Miracle-dz/UniFAR-FTUs huggingface dataset:
Device-Centric Positioning using Radiomap Database on Cellular Networks

The Global Positioning System (GPS) is the most effective positioning technique and used mostly in outdoor environments. Unfortunately, GPS consumes considerably high power and excessively drains battery life while tracking locations. Wi-Fi positioning system (WPS) also suffers from the battery drain issues related to prohibitively enormous Wi-Fi scans and network connections while computing locations at a remote server. We point out that a wireless positioning based on cellular networks can provide a reasonable accuracy depending on cell density and in the meantime, impose negligible power consumption. However, the cellular networks were not designed for a positioning purpose. In this paper, we propose a downloadable radiomap database and a Device-Centric Positioning (DCP) based on the radiomap database stored at the mobile terminal’s memory. The DCP is desirable for tracking locations and working at the mobile device in a background manner. The proposed methodology was implemented in the mobile device and evaluated through field tests.
CT-Based MR Synthesis Using Adversarial Cycle-Consistent Networks with Paired Data Learning

Radiotherapy devices using magnetic resonance (MR) imaging are being developed because MR is a safe imaging protocol that provides clear anatomical details. However, the application of MR-based radiotherapy to the aging population is limited because of its high cost and the increased use of metal implants such as cardiac pacemakers and artificial joints. To improve the accuracy of computed tomography (CT)-based radiotherapy planning, we propose a synthetic approach that translates a CT image into an MR image using adversarial cycle-consistent networks with paired data learning. The networks were trained to transform 2D brain CT image slices into 2D brain MR image slices, combining adversarial loss, cycle-consistent loss, voxel-wise loss, and gradient difference loss. The experiments were analyzed using the CT and MR images of 20 subjects, and an ablation study was conducted to show the strength of the proposed objective function. The experimental results show that the proposed method is accurate and robust for predicting MR images from CT images and also outperforms two state-of-the-art methods.
An Enhanced OLSR Routing Protocol based on Node Link Expiration Time and Residual Energy in Ocean FANETS

Due to complex and varied environment in the sea and high mobility of nodes, establishing a communication connection between UAVs is a difficult point. In addition, these existing routing protocols designed for mobile ad hoc networks (MANETs) or vehicle ad hoc networks (VANETs) could not play a good role in communication in Flying ad hoc Networks (FANETs). In this paper, we put forward a new form of the Optimized Link-State Routing (OLSR) protocol: it can suit rapid dynamic topology changes and prevent the interruption of communication. The key idea is to make full use of Global Positioning System (GPS) information to calculate node link expiration time and take into account residual energy. The improved multi metric ETX (Expected Transmission Count) simulation in OLSR is carried out in the NS-3 simulator. So, this paper reports the experimental results of routing protocols carried by small flying robots in marine scenes, and these numerical results show that the new protocol is significantly superior to the traditional OLSR in terms of packet transmission rate, End-to-End delay and routing overheads, providing reliable communication in the ocean FANETs.
Competitive Influence Blocking in Online Social Networks: A Case Study on WeChat

Rapid development of online social networks further facilitates the expansion of information over the Internet. In many scenarios, several pieces of different or even opposite information could diffuse competitively at the same time. Recently, some messenger APPs such as WeChat arose, where users can send links in their "WeChat Moments (WM)", which is called the messenger-based social network (Msg-SN). The unique characteristics in Msg-SN may lead to great difference on the network topology compared to conventional social networks. It is impossible to find the key opinion leaders with millions of followers to help block the rumors or to locate the source of rumors. Thus, the business often tries to select and inject a set of users in the network with the truth to block negative influence diffusion. We call this problem as the competitive influence blocking (CIB) problem. In this paper, we study the CIB problem in Msg-SN under the competitive linear threshold (CLT) model. We propose a fast heuristic algorithm based on eigenvector centrality to optimize the negative influence reduction by selecting a positive seed set. The experimental results using real-world WeChat Moments data show that our algorithm performs better than the effective CLDAG algorithm and also runs faster.
Application of Convolutional Neural Network (Cnn)in Microblog Text Classification

At present, Weibo has become the main platform of lyric fermentation in China. On this platform, Chinese people can discuss many major events, so it is very necessary to monitor the grievances on Weibo in time. This paper aims to classify and monitor Weibo public opinion through Convolutional Neural Network (CNN). Firstly, the data is cleaned up and a vocabulary is built. Then the model of the convolutional neural network is built, including the embedding layer, the convolution layer, the pooling layer and the fully connected layer. Finally, the data is predicted and classified by the Softmax function. The experimental results show that the model can effectively classify and predict the Weibo public opinion, which is a certain improvement compared with the traditional machine learning algorithm.
Process Mining in Healthcare: A Systematic Review

