Miracle-dz/UniFAR-FTUs huggingface dataset:
A Deep Neural Network-Based Indoor Positioning Method using Channel State Information

The channel state information (CSI) measurement, which characterizes the multipath channel between the transmitter and the receiver, can serve as a fingerprint for the receiver position. Recently, a series of deep learning-based indoor positioning fingerprinting (FP) methods using CSI have been proposed to enhance the localization performance. In this paper, we present a deep neural network (DNN)-based indoor positioning FP system using CSI, which is termed DNNFi. The proposed DNNFi, which maintains a single DNN instead of multiple deep autoencoders at different reference points, allows a faster computation for the online inference and a lower memory usage for the weights/biases. A stack of autoencoders is utilized to pre-train the weights layer-by-layer. The softmax function is utilized to decide the probabilities of the receiver position being on these reference points, which can be used to estimate the receiver position. Experimental results are presented to confirm that DNNFi can effectively reduce location error compared with the conventional CSI positioning FP approaches.
Comparing Wealth Polarization Over Time and Across Countries in Africa

In this paper we compare wealth polarization within and across ten African countries. Using DHS data and applying the recently developed polarization measure by Duclos et al (2004) we find that most countries included in this study experienced a sharp decrease of polarization but at different rates. Compared to inequality, polarization behaves differently suggesting the different sensitivities of both notions to changes in different parts of the distribution of wealth. Our decomposition of polarization by ethnic, religious and wealth groups shows that: In general, the relative contribution of the poorest population to the overall polarization is greater than their relative size. In all countries the relative contribution of each ethnic or religious group to the overall polarization is very close to its relative size.
RNA sequencing, de novo assembly and differential analysis of the gill transcriptome of freshwater climbing perch Anabas testudineus after 6 days of seawater exposure.

To obtain transcriptomic insights into branchial responses to salinity challenge in Anabas testudineus, this study employed RNA sequencing (RNA-Seq) to analyse the gill transcriptome of A. testudineus exposed to seawater (SW) for 6 days compared with the freshwater (FW) control group. A combined FW and SW gill transcriptome was de novo assembled from 169.9 million 101 bp paired-end reads. In silico validation employing 17 A. testudineus Sanger full-length coding sequences showed that 15/17 of them had greater than 80% of their sequences aligned to the de novo assembled contigs where 5/17 had their full-length (100%) aligned and 9/17 had greater than 90% of their sequences aligned. The combined FW and SW gill transcriptome was mapped to 13,780 unique human identifiers at E-value ≤1.0E-20 while 952 and 886 identifiers were determined as up and down-regulated by 1.5 fold, respectively, in the gills of A. testudineus in SW when compared with FW. These genes were found to be associated with at least 23 biological processes. A larger proportion of genes encoding enzymes and transporters associated with molecular transport, energy production, metabolisms were up-regulated, while a larger proportion of genes encoding transmembrane receptors, G-protein coupled receptors, kinases and transcription regulators associated with cell cycle, growth, development, signalling, morphology and gene expression were relatively lower in the gills of A. testudineus in SW when compared with FW. High correlation (R = 0.99) was observed between RNA-Seq data and real-time quantitative PCR validation for 13 selected genes. The transcriptomic sequence information will facilitate development of molecular resources and tools while the findings will provide insights for future studies into branchial iono-osmoregulation and related cellular processes in A. testudineus.
An improved pilot routing algorithm for compressed sensing-based channel estimation in underwater acoustic OFDM system

Compressed sensing (CS) is used to estimate channel information in underwater acoustic OFDM system recently. However, there are no recognition criteria to specify the pilot patterns in channel estimation. In order to improve the accuracy of the estimation, this paper proposes a pilot routing algorithm based on minimizing the adjusted mutual coherence of the measurement matrix in the recovery algorithm of compressed sensing theory. Each pilot symbol location is determined by calculating the adjusted mutual coherence of the corresponding measurement matrix. The matrix is produced by fixing other pilot location indexes and replacing the index of the current pilot location with all optional indexes. The simulation results show that the BER is 2dB and 8dB lower when SNR is 5dB and 30dB respectively, and the MSE is 2dB lower compared with the results of algorithm 1. Comparing with the results of algorithm 2, the BER is 2dB better, and the MSE is 3dB lower when SNR is 30dB. The data confirms the efficiency of this pilot routing algorithm.
Deep Learning Localizes and Identifies Polyps in Real Time With 96% Accuracy in Screening Colonoscopy.

