Miracle-dz/UniFAR-FTUs huggingface dataset:
A Digital Signage Audience Classification Model Based on the Huff Model and Backpropagation Neural Network

Digital signage is an important outdoor advertising medium in cities. However, advertising on digital signage often lacks pertinence. Thus, it is important to introduce an accurate digital signage audience classification method to facilitate targeted advertising. In this study, a multi-label classification model based on a backpropagation (BP) neural network and the Huff model, referred to as the Huff-BP model, is proposed to investigate digital signage audience classification. A case study is performed on outdoor digital signage within the 6th Ring Road in Beijing, China, and economic census, population census, average housing price, social media check-in and the centrality of traffic networks as research data. The data are divided into $100\times100$ -1, $000\times1$ ,000 m normal grids. Multi-label classification modelling factors for various grid scales are constructed. The BP neural network classification algorithm is improved to solve the multi-label classification problem. In addition, an improved Huff model is used to calculate the digital signage influence values between each grid cell and integrated into the improved BP neural network to classify modelling factors at various scales. Finally, four metrics are used to examine the effectiveness of the proposed model. The results show that the Huff-BP-based multi-label classification model achieves relatively good classification results, and the digital signage audiences are mainly concentrated within the 4th Ring Road and near the 5th Ring Road.
The Use of Various Input Data in the Design of Public Transport Route Networks

The article describes the features and stages of the formulation and solution of the problem of building an effective route network in a large city. The article describes the necessary source data to solve this problem. An algorithm is proposed for the formation of the initial set of routes for the public transport network based on the use of intermediate information on the functioning modes of the entire transport system of the city. Further steps to improve the initial set of routes using genetic algorithms are described.
Dynamic RNN -CNN based Malware Classifier for Deep Learning Algorithm

This study proposes a malware classification model that can handle arbitrary length input data using the Microsoft Malware Classification Challenge dataset. We are based on imaging existing data from malware. The proposed model generates a lot of images when malware data is large, and generates a small image of small data. The generated image is learned as time series data by Dynamic RNN. The output value of the RNN is classified into malware by using only the highest weighted output by applying the Attention technique, and learning the RNN output value by Residual CNN again. Experiments on the proposed model showed a Micro-average F1 score of 92% in the validation data set. Experimental results show that the performance of a model capable of learning and classifying arbitrary length data can be verified without special feature extraction and dimension reduction.
The Application Discussion of Third-Order Volterra System Identification for Separating Sub- and Ultraharmonics of Nonlinear Ultrasound Signal

The purpose of this paper is to discuss the third- order Volterra model kernels between linear-cubic and cubic for separating sub-and ultraharmonics (SUH) of nonlinear ultrasound signal. The performance of two filters by contrast- to-tissue ratio (CTR) is compared to discuss the use for creating images. Comparison of simulation results have shown that two methods are successful in separating SUH. Both filters can improve CTR values up to 189.95 dB compared with -0.28 dB original signal (B-mode imaging) before filtering.
Population abundance and habitat utilization of bottlenose dolphins in the Mississippi Sound

Distance sampling principles were utilized to examine population density and abundance for bottlenose dolphins (Tursiops truncatus) in the Mississippi Sound. Information was collected during summer and winter to allow for examination of habitat utilization and abundance during two different seasons. Within the study area of the Mississippi Sound there are approximately 2225 bottlenose dolphins. The population was larger during the summer than during the winter months. Dolphins utilized coastal areas more during the summer, potentially as nursery grounds, as evidenced by larger numbers of calves and percentage of groups containing calves during this time. Bottlenose dolphin densities were lower in this area during the winter suggesting migration to deeper waters potentially in search of prey. As the Mississippi Sound is regularly utilized for a variety of human activities, the monitoring of dolphin populations in this area is critical to determine increasing or decreasing trends in population abundance due to potential anthropogenic factors that may affect animal populations. Similar studies around the world could provide evidence for areas that could be protected to ensure survival of species such as dolphins and other marine mammals. Copyright © 2012 John Wiley & Sons, Ltd.
FDTLS: Supporting DTLS-Based Combined Storage and Communication Security for IoT Devices

