Miracle-dz/UniFAR-FTUs huggingface dataset:
On Energy Efficiency Optimization for Network Slices in 5G Power Communication System

In this paper, we propose a cloud-edge model for power communication systems to allocate resources in network slices. As a result, diverse requirements for different 5G applications in power networks can be satisfied. Firstly, we adopt MIMO and OFDM in the edge computing network to support the power line communication (PLC) for terminal users. Second, to achieve maximal energy efficiency for the network slice in the PLC network, we formulate the optimal power allocation problem to maximize the energy efficiency. We adopt the nonlinear fractional programming to transform the original nonconvex problem to a convex one. Later on, we design a heuristic algorithm to solve the optimal problem to achieve the optimal solution. Simulation results show that the optimization algorithm for network slices in PLC networks can significantly improve the energy efficiency.
Partial Task Offloading Strategy Based on Deep Reinforcement Learning

Mobile Edge Computing (MEC) can offload terminal tasks to edge servers to save its limited resources. We propose a dynamic offloading strategy for the edge computing system in a cellular network. The optimization problem of energy consumption and offloading cost minimization is established, and the offloading decision problem is transformed into a multi-label classification problem, making it easier for DRL to solve. An offloading decision model based on deep reinforcement learning is constructed, in which the system state is the relevant variable; the action is the offloading strategy. The deep reinforcement learning framework is implemented by a deep neural network. It learns binary offloading decision from experience replay and updates neural network parameters according to it. The neural network output results then will be quantified by the K-Nearest Neighbor (KNN) algorithm. The simulation results show that this method could not only solve the combinatorial optimization problem but also significantly simplify the computational process and the total resource overhead.
Authentication Method for WiFi Connection of Devices Based on Channel State Information

The popularity of intelligent mobile devices enables users to access various information services anytime and anywhere, which increases people's demand for communication networks. WiFi network is one of the main ways for smart devices to access the network. Compared with using cellular network, users are more willing to connect to WiFi when there is WiFi coverage, and even automatically crack the WiFi password to connect. In order to improve the access security of WiFi devices, this paper proposes an Authentication method for WiFi connection of device based on Channel State Information (CSI). The method can effectively prevent illegal users from connecting WiFi outside the legal area. When devices are connected to WiFi in different locations, it will cause different changes in CSI. According to this feature, a fingerprint database is established in each area within the WiFi signal range. Hampel filter is used to remove the noise caused by environmental factors in CSI data stream. Support Vector Machine (SVM) is used to filter the non-limited locations. However, WiFi authentication has a strong real-time requirement. SVM algorithm which only uses cross-validation to select parameters can not quickly find the appropriate parameters to adapt to the current environment. To solve the above problems, Percentile algorithm is used to improve the efficiency of RBF kernel function parameter selection, which significantly improves the real-time performance of authentication methods. The experimental results show that the accuracy of correct authentication can reach about 95%.
A Novel DNA Methylation Signature as an Independent Prognostic Factor in Muscle-Invasive Bladder Cancer

Background Muscle-invasive bladder cancer (MIBC) accounts for approximately 20% of all urothelial bladder carcinomas (UBC) at time of diagnosis, and up to 30% of patients with non-muscle invasive UBC will progress to MIBC over time. An increasing body of evidence has revealed a strong correlation between aberrant DNA methylation and tumorigenesis in MIBC. Results Using The Cancer Genome Atlas (TCGA) molecular data for 413 patients, we described a DNA methylation-based signature as a prognostic factor for overall survival (OS) in MIBC patients. By using a least absolute shrinkage and selection operator (LASSO) model, differentially methylated regions were first identified using multiple criteria followed by survival and LASSO analyses to identify DNA methylation probes related to OS and build a classifier to stratify patients with MIBC. The prognostic value of the classifier, referred to as risk score (RS), was validated in a held-out testing set from the TCGA MIBC cohort. Finally, receiver operating characteristic (ROC) analysis was used to compare the prognostic accuracy of the models built with RS alone, RS plus clinicopathologic features, and clinicopathologic features alone. We found that our seven-probe classifier-based RS stratifies patients into high- and low-risk groups for overall survival (OS) in the testing set (n = 137) (AUC at 3 years, 0.65; AUC at 5 years, 0.65). In addition, RS significantly improved the prognostic model when it was combined with clinical information including age, smoking status, Tumor (T) stage, and Lymph node metastasis (N) stage. Conclusions The DNA methylation-based RS can be a useful tool to predict the accuracy of preoperative and/or post-cystectomy models of OS in MIBC patients.
Attitudes and Perceptions Toward Healthcare Technology Adoption Among Older Adults in Singapore: A Qualitative Study