BACKGROUND & AIMS The benefit of colonoscopy for colorectal cancer prevention depends on the adenoma detection rate (ADR). The ADR should reflect the adenoma prevalence rate, which is estimated to be higher than 50% in the screening-age population. However, the ADR by colonoscopists varies from 7% to 53%. It is estimated that every 1% increase in ADR lowers the risk of interval colorectal cancers by 3%-6%. New strategies are needed to increase the ADR during colonoscopy. We tested the ability of computer-assisted image analysis using convolutional neural networks (CNNs; a deep learning model for image analysis) to improve polyp detection, a surrogate of ADR. METHODS We designed and trained deep CNNs to detect polyps using a diverse and representative set of 8,641 hand-labeled images from screening colonoscopies collected from more than 2000 patients. We tested the models on 20 colonoscopy videos with a total duration of 5 hours. Expert colonoscopists were asked to identify all polyps in 9 de-identified colonoscopy videos, which were selected from archived video studies, with or without benefit of the CNN overlay. Their findings were compared with those of the CNN using CNN-assisted expert review as the reference. RESULTS When tested on manually labeled images, the CNN identified polyps with an area under the receiver operating characteristic curve of 0.991 and an accuracy of 96.4%. In the analysis of colonoscopy videos in which 28 polyps were removed, 4 expert reviewers identified 8 additional polyps without CNN assistance that had not been removed and identified an additional 17 polyps with CNN assistance (45 in total). All polyps removed and identified by expert review were detected by the CNN. The CNN had a false-positive rate of 7%. CONCLUSION In a set of 8,641 colonoscopy images containing 4,088 unique polyps, the CNN identified polyps with a cross-validation accuracy of 96.4% and an area under the receiver operating characteristic curve of 0.991. The CNN system detected and localized polyps well within real-time constraints using an ordinary desktop machine with a contemporary graphics processing unit. This system could increase the ADR and decrease interval colorectal cancers but requires validation in large multicenter trials.
Interactive Energy-Saving Dispatch Considering Generation and Demand Side Uncertainties: A Chinese Study

The energy-saving dispatch is becoming one of the most important smart grid components in China, which asks for both generation and demand side resources’ participation. In smart electricity grid, the uncertainties related with variable renewable energy outputs on the generation side, as well as the deviations between the scheduled demand response (DR) resources and their actual availabilities on the demand side should be simultaneously taken care of. In this paper, a new energy-saving dispatch problem is studied considering the uncertainties from both generation and demand sides. A bi-level optimization model is established to address the generation and demand side’s interactions considering economic dispatch of thermal units and load control schemes of end users in the new energy-saving dispatch problem. The upper-level minimizes the electric transmission company’s power generation and carbon emission costs while maintaining the system reliability, and the lower-level minimizes the expected total load dispatch cost paid by the electric distribution company to DR providers and the demand side emission cost. An iterative algorithm is adopted to solve the proposed bi-level model. In addition, improved NSGA-II method is used to solve the lower-level multi-objective optimization problem for seeking optimal compromise solution. Numerical case studies on the practical energy-saving dispatch problem of Guangdong Province in China illustrate the effectiveness of the proposed approach.
Digital signature with multiple signatories based on modified ElGamal Cryptosystem

