes the system remember the 3D positions and types of objects in advance. Previous studies have simply assumed that the information is already available because it requires a cumbersome process performed by an expert in a carefully calibrated environment. We introduce Overthere, which allows the user to intuitively register the objects in a smart environment by pointing to each target object a few times. To ensure accurate and coherent pointing gestures made by the user regardless of individual differences between them, we performed a user study and identified a desirable gesture motion for this purpose. In addition, we provide the user with various feedback to help them understand the current registration progress and adhere to required conditions, which will lead to accurate registration results. The user studies show that Overthere is sufficiently intuitive to be used by ordinary people.
High-Efficiency Classification of White Blood Cells Based on Object Detection
Authors: Yao Jiangfan , Huang Xiwei , Wei Maoyu , Han Wentao , Xu Xuefeng , Wang Renjie , Chen Jin , Sun Lingling
Published: September 2021

White blood cells (WBCs) play a significant role in the human immune system, and the content of various subtypes of WBCs is usually maintained within a certain range in the human body, while deviant levels are important warning signs for diseases. Hence, the detection and classification of WBCs is an essential diagnostic technique. However, traditional WBC classification technologies based on image processing usually need to segment the collected target cell images from the background. This preprocessing operation not only increases the workload but also heavily affects the classification quality and efficiency. Therefore, we proposed one high-efficiency object detection technology that combines the segmentation and recognition of targets into one step to realize the detection and classification of WBCs in an image at the same time. Two state-of-the-art object detection models, Faster RCNN and Yolov4, were employed and comparatively studied to classify neutrophils, eosinophils, monocytes, and lymphocytes on a balanced and enhanced Blood Cell Count Dataset (BCCD). Our experimental results showed that the Faster RCNN and Yolov4 based deep transfer learning models achieved classification accuracy rates of 96.25% and 95.75%, respectively. For the one-stage model, Yolov4, while ensuring more than 95% accuracy, its detection speed could reach 60 FPS, which showed better performance compared with the two-stage model, Faster RCNN. The high-efficiency object detection network that does not require cell presegmentation can remove the difficulty of image preprocessing and greatly improve the efficiency of the entire classification task, which provides a potential solution for future real-time point-of-care diagnostic systems.
A Rare Case of a Migrating Inguinal Hernia Mesh Presenting as Acute Appendicitis
Authors: Nogueiro Jorge , Santos-Sousa Hugo , de Almeida Marinho , Malheiro Luis , Barbosa Elisabete
Published: September 2021

Acute appendicitis is a very common event. Migration of hernia mesh is rare, especially intraluminal migrations. We aim to report a case of a migrated inguinal mesh presenting as an acute appendicitis. A 58-year-old male previously submitted to ONSTEP right inguinal hernia repair with a PolySoft™ hernia patch eight years before, was admitted in the emergency department with acute appendicitis, and submitted to laparoscopic appendectomy. Intraoperatively, the “recoil ring” from the inguinal hernia patch was extended from the anterior abdominal wall to the appendix, perforating it and progressing intraluminally. Appendectomy was performed, as well as removal of the mesh by an anterior approach. Hernia mesh migration to an intraluminally position is extremely rare with only a few cases described in literature. Pathogenesis of migration is still poorly understood. Clinicians should consider hernia mesh migration in their differential diagnosis for causes of acute appendicitis, in the right clinical setting, when a previous hernia defect correction is present. To the best of our knowledge, this is the first reported case of inguinal hernia mesh migration to the appendix, presenting as acute appendicitis.
Optimized Design of Mechanical Chain Drive Based on a Wireless Sensor Network Data Algorithm
Authors: Zhuang Min , Li Ge , Ding Kexin , Xu Guansheng
Published: September 2021