This work presents FDTLS, a security framework that combines storage and network/communication-level security on resource limited Internet of Things (IoT) devices using Datagram Transport Layer Security (DTLS). While coalescing the storage and networking security schemes can reduce redundant and unnecessary cryptographic operations, we identify security-and system-level challenges that can occur when applying DTLS towards such concept. FDTLS addresses these challenges by employing an asymmetric key generation scheme, a virtual peer-based handshaking mechanism, and a header size reduction scheme. Our results obtained using Contiki-based implementations on OpenMote devices show that compared to using storage and networking security separately, FDTLS can reduce the network response latency and improve energy savings.
Detecting IRC-based Botnets by Network Traffic Analysis Through Machine Learning

Cybersecurity becomes increasingly important as information and communications technology (ICT) is adopted throughout the world. Cyber attacks can happen both externally and internally. With majority of cyber attacks being executed by insiders, it is important to detect insider attacks and even prevent them. This paper studies how to apply machine learning in analyzing network traffic to detect insider attacks, particularly in the area of botnet detection. Different from existing work considered two types of Hyper Text Transfer Protocol (HTTP)-based botnets, we consider two types of popular Internet Relay Chat (IRC)-based botnets. With selected flow characteristics, experimental results show that the proposed detection model can achieve a true positive rate of over 96%, with a false positive rate of less than 5%.
Achievable Spectral Efficiency of Hybrid Beamforming Massive MIMO Systems With Quantized Phase Shifters, Channel Non-Reciprocity and Estimation Errors

This paper considers a hybrid beamforming massive multiple-input multiple-output (MIMO) system operating in time-division duplex (TDD) mode. We investigate the combined impacts of quantized phase shifters (QPSs), channel non-reciprocity and channel estimation errors on the achievable spectral efficiency (SE) of the considered system. We first introduce an equivalent hybrid beamforming structure, based on which, a generic and practical channel non-reciprocity model is built for massive MIMO systems with hybrid structure. This model jointly considers the mismatch between transceiver and receiver circuits, as well as the mutual coupling between antennas. Then we derive the generic expression of achievable SE for both the ideal phase shifters (IPSs) and QPSs cases. Furthermore, the closed-form expressions of SE are derived for IPSs and QPSs cases, respectively. The asymptotic performance in large antenna regime is also analyzed based on these expressions. Simulation results demonstrate the validity and accuracy of the derived analytical expressions. In addition, results show that the phase quantization and channel non-reciprocity both contributes to the SE degradation and the detrimental effects cannot be canceled completely even in the large-antenna regime.
Multiplexing Gains Through Clustering In Cloud Radio Access Network

There is an increase in the energy consumption and cost of base station deployment due to increase in the number of devices which require a dense deployment of base stations to handle the resultant data traffic. C-RAN (Cloud Radio Access Network) was proposed to remedy the problem of increasing data traffic volume and reduce the capital expenditure and operating expenditure of Mobile Network operators. Cloud Radio Access Network (C-RAN) allows for network resources to be shared amongst several base stations thereby reducing cost. By using different clustering algorithms such as K-means, Hierarchical and Gaussian Mixture Models to cluster these base stations there is a reduction in the needed network resources and this reduces cost. Capacity Utility and cost of deployment are the metrics used in making a comparative analysis of the different clustering algorithms used in this work. From evaluation of the methodology, it showed that the Hierarchical clustering algorithm had a Capacity Utility of 0.0012, Gaussian Mixture Models had 0.0035 and K-means with 0.0044 and when you compare this with the Capacity Utility before clustering of 0.63 it can be seen that the Hierarchical clustering algorithm had reduced the needed network resources better than Gaussian Mixture Models and K-means. The 3 clustering algorithms were also able to reduce the number of needed base stations from 182 to 80, thereby reducing Cost of deployment.
Development of a Piezoresistive Sitting Pressure Distribution Sensor: Experiments on Standard Shape Objects

Sitting distribution pressure is a useful ergonomic parameter for the study of sitting behavior. This paper presents the development of a piezoresistive pressure distribution sensor for sitting analysis using a matrix configuration of electrodes for the measurement. The sensor consisted of 15x15 sensing points and was implemented using the piezoresistive property to convert the compressive force during sitting into varying resistance readings, before further calibration into pressure distribution image. The resulting image was subsequently interpolated to obtain the best possible image resolution. In the project, standard shapes were used to investigate the functions that provide the most appropriate image, which can be applied in interpolating the shape of a human’s buttocks that facilitates sitting posture analysis.
Unambiguous Reconstruction for Multichannel Nonuniform Sampling SAR Signal Based on Image Fusion