El Gamal Digital Signature typically works between one signatory and one verifier. This scheme will not work in situations where there are two (2) or more signatories. This paper proposes a variant of the El Gamal Digital Signature Scheme that allows multiple signatories in a single signature by modifying the ElGamal Cryptosystem. The modification allowed multiple senders to encrypt a plaintext message into a ciphertext using multiple private keys. The provision also provided a scheme for a digital signature that allowed multiple signatories be tendered in a document in a single signature.
A survey on 5G mobile technology

The major objective of this paper is to make a comprehensive survey on 5G Mobile Technology. The upcoming trend research work is on 5G. In this Fifth Generation the researches ultimately focus on the development of World Wide Wireless Web (WWWW), Dynamic Adhoc Wireless Network (DAWN) and Real Wireless Communication. The recent comprehensive analysis highlights salient features such as flexibility, accessibility, higher bandwidth, Spectrum Efficiency, and Quality of Service (QoS). These salient features make 5G technology as the most dominant technology in the forthcoming trends.
Tree based distributed clustering routing scheme for energy efficiency in wireless sensor networks

: The energy efficiency the most important and vital performance metrics of wireless sensor networks (WSNs) as the lifetime of battery is limited. Energy efficiency helps in deciding the long running WSNs functionality. There are different solutions have been presented for energy efficiency based on hardware and software. However, particularly clustering based routing algorithms are most promising approaches and hence studied extensively for energy efficiency and load balancing. The existing clustering methods having drawbacks like cluster heads suffered from excessive energy consumption due to all loads on it, therefore this can be later overcome by placing the gateways in WSNs which can acts cluster head with more energy sources in it. However with use only gateways with clustering again imposing limitation of energy consumption as gateways are also battery operated as well as load balancing is not addressed. In this paper, aim is to design and simulate novel tree based distributed for energy efficiency and data aggregation in WSNs. The proposed method is composed of three main steps such as hop tree building from source node to the destination sensor node, then cluster head selection and cluster formation. Finally the efficient and reliable route setup for data transmission is presented. The working of proposed algorithm is based on existing SPT (shortest path tree) routing method. The experimental evaluation of proposed method is done using NS2. The simulation results claims that proposed method is outperforming the existing energy efficient routing solutions.
Exploiting the Genomic Diversity of Rice (Oryza sativa L.): SNP-Typing in 11 Early-Backcross Introgression-Breeding Populations

This study demonstrates genotyping-by-sequencing-based single-nucleotide polymorphism (SNP)-typing in 11 early-backcross introgression populations of rice (at BC1F5), comprising a set of 564 diverse introgression lines and 12 parents. Sequencing using 10 Ion Proton runs generated a total of ∼943.4 million raw reads, out of which ∼881.6 million reads remained after trimming for low-quality bases. After alignment, 794,297 polymorphic SNPs were identified, and filtering resulted in LMD50 SNPs (low missing data, with each SNP, genotyped in at least 50% of the samples) for each sub-population. Every data point was supported by actual sequencing data without any imputation, eliminating imputation-induced errors in SNP calling. Genotyping substantiated the impacts of novel breeding strategy revealing: (a) the donor introgression patterns in ILs were characteristic with variable introgression frequency in different genomic regions, attributed mainly to stringent selection under abiotic stress and (b) considerably lower heterozygosity was observed in ILs. Functional annotation revealed 426 non-synonymous deleterious SNPs present in 102 loci with a range of 1–4 SNPs per locus and 120 novel SNPs. SNP-typing this diversity panel will further assist in the development of markers supporting genomic applications in molecular breeding programs.
Real-Time Anomaly Detection in Elderly Behavior with the Support of Task Models