In this paper, we use a wireless sensor network data algorithm to optimize the design of mechanical chain drive by conducting an in‐depth study of the mechanical chain drive optimization. We utilize the crowdsourcing feature of the swarm‐wise sensing network for assisted wireless sensor networking to achieve crowdsourcing‐assisted localization. We consider a framework for crowdsourcing‐assisted GPS localization of wireless sensor networks and propose two recruitment participant optimization objectives, namely, minimum participants and time efficiency, respectively. A model and theoretical basis are provided for the subsequent trusted data‐driven participant selection problem in swarm‐wise sensing networks. The sprocket‐chain engagement frequency has the greatest influence on the horizontal bending‐vertical bending composite in different terrain conditions. The dynamic characteristics under working conditions are most influenced, while the scraping of the scraper and the central groove significantly influenced horizontal bending and vertical bending. Under load conditions, the amplitude of the scraper and central groove scraping increases significantly, which harm the dynamics of the scraper conveyor. By monitoring the speed difference between the head and tail sprockets and the overhang of the scraper, the tensioning status of the scraper conveyor chain can be effectively monitored to avoid chain jamming and chain breakage caused by the loose chain, thus improving the reliability and stability of the scraper conveyor.
Prognostic Value of KRAS Gene Mutation on Survival of Patients with Peritoneal Metastases of Colorectal Adenocarcinoma
Authors: Díez-Alonso Manuel , Mendoza-Moreno Fernando , Gómez-Sanz Remedios , Matías-García Belén , Ovejero-Merino Enrique , Molina Raquel , Soto-Schütte Sonia , San Juan Alberto , Gutierrez-Calvo Alberto
Published: September 2021

Objective. The main objective of the study was to determine the effect of the presence of mutation in the KRAS gene on the survival in patients with colorectal cancer (CRC) and peritoneal metastases (PM). Materials and Methods. A retrospective cohort study was performed. Patients diagnosed with CRC with synchronous or metachronous PM between January 2006 and December 2019 were included. Data on the histopathological, clinical, and treatment factors were collected. The effect of each variable on survival was evaluated by Cox regression. Results. A total of 149 patients were included (64 women (43%) and 85 men (57%); mean age, 63 years). The long-term survival rate at 36 months was 24% (median, 21 months). KRAS mutation was detected in 75 patients (50.3%). Kaplan–Meier analysis estimated that likelihood of survival was higher in patients with wild-type KRAS tumours (35%) than in mutated-type KRAS (14%) (median: 28 vs. 15, respectively) (

P
=
0.001

). Within the categories into which the peritoneal cancer index (PCI) was classified, survival at 36 months depended on the KRAS status. Survival in wild-type KRAS tumours with PCI 1–10 was 71% and with PCI 11–20 was 26%, while in mutant-type KRAS tumours, survival was 41% and 4%, respectively (

P
=
0.025

). In the multiple regression analysis, the KRAS mutation was revealed to have an independent prognostic value (HR: 2.144; 95% CI: 1.342–3.424). Conclusion. The mutational status of the KRAS gene has demonstrated a strong association with survival and prognostic utility in patients with CRC with PM.
Application of Remote Sensing Technology in Sediment Estimating Entering the Dam Reservoirs due to Floods
Authors: Hadian Mohammad , Mosaedi Abolfazl
Published: September 2021

The present study aimed to use remote sensing technology to estimate the concentration of particulate materials in the water entering the reservoirs of dams and consequently investigate the possibility of estimating the amount of sediment carried to the reservoir by flood during the life of the dam and its annual estimate. Using an advanced spectrometer device (ASD), the reflectance values of water containing different amounts of particulate sediments were measured in the range of 400–2500 nm; then, these reflectance values were represented for the Landsat 8 satellite OLI bands using their spectral response functions. In the study of interband correlation with the number of particulate materials, band 2 (blue) and band 5 (near‐infrared) were identified to prepare a specific and appropriate model. The specificity of the reflectance difference in the two abovementioned bands was presented as an exponential relationship between the concentration of particulate materials and spectral reflectance. In this model, the RMSE parameter for the maximum possible sediment concentration was equal to 1.57 and the parameter R2 was equal to 0.91. In the second step, at the same time as the satellite passed, the area was visited and the sediments of the Ardak dam reservoir were sampled by recording their location. To complete this research, two measures were performed simultaneously, calculating the concentration of particulate materials sampled in the laboratory environment and their location on the image. Then, the number of particulate materials is estimated by taking into account the coordinates recorded from the images on which the relevant corrections have been made. According to the extracted exponential model, the results of estimating the concentration of particulate matter obtained from the model and Landsat satellite images with the concentration of particulate matter obtained from sampling showed its complete compatibility with field surveys to validate this research.
Analysis on Influence Factors of Roadway Instability in High‐Stress, Steeply Inclined Extra‐Thick Coal Seam
Authors: Rong Hai , Pan Liting , Li Xiaoyan , Wang Ming , Qu Zeliang , Lu Mengsheng , Guo Kaipeng
Published: September 2021