Multichannel signal processing in azimuth is a vital technique to enable a wide-swath Synthetic Aperture Radar (SAR) with high azimuth resolution. However, the multichannel high-resolution and wide-swath (HRWS) SAR system always suffers from the problem of the azimuth nonuniform sampling resulting in the image ambiguity, when it does not satisfy the uniform sampling condition. In this paper, to suppress the azimuth image ambiguity, we propose a novel unambiguous reconstruction method based on image fusion. During this reconstruction processing, the Back Projection (BP) algorithm is first utilized for SAR imaging to obtain the designed sub-images. Then, the reconstruction expression is derived as the summation of the sub-images weighted by the interpolation coefficient. This method integrates the reconstruction into the imaging process and the image fusion makes the procedure simple. In addition, the interpolation period, which affects the reconstruction image quality and efficiency, is further analyzed. Moreover, as the curved trajectory platform brings more challenges for the unambiguous reconstruction, the performance of the proposed method applied to the curved trajectory platform is studied. Finally, experimental results clearly verify the effectiveness of the proposed method for ambiguity suppression and demonstrate its applicability to the curved trajectory.
Interference Mitigation in SAR via Gapped Spectrum Waveform Design

In the increasingly crowded RF spectrum synthetic aperture radar (SAR), a wide bandwidth mode, is particularly vulnerable to external RF interference which can significantly degrade the quality of the resulting SAR image. Adaptive waveform design is an emerging field in radar which has the potential to facilitate tailored radar operation for the surrounding environment. This work summarizes an adaptive waveform design scheme for interference mitigation for SAR by considering the optimal placement of energy across the bandwidth. The scheme competes with surrounding interference where its power is sufficiently low and introduces gaps in the waveform spectrum where the RF interference is problematic. Placing gaps in the spectrum reduces image quality by increasing sidelobes, which may mask weaker reflectors and reduce image quality, so should be avoided unless deemed necessary. A cost function is built to minimize the measurement mean square error by separating the transmit waveform into a spectral region to either place signal energy or avoid. The waveform design process is computationally efficient and performed on-the-fly on a pulse-to-pulse basis. Offline, an attempt is made to recover missing phase history by using well-known spectral interpolation algorithms for SAR phase history recovery (GAPES and AR-Burg). Simulated SAR images demonstrate the efficacy of these techniques.
Crop Analysis and Seed Marketing using Regression and Association Rules of India

The agriculture plays a crucial and vital role in the country’s Economy. A major population of any country across the globe depends on agriculture for survival. Many times resources are wasted when the farmers are not able to correctly predict the crop to be grown due to lack of information. We have tried to build a model that predicts the type of crop that can be cultivated in a given land on the basis of climatic conditions. Therefore, the crop with maximum production probability for a given area of land can be cultivated and thus in this way, the resources and hard work of the farmers would be optimized and will give better results. This model would also help in effective seed marketing as the predictive analysis of the suitable crop would help the suppliers supply the suitable crop seed in enough quantity to the farmers which would benefit the suppliers as well as the farmers.
Study on Propagation Characteristics of Partial Discharge Electromagnetic Wave in Gas Insulated Switchgear

The ultra-high frequency(UHF) method is the most commonly used method for on-site partial discharge(PD) detection in gas insulated switchgear(GIS). The primary premise of this method is to know the transmission of electromagnetic wave in GIS. According to the actual structure of 1100kV GIS, a cylindrical calculation model with a total length of 5m is established by using finite element simulation software. Considering the advantages and disadvantages of various discharge sources, the electric point dipole model is used as the internal discharge source of GIS. In the simulation, the Gauss pulse current with pulse width of 0.7ns and amplitude of 15mA is used as the excitation of the discharge source. The distribution and transmission of electromagnetic waves at different locations are studied, and the influence of the amplitude and pulse width of discharge source on electromagnetic wave signal is discussed. The results show that with the increase of time, the electric field norm of the detection point decreases slowly in the fluctuation, and the change of the electric field norm has a certain time lag; the smaller the pulse width of the excitation source, the higher the amplitude of the electric field norm; the larger the pulse width, the slower the attenuation of the electromagnetic wave.
The Sustainable Parcel Delivery (SPD) Problem: Economic and Environmental Considerations for 3PLs