With today's technology, elderly can be supported in living independently in their own homes for a prolonged period of time. Monitoring and analyzing their behavior in order to find possible unusual situation helps to provide the elderly with health warnings at the proper time. Current studies are focusing on the elderly daily activity and the detection of anomalous behaviors aiming to provide the older people with remote support. To this aim, we propose a real-time solution which models the user daily routine using a task model specification and detects relevant contextual events occurred in their life through a context manager. In addition, by a systematic validation through a system that automatically generates wrong sequences of tasks, we show that our algorithm is able to find behavioral deviations from the expected behavior at different times by considering the extended classification of the possible deviations with good accuracy.
Performance evaluation of class balancing techniques for credit card fraud detection

The number of online transactions has unraveled in large proportions with each passing day. Credit card transactions constitute a huge portion of these transactions. The financial losses have also increased analogously along with the credit card fraud transactions. Therefore, fraud detection systems have acquired great importance for banks and financial institutions. As the occurrence of fraud is unlikely in comparison to normally occurring transactions, we are posed with the class imbalance problem and to handle this imbalance problem we use resampling techniques in this paper. We applied oversampling (SMOTE, SMOTE ENN, SAFE SMOTE, ROS, SMOTE TL). On the resampled data, we applied cost sensitive (CSVM, C4.5) and ensemble classifier (Adaboost, Bagging) to evaluate the performances using sensitivity, specificity, G-mean, Area under ROC. We observed that the SMOTE ENN method detects the fraud in a better way than other classifiers in the set of oversampling techniques considered, and TL works better on the set of undersampling techniques taken.
Submm/FIR CO line emission from the disk of the Class I protostar EL 29

We present observations towards the Class I protostar EL 29 of the CO J= 6! 5a ndJ 15 transitions obtained with JCMT and ISO LWS respectively, and of five H2 rotational lines obtained with ISO SWS. The simultaneous analysis of these observations reveals the presence of a warm gas component at about 170-250 K,550 AU in size, and whose density is 10 6 cm 3 . The mass of the warm gas is8 2410 4 M. These values compare extremely well with the predictions of the temperature and mass of the flaring disk surrounding EL 29, probed by the dust continuum. We propose that the observed FIR CO emission originates in the super-heated surface layer of the disk of EL 29 and discuss its characteristics. We find that the CO abundance in the disk is large,10 4 , implying no depletion or photodissociation and we present arguments in favor of the idea that the dust has settled on the midplane disk of EL 29 and that it is thermally decoupled from the gas. We compare the characteristics of the El 29 disk with those of the disks observed in other Herbig AeBe stars using recent studies of H2 rotational line emission. The gas temperature and mass derived for the disk of EL 29 are similar to the disks of the previously studied sample. In EL 29, as in Herbig AeBe stars, the gas and dust are probably thermally decoupled. Finally, the upper limit on the H2O emission that we obtain suggests that water is not an important coolant of the disk gas, in agreement with theoretical water abundance predictions. The present study challenges previous claims that the FIR CO line emission observed in sources similar to EL 29 originates in shocks.
Hybrid adaptive neuro-fuzzy B-spline--based SSSC damping control paradigm using online system identification

B-spline membership functions have produced promising results in the field of signal processing and control due to their local control property. This work explores the potential of B-spline--based adaptive neuro-fuzzy wavelet control to damp low frequency power system oscillations using a static synchronous series compensator (SSSC). A comparison of direct and indirect adaptive control based on hybrid adaptive B-spline wavelet control (ABSWC) is presented by introducing the online identification block. ABSWC with identification (ABSWCI) provides the sensitivity information of the plant needed to control the system. The parameters of the control and identification block are updated online using a gradient descent-based backpropagation algorithm. The stability and convergence of the proposed control system is discussed based on Lyapunov stability criteria. The robustness of the proposed control algorithm has been evaluated for local and interarea modes of oscillations using different faults. The nonlinear time-domain simulations have been analyzed on the basis of different performance indices and time-frequency representation, showing that ABSWC effectively damps low-frequency oscillations and incorporation of online identification optimizes the control system performance in terms of control effort, which reduces the switching losses of the converter.
Glypican-3-targeted precision diagnosis of hepatocellular carcinoma on clinical sections with a supramolecular 2D imaging probe