Smart Nation is a key initiative of Singapore to move toward digitalization of its industries including healthcare. The complex negotiations of aging amid Smart Nation are addressed in this paper, where we study the challenges faced to adapt the elderly for the digital revolution while ensuring dignified aging. While the healthcare industry accelerates its study and use of health technologies to improve diagnostics, treatment, and the quality of life of those in the aging category, the elderly socially construct these technological insertions that challenge the dominant understandings of what these technologies can do for their health outcomes. The study reveals re-constructions of these technological insertions through the voice of the elderly in their negotiations with health technologies in their everyday lives. Here, narratives reveal key themes that proliferate technology negotiation as barriers to everyday lived experiences.
Defining Influent Scenarios: Application of Cluster Analysis to a Water Reclamation Plant

AbstractProcess control at older water reclamation plants (WRPs) can be challenging as operators work to balance higher energy costs and more stringent effluent limitations while managing fluctuating loads. To better understand WRP loading characteristics, we developed a classification system for the Metropolitan Water Reclamation District of Greater Chicago (MWRDGC) Calumet WRP by combining k-means cluster analysis with cross-tabulation analysis. Based on weather and influent composition characteristics, we identified 25 clusters; nine of those clusters are significant (99% confidence level) based on chi-square tests. Dry weather and middle temperature conditions after wet-weather days are common, and compared to cold-weather flows, warm-weather flows are more likely to have large precipitation events and more variation in influent quality. To explore how information about influent scenarios could inform process control and improve process efficiency, we also developed a process simulation model. The mod.
Political Arabic Articles Orientation Using Rough Set Theory With Sentiment Lexicon

Sentiment analysis is an emerging research field that can be integrated with other domains, including data mining, natural language processing and machine learning. In political articles, it is difficult to understand and summarise the state or overall views due to the diversity and size of social media information. A number of studies were conducted in the area of sentiment analysis, especially using English texts, while Arabic language received less attention in the literature. In this study, we propose a detection model for political orientation articles in the Arabic language. We introduce the key assumptions of the model, present and discuss the obtained results, and highlight the issues that still need to be explored to further our understanding of subjective sentences. The main purpose of applying this new approach based on Rough Set (RS) theory is to increase the accuracy of the models in recognizing the orientation of the articles. We present extensive simulation results, which demonstrate the superiority of the proposed model over other algorithms. It is shown that the performance of the proposed approach significantly improves by adding discriminating features. To summarize, the proposed approach demonstrates an accuracy of 85.483%, when evaluating the orientation of political Arabic datasets, compared to 72.58% and 64.516% for the Support Vector Machines and Naïve Bayes methods, respectively.
Adaptive Extraction-Based Independent Component Analysis for Time-Sensitive Applications

Blind Source Separation (BSS) for time-sensitive applications in the Internet of Things (IoT) results in a tradeoff between separation speed and accuracy. Data extraction has been widely employed recently to solve this problem. Although the introduction of current data extraction methods reduces the required time for separation, it is at the expense of separation quality. In this paper, we propose Adaptive extraction-based Independent Component Analysis (AeICA) to address these limitations. Specifically, the speed of separation is improved by using the extracted subset of the available data without affecting the overall separation accuracy, which we demonstrate through extensive numerical evaluations. In particular, AeICA reduces the total separation time by 50% to 75%, compared to the most remarkable related work.
The Design of Intelligent production line for Clothing Industry

To promote the development of the textile industrial and upgrade the existing production technology. This article explored the intelligent application scenarios by analyzing the production process and technology of clothing production process. The hardware deployment and the management architecture of the production line were designed by combining the machine vision, deep learning and robot technology. And the performance of the intelligent algorithms under the production framework were also verified in this article.
Joint Transmission Reception Point Selection and Resource Allocation for Energy-Efficient Millimeter-Wave Communications

In this paper, we study the joint transmission reception point (TRP) selection and resource allocation problem to maximize the weighted sum energy efficiency under imperfect channel state information (CSI) for an uplink mmWave network considering beam training overheads and delay constraints with and without inter-beam interference, respectively. First, to guarantee the delay constraints of all UEs, we use the worst-case approach to cast the considered problem to a deterministic optimization problem. Then, for the scenario without inter-beam interference, the product of transmitting and receiving beams and the power allocation scheme for each pair of UEs and TRPs are derived iteratively, where the former is obtained by some relaxation operations under given power allocation policy, and the latter is searched by bisection under given beamwidth selection policy. After that, Kuhn-Munkres algorithm is used to find the joint TRP selection and resource allocation policy. For the scenario with inter-beam interference, coalition game is proposed to solve the considered problem, where UEs form coalitions to select their respective serving TRPs. At each iteration of the coalition game, the beamwidth control and power allocation are performed successively, where the transmitting and receiving beamwidths are solved by particle swarm algorithm under given power allocation policy, and the power allocation strategy is designed by a novel cooperative cost mechanism under given beamwidth selection policy. Finally, extensive simulation results are provided to verify that the proposed schemes achieve better weighted sum energy efficiency than existing schemes.
Design of Physical Layer Secure Transmission in RF Energy Harvesting Cognitive Radio Network