In order to solve the problem of roadway support safety in coal mining under high stress conditions and to improve safe and efficient production in coal mines, the control countermeasures of the surrounding rock stability and the optimization scheme of support are put forward and the model and numerical simulation of roadway bolt support system are established. Based on bolt support theory and instability mechanism of the coal rock dynamic system, this paper puts forward the evaluation of support effect and the optimization parameters of bolt support, and the scheme of mine pressure monitoring and the corresponding support optimization system are established. The roof fall accident and the bolt and cable of support have been broken in the Wudong coal mine, the phenomenon of bolt pulling out in the roadway. The causes of roof fall are analyzed and the solutions are put forth, judging the influence of different factors on roadway support. In view of the roof fall accident in the North Lane of the east wing of the +575 level 43 #coal seam in the north mining area of the Wudong coal mine, the cause analysis and support suggestions are made. And, according to the performance of the bolting material and anchoring agent, the laboratory theoretical research was carried out. Through the experiment, it is concluded that the FRP bolt with a diameter not less than 27 mm is the first choice for the side support of the working face in the mining roadway, then ribbed steel bolt with a diameter not less than 20 mm for the nonworking face, and the length of the anchor rod not less than the range of the loose circle. Therefore, full‐length anchoring should be carried out in roadway support, the anchorage length of the anchor cable should be increased, and the integrity of the roof should be improved, so as to reduce the amount of roadway roof separation and improve the support effect.
“At-Home” Photobiomodulation: A New Approach for Bell’s Palsy Treatment
Authors: Fornaini Carlo , Meng Zhao , Merigo Elisabetta , Rocca Jean-Paul
Published: September 2021

Objective. This report is the first one to describe the possibility to use “self‐administered” photobiomodulation (PBM) for Bell’s palsy (BP) treatment. Background. BP is a peripheral disorder of the facial nerve causing sudden paralysis of unilateral facial muscles, and PBM has been successfully suggested for its treatment without any side effect. This is the first case report where a laser device was successfully used at home by the patient herself to treat BP opening new perspectives on the therapy of this disease. Methods. This report describes the “at-home PBM” treatment performed on a 15-year-old girl who presented BP consisting of acute pain on the right side of her face, difficulty in biting and dripping saliva from the right side of her lips. The treatment was performed twice a day by cutaneous applications, each of 15 minutes (total fluence 48 J/cm2) in an area corresponding to the parotid gland by a device emitting at 808 nm at 250 mW output power. Results. Two weeks after PBM treatment, performed at home twice a day by the patient herself without any kind of pharmacological therapy, the complete disappearing of the disease was noticed with no side effects. Conclusion. With the limitations due to a single case report and with the need of further clinical trials to confirm it, “at-home PBM” seems to represent a good and safe approach to the treatment of BP.
Efficient Energy Utilization with Device Placement and Scheduling in the Internet of Things
Authors: Zhu Yanli , Yang Xiaoping , Hong Yi , Leng Youfang , Luo Chuanwen
Published: September 2021

The low‐power wide‐area network (LPWAN) technologies, such as LoRa, Sigfox, and NB‐IoT, bring new renovation to the wireless communication between end devices in the Internet of things (IoT), which can provide larger coverage and support a large number of IoT devices to connect to the Internet with few gateways. Based on these technologies, we can directly deploy IoT devices on the candidate locations to cover targets or the detection area without considering multihop data transmission to the base station like the traditional wireless sensor networks. In this paper, we investigate the problems of the minimum energy consumption of IoT devices for target coverage through placement and scheduling (MTPS) and minimum energy consumption of IoT devices for area coverage through placement and scheduling (MAPS). In the problems, we consider both the placement and scheduling of IoT devices to monitor all targets (or the whole detection area) such that all targets (or the whole area) are (or is) continuously observed for a certain period of time. The objectives of the problems are to minimize the total energy consumption of the IoT devices. We first, respectively, propose the mathematical models for the MTPS and MAPS problems and prove that they are NP‐hard. Then, we study two subproblems of the MTPS problem, minimum location coverage (MLC), and minimum energy consumption scheduling deployment (MESD) and propose an approximation algorithm for each of them. Based on these two subproblems, we propose an approximation algorithm for the MTPS problem. After that, we investigate the minimum location area coverage (MLAC) problem and propose an algorithm for it. Based on the MLAC and MESD problems, we propose an approximation algorithm to solve the MAPS problem. Finally, extensive simulation results are given to further verify the performance of the proposed algorithms.
Morphological Reconstruction-Based Image-Guided Fuzzy Clustering with a Novel Impact Factor
Authors: Qin Qingxue , Xu Guangmei , Zhou Jin , Wang Rongrong , Jiang Hui , Wang Lin , Han Shiyuan , Du Tao , Ji Ke , Zhao Ya-ou , Zhang Kun
Published: September 2021