The ability of chemical tools to effectively detect malignancy in frozen sections removed from patients during surgery is important for the timely determination of the subsequent surgical program. However, current clinical methods for tissue imaging rely on dye-based staining or antibody-based techniques, which are sluggish and complicated. Methods: Here, we have developed a 2D material-based supramolecular imaging probe for the simple, rapid yet precise diagnosis of hepatocellular carcinoma (HCC). The 2D probe is constructed through supramolecular self-assembly between a water soluble, fluorescent peptide ligand that selectively targets glypican-3 (GPC-3, a specific cell-surface biomarker for HCC) and 2D molybdenum disulfide that acts as a fluorescence quencher as well as imaging enhancer. Results: We show that the 2D imaging probe developed with minimal background fluorescence can sensitively and selectively image cells overexpressing GPC-3 over a range of control cells expressing other membrane proteins. Importantly, we demonstrate that the 2D probe is capable of rapidly (signal became readable within 1 min) imaging HCC tissues over para-carcinoma regions in frozen sections derived from HCC patients; the results are in accordance with those obtained using traditional clinical staining methods. Conclusion: Compared to conventional staining methods, which are laborious (e.g., over 30 min is needed for antibody-based immunosorbent assays) and complex (e.g., diagnosis is based on discrimination of the nucleus morphology of cancer cells from that of normal cells), our probe, with its simplicity and quickness, might become a promising candidate for tumor-section staining as well as fluorescence imaging-guided surgery.
Bayesian optimization for parameter identification on a small simulation budget

Bayesian optimization uses a probabilistic model of the objective function to guide the search for the optimum. It is particularly interesting for the optimization of expensive-to-evaluate functions. For the last decade, it has been increasingly used for industrial optimization problems and especially for numerical design involving complex computer simulations. We feel that Bayesian optimization should be considered with attention by anyone who has to identify the parameters of a model based on a very limited number of model simulations because of model complexity. In this paper, we wish to describe, as simply as possible, how Bayesian optimization can be used in parameter identification and to present a new application. We concentrate on two algorithms, namely EGO (for Efficient Global Optimization) and IAGO (for Informational Approach to Global Optimization), and describe how they can be used for parameter identification when the budget for evaluating the cost function is severely limited. Some open questions that must be addressed for theoretical and practical reasons are indicated.
Content matching problem in large scale network using weighted b-matching algorithm

This paper highlights the solution of a content matching problem in large-scale social network, where the matching is to be done between a set of producers of information and the associated consumers. In the information dissemination process, the objective is to provide suitable information from the provider to the right kind of consumers. In order to have higher or maximized relevance of the matched content, a good number of algorithms have been proposed. This matching task can be further extended based on the solution of the weighted b-matching problem of graph theory. Hence an algorithm, named the b-suitor algorithm has been proposed in this direction and different case studies have been considered to verify the accuracy of the algorithm when the data size is large. Further, a comparative analysis has been presented based on the performance of this algorithm versus various other algorithms as available in the literature.
Distribution Locational Marginal Pricing (DLMP) for Congestion Management and Voltage Support

In this paper, a day-ahead market-clearing model for smart distribution systems is proposed. Various types of distributed energy resources (DERs), such as distributed energy storage, distributed generators, microgrids, and load aggregators, can bid into the day-ahead distribution-level electricity market. Considering system Volt/VAR control, network reconfiguration, and interactions with the wholesale market, an optimization model is built to clear the day-ahead market, through which the distribution locational marginal pricing (DLMPs) for both active power and reactive power are determined. Through derivations of the Lagrangian function and sensitivity factors, DLMPs are decomposed to five components (i.e., marginal costs for active power, reactive power, congestion, voltage support, and loss), which provide price signals to motivate DERs to contribute to congestion management and voltage support. Finally, case studies demonstrate the effectiveness of the proposed method.