This paper considers a multiple-input multiple-output (MIMO) cognitive radio network (CRN) with multiple primary user nodes, multiple secondary user nodes, and an eavesdropping node. The primary user network includes a primary user transmitting node (PUT) and multiple primary user receiving nodes (PURs), all of which are equipped with one antenna. The secondary user network includes a secondary user transmitting node (SUT) and multiple secondary user receiving nodes (SURs), all of which are equipped with multiple antennas. The eavesdropping node (Eve) is equipped with multiple antennas. The energy of PUT is sufficient, while the energy of SUT is limited. SUT communicates with SURs by harvesting the radio frequency (RF) energy emitted by PUT. Based on the dual selection of SUT antennas and SURs, the optimal selection (OS) scheme and the suboptimal selection (SS) scheme are proposed. The closed-form expressions of the secrecy outage probability (SOP) of two schemes are derived. Finally, the derivation is verified by simulation and the simulation results show that the security performance of OS scheme is better than SS scheme. In addition, the influence of different parameters on SOP is also analyzed.
Cost-Efficient Data Retrieval Based on Integration of VC and NDN

The Vehicular Cloud (VC) is a new vehicular system and aims to achieve cost-efficient data retrieval with high success rates. The Named Data Networking (NDN) presents a new data-centric mechanism and its advantages may help achieve VC. Therefore, we are motivated to exploit NDN to achieve VC. However, the intrinsic features of vehicles such as high mobility and the different architectures between NDN and vehicular networks make it challenging to deploy NDN in vehicular scenarios. For example, NDN employs limited broadcast and reverse paths to achieve data retrieval, which might lead to high data retrieval costs and frequent failures in vehicular environments. Furthermore, the lack of mobility support in NDN deteriorates the situation. Taking these issues into account, we propose a cost-efficient and VC-based data retrieval solution and aims to achieve VC by exploiting the advantages of NDN. In this solution, vehicles employ unicast and aggregation to acquire data from the nearest VC member via one data retrieval process, so efficient-cost and low-latency data retrieval is achieved. Moreover, this solution supports vehicle mobility and enhances stability of reverse paths, so data retrieval success rates are effectively improved. The proposal is evaluated, and the experimental results show that the proposal effectively reduces data retrieval costs and improve data retrieval success rates.
Robotic Instrument Segmentation With Image-to-Image Translation

The semantic segmentation of robotic surgery video and the delineation of robotic instruments are important for enabling automation. Despite major recent progresses, the majority of the latest deep learning models for instrument detection and segmentation rely on large datasets with ground truth labels. While demonstrating the capability, reliance on large labelled data is a problem for practical applications because systems would need to be re-trained on domain variations such as procedure type or instrument sets. In this letter, we propose to alleviate this problem by training deep learning models on datasets that are synthesised using image-to-image translation techniques and we investigate different methods to perform this process optimally. Experimentally, we demonstrate that the same deep network architecture for robotic instrument segmentation can be trained on both real data and on our proposed synthetic data without affecting the quality of the output models’ performance. We show this for several recent approaches and provide experimental support on publicly available datasets, which highlight the potential value of this approach.
Depressive and Anxiety Symptoms Among People Under Quarantine During the COVID-19 Epidemic in China: A Cross-Sectional Study