The guided filter is a novel explicit image filtering method, which implements a smoothing filter on “flat patch” regions and ensures edge preserving on “high variance” regions. Recently, the guided filter has been successfully incorporated into the process of fuzzy c-means (FCM) to boost the clustering results of noisy images. However, the adaptability of the existing guided filter-based FCM methods to different images is deteriorated, as the factor

ε

of the guided filter is fixed to a scalar. To solve this issue, this paper proposes a new guided filter-based FCM method (IFCM_GF), in which the guidance image of the guided filter is adjusted by a newly defined influence factor

ρ

. By dynamically changing the impact factor

ρ

, the IFCM_GF acquires excellent segmentation results on various noisy images. Furthermore, to promote the segmentation accuracy of images with heavy noise and simplify the selection of the influence factor

ρ

, we further propose a morphological reconstruction-based improved FCM clustering algorithm with guided filter (MRIFCM_GF). In this approach, the original noisy image is reconstructed by the morphological reconstruction (MR) before clustering, and the IFCM_GF is performed on the reconstructed image by utilizing the adjusted guidance image. Due to the efficiency of the MR to remove noise, the MRIFCM_GF achieves better segmentation results than the IFCM_GF on images with heavy noise and the selection of the influence factor for the MRIFCM_GF is simple. Experiments demonstrate the effectiveness of the presented methods.
NSD‐SSD: A Novel Real‐Time Ship Detector Based on Convolutional Neural Network in Surveillance Video
Authors: Sun Jiuwu , Xu Zhijing , Liang Shanshan
Published: September 2021

With the rapid development of the marine industry, intelligent ship detection plays a very important role in the marine traffic safety and the port management. Current detection methods mainly focus on synthetic aperture radar (SAR) images, which is of great significance to the field of ship detection. However, these methods sometimes cannot meet the real‐time requirement. To solve the problems, a novel ship detection network based on SSD (Single Shot Detector), named NSD‐SSD, is proposed in this paper. Nowadays, the surveillance system is widely used in the indoor and outdoor environment, and its combination with deep learning greatly promotes the development of intelligent object detection and recognition. The NSD‐SSD uses visual images captured by surveillance cameras to achieve real‐time detection and further improves detection performance. First, dilated convolution and multiscale feature fusion are combined to improve the small objects’ performance and detection accuracy. Second, an improved prediction module is introduced to enhance deeper feature extraction ability of the model, and the mean Average Precision (mAP) and recall are significant improved. Finally, the prior boxes are reconstructed by using the K‐means clustering algorithm, the Intersection‐over‐Union (IoU) is higher, and the visual effect is better. The experimental results based on ship images show that the mAP and recall can reach 89.3% and 93.6%, respectively, which outperforms the representative model (Faster R‐CNN, SSD, and YOLOv3). Moreover, our model’s FPS is 45, which can meet real‐time detection acquirement well. Hence, the proposed method has the better overall performance and achieves higher detection efficiency and better robustness.
Parameter Analysis of an Intershaft Dual‐Rotor with the Application of Squeeze Film Dampers
Authors: Chen Huizheng , Zhong Shun , Lu Zhenyong , Chen Yushu , Liu Xiyu
Published: September 2021

The squeeze film damper is usually adopted in the rotor system to suppress the vibrating motion of the rotor system. In this work, not only are the physical parameters of the squeeze film damper analyzed but also the system parameters, like the number of squeeze film dampers used and squeeze film damper implementation positions, are analyzed. The amplitude‐frequency curves are obtained by conducting the simulation of a dual‐rotor, intershaft, and oil film force concatenated model. Through the analysis and comparisons of the results, the vibration suppression effects of the squeeze film damper with different parameter configurations are analyzed and summarized. This work contributes to further optimization and dynamical analysis work on rotor systems with the application of the squeeze film damper.
Some New Kinds of Fractional Integral Inequalities via Refined

α
,
h
−
m

-Convex Function
Authors: Zahra Moquddsa , Ashraf Muhammad , Farid Ghulam , Nonlaopon Kamsing
Published: September 2021