Process mining (PM) aims at discovering, monitoring and improving real processes by obtaining knowledge from event logs. It has been used in a variety of fields and has gained attention due to its ability to optimise processes, detect bottlenecks and reduce costs. In this article we provide a concise overview of process mining applied in the healthcare sector, which due to its inner complexity represents a challenge. The contribution of the article is two-fold: First, we present the results of a thorough review of the PM literature applied to the healthcare domain. We do not explain all papers in detail, on the contrary, we discuss current trends along nine dimensions of PM, namely Objective, Type, Perspective, Algorithms & Tools, Medical facilities, Medical fields, Medical process type, Medical data, and Preprocessing. By doing so, this article serves as an introductory reference for researchers interested in this field. Second, we discuss the main challenges of PM applied to the healthcare domain and point out several research lines that remain open.
A Wearable Rectenna to Harvest Low-Power RF Energy for Wireless Healthcare Applications

The article focuses on designing a wearable rectenna to harvest low-power RF energy for a biomedical device pasted on human body. The wearable textile material we selected for on-body application is cordura fabric. The textile rectenna is designed at the WiFi frequency band, 2.45 GHz to harvest RF energy. The rectenna has a rectifier and a patch antenna which is linear-polarized. The rectifier is a single-stage full-wave Greinacher rectifier where we added a radio frequency choke (RFC) to increase the output voltage. The output DC voltage of the rectenna can achieve 2.2 volts when the RF power is swept from −40 dBm to 0 dBm. It has excellent performance as pasted on the human body. The maximum output voltage of 2×2 array rectenna is about 1.05 volts as the power transferring distance is 150 cm in the indoor of WiFi environment.
Leach-Centric Load Sharing Between Cluster Nodes Based on Network Node Distribution

Wireless sensor networks consist of hundreds of nodes that are limited on the amount of energy, memory and computational capacity, which would lead to restrictions on replacing them in many situations. Therefore, when it comes to these networks, the communication protocol having the ability of minimizing the consumption of the mentioned items becomes of great importance. Among communication protocols for wireless sensor networks, leach protocol is prominent due to its specific characteristics of cluster formation. The random nature of the selection properties like cluster nodes with supervision features, adaptive nature of cluster head selection, its self-configuration property together with its non-interference when it comes to external factors play an essential role in network load sharing. This paper provides an improvement to leach protocol using a fair share of the load transfer between cluster head. For this purpose, the space containing the distributed sensors is divided into three areas according to the distance between the furthest sensor and the base station. Then the total number of cluster heads together with the number of cluster head for each area, are determined to demonstrate the achieved improvement.
A Pedestrian Detection Method Based on YOLOv3 Model and Image Enhanced by Retinex

Pedestrian detection is a basic technology in the field of intelligent traffic video surveillance. It is also help for the optimization design of rail transport. It is known that the deep learning technology can achieve considerable performance on pedestrian detection. However, this kind of methods demand a large number of high-quality samples. In addition, the quality of data sample in the subway station is usually sensitive to the background environment, such as variant illumination or pedestrian density, which can significantly affect performance of the deep neural network. To solve this problem, this paper adopts an image enhancement policy based on the Retinex theory to preprocess training samples to reduce the influence of light changes. Firstly, we use the image enhancement method to enhance the contrast of the image and highlight the color of the object itself. Next, we put the initial sample into darknet frame with YOLOv3 to train the detection model1 and put the enhanced sample into the YOLOv3 to train the detection model 2. Finally, we tested these two models with 200 pedestrian pictures of four different scenarios. The experimental results show that the model trained by Retinex image enhancement has a more accurate detection rate of 94% compared with the model without the enhancement sample trained.
An Efficient Framework for Online Malicious Domain Detection

Since the lack of sufficient security mechanisms, domain system (DNS) has become the main operational infrastructure for cyber intruders to launch cyber-attacks. Therefore, how to discover and block the potential malicious domains and its corresponding IP addresses fast and accurately has become a hot research area as it is one of the most important method in preventing unknown cyber-attacks. Based on deep investigation of the achievements in malicious domain detection fields, a novel framework OMDD (Online Malicious Domain Detection) was proposed which combines the advantages of three different malicious domain detecting approaches. According to the experimental results, OMDD performs a higher detection efficiency and accuracy than existing methods.
SafeHome App:

This paper describes an innovative product that aims to support caregivers of persons living with dementia (PLWD). SafeHome is a digital application that trains users to identify common safety hazards in the home; the game presents users with a furnished digital kitchen containing potential hazards, such as items that could cause electrocution, fire, slips, trips, and falls. Users must identify hazards and are guided through the game by a mascot who provides encouragement and explanations for correct and incorrect selections. A convenient sample of seven participants (caregivers) completed a Questionnaire for User Interaction Satisfaction, a reliable and valid tool for evaluating users' subjective satisfaction to app usability and for optimizing the app. A semi-structured interview was also conducted to generate user feedback. Themes that emerged from the data included relevance of knowledge, navigation, and continued needs. Recommended changes were captured and informed current modifications and future enhancements to the design elements of the app.
Optical Fiber Intrusion Signal Recognition Based on Improved Mel Frequency Cepstrum Coefficient