Background: During the coronavirus disease 2019 (COVID-19) pandemic, quarantine as an effective public health measure has been widely used in China and elsewhere to slow down the spread, while high-risk psychological response populations remain under-reported. Objective: The aim of the study is to investigate the depressive and anxiety symptoms among the high-risk individuals quarantined during the COVID-19 pandemic in China. Methods: An online survey was conducted from February 29 to April 10, 2020, among individuals quarantined for at least 2 weeks due to the high-risk exposure. Chinese versions of the nine-item Patient Health Questionnaire (PHQ-9) with a seven-item Generalized Anxiety Disorder (GAD-7) were applied to assess depressive and anxiety symptoms, respectively. Compliance with quarantine and knowledge of COVID-19 was also assessed. An unconditional logistic regression model was performed to identify the correlators. Results: Of the 1,260 participants completing the full survey, 14.0% (95% CI: 12.2–16.1%), 7.1% (95% CI: 5.9–8.7%), and 6.3% (95% CI: 5.1–7.8%) had at least moderate symptoms of depression, anxiety, and a combination of depression and anxiety (CDA), respectively; 14.8% (95% CI: 13.0–16.9%) had at least one condition. Multivariate analysis showed that participants with an undergraduate or above degree were more likely to report depressive (OR = 2.98, 95% CI: 1.56–5.72) and anxiety symptoms (OR = 2.95, 95% CI: 1.14–7.63) than those with middle school education. Those who were unemployed (OR = 0.37, 95% CI: 0.21–0.65 for depression; OR = 0.31, 95% CI: 0.14–0.73 for anxiety), students (OR = 0.14, 95% CI: 0.04–0.48 for depression; OR = 0.11, 95% CI: 0.01–0.85 for anxiety), and more knowledgeable of COVID-19 (OR = 0.84, 95% CI: 0.73–0.96 for depression, OR = 0.82, 95% CI: 0.68–0.98 for anxiety) were less likely to report depressive and anxiety symptoms. Higher quarantine compliance correlated with lower risks of depressive (OR = 0.94, 95% CI: 0.91–0.96) and anxiety symptoms (OR = 0.95, 95% CI: 0.91–0.98). Conclusion: Individuals under quarantine during the COVID-19 pandemic suffered prevalent depressive and anxiety symptoms. Consequently, comprehensive interventional measures, including knowledge dissemination, timely virus tests, and strengthened communication, may minimize quarantine's adverse effects.
BBR-Based Congestion Control and Packet Scheduling for Bottleneck Fairness Considered Multipath TCP in Heterogeneous Wireless Networks

Aiming at improving the performance of Multipath TCP (MPTCP) in heterogeneous wireless network environments, in this paper, by taking the advantages Bottleneck Bandwidth and Round-trip propagation time (BBR) and considering bottleneck fairness, we propose BBR-based Congestion Control and Packet Scheduling scheme, called BCCPS. The proposed BCCPS first considers a BBR-based congestion control algorithm, MPTCP-BBR, for MPTCP by adaptively adjusting the sending rate of each subflow according to the real probing rate rather than the loss information to enhance the goodput while keeping bottleneck fairness with regular TCP. Then, considering that the different sending rates of the subflows in heterogeneous wireless networks will result in the problem of out-of-order (OFO) delivery, a fine-grained packet scheduling scheme is proposed to keep in-order delivery and so as to reduce the application layer completion time. The performance of the proposed scheme is evaluated via both NS-3 and real network scenarios. Experimental results show that our proposed scheme far outperforms existing MPTCP schemes in heterogeneous wireless environment.
UAV Localization with Multipath Fingerprints and Machine Learning in Urban NLOS Scenario

With the increasing number and application of unmanned aerial vehicle (UAV) in urban areas, positioning of UAV has become one of the key technologies for maintaining city security and managing airspace resources. Time of arrival (TOA) based location technology is widely used for its high precision, but its performance may suffer from strong multipath and NLOS propagation in urban scenario. However, the NLOS components may also be useful for positioning if the propagation path can be analyzed in a map model. In this paper, a multipath fingerprint dataset for an urban area is built by ray tracing simulation. Based on this dataset, we propose a two-stage localization method on machine learning framework. Firstly, in the stage of coarse positioning, the Random Forest (RF) algorithm is applied to determine which region the UAV is located in. Then, in the fine positioning stage, a neural network is trained to predict the specific location within the region. The simulation results in a $600 \times 600\ m^{2}$ region show that 90% of the positioning error of this method is less than 16m.
Congestion Perception Based on Load Estimation in Spread Spectrum Aloha

Satellite communication plays an irreplaceable role in realizing the seamless coverage and improving the operation efficiency of the broadband network. In order to ensure the stable operation of the satellite system, it is necessary to study the access congestion perception. The current researches mainly perceive the possible congestion through the estimation and prediction of the load. But few people have studied the congestion perception technology in the case of spread spectrum Aloha. Spread-spectrum Aloha technology has the advantage of improving throughput, and it can solve the problem of mobile terminal power limitation. This paper studies how to perceive access congestion when the advantage of pure spread-spectrum Aloha multi-access technology is extended to the geosynchronous orbit satellite system with regional space. This paper presents the mathematical model of spread spectrum Aloha multiple access and analyzes its throughput performance. We simulate and analyze the proposed load estimation scheme. The RMS error of simulation results is less than 2.
Dual Mathematical Model for Calculating of Deep Packet Inspection

This article describes a dual mathematical model for calculating deep packet inspection systems, based on the Norros model and the Ventcel-Ovcharov model. The modified Norros model is used to calculate the hardware filter for the deep packet inspection system. The Ventcel-Ovcharov model is used to calculate the remaining specialized servers of the deep packet inspection system. The created simulation model of the deep packet inspection system in GPSS World is described. The results of the mathematical and simulation modeling are compared.