In this article, we present new integral inequalities for refined

α
,
h
−
m

-convex functions using unified integral operators (12) and (13). The established results provide the refinements of several well-known integral and fractional integral inequalities.
Research on Classification Method of Eggplant Seeds Based on Machine Learning and Multispectral Imaging Classification Eggplant Seeds
Authors: Sun Lei , Fan Xiaofei , Huang Sheng , Luo Shuangxia , Zhao Lili , Chen Xueping , He Yi , Suo Xuesong
Published: September 2021

In this study, eggplant seeds of fifteen different varieties were selected for discriminant analyses with a multispectral imaging technique. Seventy‐eight features acquired with the multispectral images were extracted from individual eggplant seeds, which were then classified using SVM and a one‐dimensional convolutional neural network (1D‐CNN), and the overall accuracy was 90.12% and 94.80%, respectively. A two‐dimensional convolutional neural network (2D‐CNN) was also adopted for discrimination of seed varieties, and an accuracy of 90.67% was achieved. This study not only demonstrated that multispectral imaging combining machine learning techniques could be used as a high‐throughput and nondestructive tool to discriminate seed varieties but also revealed that the shape of the seed shell may not be exactly the same as the female parents due to the genetic and environmental factors.
The Value of Biological Image Technology in Improving Athletes’ Motor Skills
Authors: Huang Lu , Han Hongfei
Published: September 2021

Objective. To explore the value of biological image technology in improving athletes’ motor skills. Methods. Golfers were given biofeedback relaxation training, and the variation trend and influence of biofeedback training on athletes’ heart rate variability were explored by monitoring and feedback evaluation of athletes’ heart rate variability. Results. Biofeedback relaxation training can help athletes improve the balance of sympathetic nerve and parasympathetic nerve, effectively inhibit the activity of sympathetic nerve, enhance the tension, effectively increase the heart rate variability of athletes, and strengthen the ability of psychological relaxation. Conclusion. Part of the research results show that biofeedback training can enhance athletes’ athletic performance and improve their athletic performance to a certain extent.
Optimization of Brand Marketing Strategy of Intelligent Technology under the Background of Artificial Intelligence
Authors: Liu Yishu , Chen Weixiong
Published: September 2021

With the progress of Internet technology and the rapid development of artificial intelligence, intelligent technology products rapidly grow up. However, influenced by many factors, the marketing status of this kind of products in recent years is not ideal, for example, big data leakage and other issues. Especially in recent years, because of the greater market competition, the marketing of intelligent technology brands is facing greater problems. The purpose of this study is to analyze the current situation of brand marketing of intelligent technology under the background of artificial intelligence and propose the optimization strategy of brand marketing of such products, so as to promote the continuous improvement of brand marketing effect of intelligent technology. Based on artificial intelligence and the concept of brand marketing, through intelligent technology brand marketing investigation and experiment, the intelligent technology brand marketing status and current problems are analyzed; from marketing, product type, and product price, three angle analysis of the factors that influence the marketing and Internet marketing, product quality, product pricing, and product development is put forward from the four angles of specific strategy of optimization of smart technology products’ brand marketing. The experimental results show that the year-on-year growth rate of smart technology brand turnover is decreasing at the present stage. The increase rate of the turnover in 2019 is only 3.24%, far lower than that in 2015, and the growth rate of the turnover decreases by about 33%. At present, there are many problems in the marketing strategy of intelligent technology brands, and the optimization of marketing strategy is an urgent problem to be solved.
SuperPruner: Automatic Neural Network Pruning via Super Network
Authors: Liu Yu , Wang Yong , Qi Haojin , Ju Xiaoming
Published: September 2021

Most network pruning methods rely on rule-of-thumb for human experts to prune the unimportant channels. This is time-consuming and can lead to suboptimal pruning. In this paper, we propose an effective SuperPruner algorithm, which aims to find optimal pruned structure instead of pruning unimportant channels. We first train a VerifyNet, a kind of super network, which is able to roughly evaluate the performance of any given network structure. The particle swarm optimization algorithm is then used to search for optimal network structure. Lastly, the weights in the VerifyNet are used as the initial weights of the optimal pruned structure to make fine-tuning. VerifyNet is a network performance evaluation; our algorithm can quickly prune the network under any hardware constraints. Our algorithm can be applied in multiple fields such as object recognition and semantic segmentation. Extensive experiment results demonstrate the effectiveness of SuperPruner. For example, on CIFAR-10, the pruned VGG16 achieves 93.18% Top-1 accuracy and reduces 74.19% of FLOPs and 89.25% of parameters. Compared with state-of-the-art methods, our algorithm can achieve higher pruned ratio with less accuracy cost.
The Novel RET Inhibitor SYHA1815 Inhibits RET-Driven Cancers and Overcomes Gatekeeper Mutations by Inducing G1 Cell-Cycle Arrest through c-Myc Downregulation
Authors: Jiang Yuchen , Peng Xia , Ji Yinchun , Dai Yang , Fang Yanfen , Xiong Bing , Ren Wenming , Hu Youhong , Chen Yi , Ai Jing
Published: September 2021