Oil and gas resources pipelines, boundary lines and other places need to monitor their safety status in real time. The fiber early warning system becomes a good choice for its high sensitivity, corrosion resistance and concealment. The system provides early warning of the detection of fiber vibration signals. In this paper, an improved Mel frequency cepstrum coefficient (MFCC) method is proposed for the cepstrum characteristics recognition of different typical optical fiber vibration signals. Firstly, we pre-process the intrusion signals and obtain its power spectral density (PSD) to quantify the difference of frequency spectrum in respective intrusions. Secondly, the adaptive filter bank is designed according to the distribution of signal power spectrum to improve the conventional MFCC method. Through the analysis of the characteristic parameters, the MFCC coefficients are obtained. Finally, the Mean-crossing rates (MCR) of MFCC are calculated and the appropriate thresholds are selected to classify the typical vibration signals. Compared with the traditional MFCC, this improved MFCC method realizes adaptive division of frequency band according to the distribution of signal power spectrum. Experiments show that the algorithm can identify the manual signal, the mechanical signal and the vehicle signal in the research of the vibration signal recognition of the optical fiber pre-warning system (OFPS).
Neural Embeddings for Metaphor Detection in a Corpus of Greek Texts

One of the major challenges that NLP faces is metaphor detection, especially by automatic means, a task that becomes even more difficult for languages lacking in linguistic resources and tools. Our purpose is the automatic differentiation between literal and metaphorical meaning in authentic non-annotated phrases from the Corpus of Greek Texts by means of computational methods of machine learning. For this purpose the theoretical background of distributional semantics is discussed and employed. Distributional Semantics Theory develops concepts and methods for the quantification and classification of semantic similarities displayed by linguistic elements in large amounts of linguistic data according to their distributional properties. In accordance with this model, the approach followed in the current work takes into account the linguistic context for the computation of the distributional representation of phrases in geometrical space, as well as for their comparison with the distributional representations of other phrases, whose function in speech is already “known” with the objective to reach conclusions about their literal or metaphorical function in the specific linguistic context. This procedure aims at dealing with the lack of linguistic resources for the Greek language, as the almost impossible up to now semantic comparison between “phrases”, takes the form of an arithmetical comparison of their distributional representations in geometrical space.
Tracing Geographical Origins of Teas Based on FT-NIR Spectroscopy: Introduction of Model Updating and Imbalanced Data Handling Approaches

This work presents a reliable approach to trace teas' geographical origins despite changes in teas caused by different harvest years. A total of 1447 tea samples collected from various areas in 2014 (660 samples) and 2015 (787 samples) were detected by FT-NIR. Seven classifiers trained on the 2014 dataset all succeeded to trace origins of samples collected in 2014; however, they all failed to predict origins for the 2015 samples due to different data distributions and imbalanced dataset. Three outlier detection based undersampling approaches—one-class SVM (OC-SVM), isolation forest and elliptic envelope—were then proposed; as a result, the highest macro average recall (MAR) for the 2015 dataset was improved from 56.86% to 73.95% (by SVM). A model updating approach was also applied, and the prediction MAR was significantly improved with increase in the updating rate. The best MAR (90.31%) was first achieved by the OC-SVM combined SVM classifier at a 50% rate.
Combining data from TCGA and GEO databases and reverse transcription quantitative PCR validation to identify gene prognostic markers in lung cancer

Background The aim of this study was to predict and explore the possible mechanism and clinical value of genetic markers in the development of lung cancer with a combined database to screen the prognostic genes of lung cancer. Materials and methods Common differential genes in two gene expression chips (GSE3268 and GSE10072 datasets) were investigated by collecting and calculating from Gene Expression Omnibus and The Cancer Genome Atlas databases using R language. Five markers of gene composition (ribonucleotide reductase regulatory subunit M2 [RRM2], trophoblast glycoprotein [TPBG], transmembrane protease serine 4[TMPRFF4], chloride intracellular channel 3 [CLIC3], and WNT inhibitory factor-1 [WIF1]) were found by the stepwise Cox regression function when we further screened combinations of gene models, which were more meaningful for prognosis. By analyzing the correlation between gene markers and clinicopathological parameters of lung cancer and its effect on prognosis, the TPBG gene was selected to analyze differential expression, its possible pathways and functions were predicted using gene set enrichment analysis (GSEA), and its protein interaction network was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database; then, quantitative PCR and the Oncomine database were used to verify the expression differences of TPBG in lung cancer cells and tissues. Results The expression levels of five genetic markers were correlated with survival prognosis, and the total survival time of the patients with high expression of the genetic markers was shorter than those with low expression (P<0.001). GSEA showed that these high-expression samples enriched the gene sets of cell adhesion, cytokine receptor interaction pathway, extracellular matrix receptor pathway, adhesion pathway, skeleton protein regulation, cancer pathway and TGF-β pathway. Conclusion The high expression of five gene constituent markers is a poor prognostic factor in lung cancer and may serve as an effective biomarker for predicting metastasis and prognosis of patients with lung cancer.
ATF4 contributes to autophagy and survival in sunitinib treated brain tumor initiating cells (BTICs)