Rearranged during transfection (RET), an oncogenic driver, has been found in multiple tumor types and is thus a promising anticancer therapeutic target. Novel selective RET inhibitors (RETi) that can overcome V804 gatekeeper mutations, endowing resistance to multikinase inhibitors (MKI) and, in particular, achieving KDR selectivity, are needed. In addition, the mechanisms underlying RET–inhibition-induced antiproliferative effects in the context of RET addiction are incompletely understood. This study describes a novel selective RETi, SYHA1815, which inhibited the kinase activity of RET wild type and V804 mutant with an IC50 in the subnanomolar to nanomolar range. Notably, SYHA1815 exhibited approximately 20-fold selectivity for RET over KDR, almost equivalent to that of the launched selective inhibitor pralsetinib. SYHA1815 had only a marginal inhibitory effect on cellular KDR signaling at a high (200 nmol/L) concentration, confirming the selectivity over KDR. In addition, SYHA1815 exhibited a favorable selectivity profile, with greater than 100-fold selectivity for RET over 347 other kinases. It exhibited potent antitumor efficacy and overcame V804 mutations in vitro and in vivo by targeting RET. Then, using SYHA1815 as a probe, we found that RET inhibition suppressed RET-driven cell proliferation via G1 cell-cycle arrest through downregulating c-Myc. Furthermore, disruption of c-Myc upon Brd4 inhibitor treatment led to G1 cell-cycle arrest and overrode RET-driven cell proliferation. Moreover, consistent with the marked in vivo efficacy of RET inhibition, the intratumoral c-Myc level was significantly decreased. In summary, SYHA1815 is a promising RETi for RET-aberrant cancer treatment that is currently in a phase I trial.
Adenosine A2A Receptor Activation Enhances Blood–Tumor Barrier Permeability in a Rodent Glioma Model
Authors: Vézina Amélie , Manglani Monica , Morris DreeAnna , Foster Brandon , McCord Matthew , Song Hua , Zhang Meili , Davis Dionne , Zhang Wei , Bills Jessica , Nagashima Kunio , Shankarappa Priya , Kindrick Jessica , Walbridge Stuart , Peer Cody J. , Figg William D. , Gilbert Mark R. , McGavern Dorian B. , Muldoon Leslie L. , Jackson Sadhana
Published: September 2021

The blood–tumor barrier (BTB) limits the entry of effective chemotherapeutic agents into the brain for treatment of malignant tumors like glioblastoma. Poor drug entry across the BTB allows infiltrative glioma stem cells to evade therapy and develop treatment resistance. Regadenoson, an FDA-approved adenosine A2A receptor (A2AR) agonist, has been shown to increase drug delivery across the blood–brain barrier in non–tumor-bearing rodents without a defined mechanism of enhancing BTB permeability. Here, we characterize the time-dependent impact of regadenoson on brain endothelial cell interactions and paracellular transport, using mouse and rat brain endothelial cells and tumor models. In vitro, A2AR activation leads to disorganization of cytoskeletal actin filaments by 30 minutes, downregulation of junctional protein expression by 4 hours, and reestablishment of endothelial cell integrity by 8 hours. In rats bearing intracranial gliomas, regadenoson treatment results in increase of intratumoral temozolomide concentrations, yet no increased survival noted with combined temozolomide therapy. These findings demonstrate regadenoson's ability to induce brain endothelial structural changes among glioma to increase BTB permeability. The use of vasoactive mediators, like regadenoson, which transiently influences paracellular transport, should further be explored to evaluate their potential to enhance central nervous system treatment delivery to aggressive brain tumors.

Implications:

This study provides insight on the use of a vasoactive agent to increase exposure of the BTB to chemotherapy with intention to improve glioma treatment efficacy.