Receptor tyrosine kinase (RTK) pathways are known to play an important role in tumor cell proliferation of glioblastoma (GBM). Cellular determinants of RTK-inhibitor sensitivity are important to optimize and tailor treatment strategies. The stress response gene activating transcription factor 4 (ATF4) is involved in homeostasis and cellular protection. However, little is known about its function in GBM. We found that the ATF4/p-eIF2α pathway is activated in response to Sunitinib in primary tumor initiating progenitor cell cultures (BTICs). Furthermore, lysosome entrapment of RTK-inhibitors (RTK-Is) leads to accumulation of autophagosomes. In case of Sunitinib treated cells, autophagy is additionally increased by ATF4 mediated upregulation of autophagy genes. Inhibition of ATF4 by small interfering RNA (siRNA) reduced autophagy and cell proliferation after Sunitinib treatment in a subset of BTIC cultures. Overall, this study suggests a pro-survival role of the ATF4/p-eIF2α pathway in a cell type and treatment specific manner.
Cell cycle regulation of condensin Smc4

The condensin complex is a conserved ATPase which promotes the compaction of chromatin during mitosis in eukaryotic cells. Condensin complexes have in addition been reported to contribute to interphase processes including sister chromatid cohesion. It is not understood how condensins specifically become competent to facilitate chromosome condensation in preparation for chromosome segregation in anaphase. Here we describe evidence that core condensin subunits are regulated at the level of protein stability in budding yeast. In particular, Smc2 and Smc4 abundance is cell cycle regulated, peaking at mitosis and falling to low levels in interphase. Smc4 degradation at the end of mitosis is dependent on the Anaphase Promoting Complex/Cyclosome and is mediated by the proteasome. Overproduction of Smc4 results in delayed decondensation, but has a limited ability to promote premature condensation in interphase. Unexpectedly, the Mad2 spindle checkpoint protein is required for mitotic Smc4 degradation. These studies have revealed the novel finding that condensin protein levels are cell cycle regulated and have identified the factors necessary for Smc4 proteolysis.
Advances in Identification of Susceptibility Gene Defects of Hereditary Colorectal Cancer

Colorectal cancer (CRC) is a common malignant tumor of the digestive system worldwide, associated with hereditary genetic features. CRC with a Mendelian genetic predisposition accounts for approximately 5-10% of total CRC cases, mainly caused by a single germline mutation of a CRC susceptibility gene. The main subtypes of hereditary CRC are hereditary non-polyposis colorectal cancer (HNPCC) and familial adenomatous polyposis (FAP). With the rapid development of genetic testing methods, especially next-generation sequencing technology, multiple genes have now been confirmed to be pathogenic, including DNA repair or DNA mismatch repair genes such as APC, MLH1, and MSH2. Since familial CRC patients have poor clinical outcomes, timely clinical diagnosis and mutation screening of susceptibility genes will aid clinicians in establishing appropriate risk assessment and treatment interventions at a personal level. Here, we systematically summarize the susceptibility genes identified to date and the potential pathogenic mechanism of HNPCC and FAP development. Moreover, clinical recommendations for susceptibility gene screening, diagnosis, and treatment of HNPCC and FAP are discussed.
Recent Advances in Hybrid Brain-Computer Interface Systems: A Technological and Quantitative Review

Brain-Computer Interface (BCI) is a system that enables users to transmit commands to the computer using their brain activity recorded by electroencephalography. In a Hybrid Brain-Computer Interface (HBCI), a BCI control signal combines with one or more BCI control signals or with Human-Machine Interface (HMI) biosignals to increase classification accuracy, boost system speed, and improve user’s satisfaction. HBCI systems are categorized according to the type of combined signals and the combination technique (simultaneous or sequential). They have been used in several applications such as cursor control, target selection, and spellers. Increasing the number of articles published in this field indicates the significance of these systems. In this paper, different HBCI combinations, their important features, and potential applications are discussed. In most cases, the combination of a BCI control signal with a HMI biosignal yields higher information transfer rate than two BCI control signals.