Sustainability in parcel delivery is a growing area of interest, especially for third-party logistics providers (3PLs). The recent increase of urban population is directly related to the increase request of goods in urban areas, and consequently to the growth of the urban freight transport and CO2 emissions. For these reasons, national and local institutions carried out regulations and incentives to reduce urban pollution and promote zero-emission vehicles. In particular, daily tickets to access to city centers is a common regulation applied to reduce freight transport. This paper presents a new SPD model that compares Eclectic Vehicles (EVs) and Fossil Fuel Vehicles (FFVs) considering economic savings and CO2 emissions, for parcel delivery from a single distribution center to a set of delivery point located inside and/or outside an urban area. Limitations as the daily ticket, the fuel cost, the battery duration are considered to provide 3PLs an innovative model to evaluate both the economic convenience and the environmental impact of its vehicles fleet. Through an explanatory study, economic considerations are carried out, related to the length of the route, the daily ticket cost, and the fuel cost to evaluate and to assess the different transportation options. It demonstrates that EVs are more convenient in terms of economic savings when the route (urban distances) and the daily ticket cost increase.
Assessing corrosion resistance of two-dimensional nanomaterial-based coatings on stainless steel substrates

Two-dimensional (2D) materials have elicited considerable interest in the past decade due to a diverse array of novel properties ranging from high surface to mass ratios, a wide range of band gaps (insulating boron nitride (BN) to semiconducting transition metal dichalcogenides), high mechanical strength and chemical stability. Given the superior chemo-thermo-mechanical properties, 2D materials may provide transformative solution to a familiar yet persistent problem of significant socio-economic burden: the corrosion of stainless steel (SS). With this broader perspective, we investigate corrosion resistance properties of SS-coated with 2D nanomaterials; molybdenum disulfide (MoS2), BN, bulk graphite in 3.5 wt% aqueous NaCl solution. The nanosheets were prepared by a novel liquid phase exfoliation technique and the coatings were made by a paint brush to achieve uniformity. Open circuit potential (OCP) and potentiodynamic plots indicate the best corrosion resistance is provided by the MoS2 coatings. Superior performance of the coating is attributed to low electronic conductivity, large flake size and uniform coverage of SS substrate, which probably impeded the corrosive ions from the solution from diffusing through the coating.
DDP-resistant ovarian cancer cells-derived exosomal microRNA-30a-5p reduces the resistance of ovarian cancer cells to DDP

Exosomes carrying microRNAs (miRNAs) have been demonstrated to play critical roles in the regulation of development, growth and metastasis of cancer. Bioinformatic predictions identified differentially expressed SRY-box 9 (SOX9) in OC, and the regulatory miRNA miR-139-5p. Here, we aim to evaluate the function of exosomal miR-139-5p in the sensitivity of ovarian cancer (OC) cells to cis-diamminedichloroplatinum(II) (DDP). Expression pattern of miR-139-5p and SOX9 in ovarian cancer cells (SKOV3) and DDP-resistant cells (SKOV3/DDP) was identified using reverse transcription quantitative polymerase chain reaction and western blot analysis. The relationship between miR-139-5p and SOX9 was validated using a dual-luciferase reporter assay. SKOV3/DDP cell line was developed and introduced with miR-30a-5p mimic to analyse the effects of miR-30a-5p on resistance to DDP. The in vitro and in vivo effects of exosomal miR-30a-5p on resistance of SKOV3 cells to DDP were assessed in a co-culture system of exosomes and OC cells as well as in tumour-bearing nude mice. High expression of SOX9 and low expression of miR-30-5p were witnessed in OC. Furthermore, miR-30-5p, a downregulated miRNA in SKOV3/DDP cells, increased the rate of cell apoptosis and enhanced the sensitivity of SKOV3/DDP cells to DDP by targeting SOX9. Moreover, exosomes carrying miR-30a-5p were identified to sensitize SKOV3/DDP cells to DDP both in vitro and in vivo. These data together supported an important conclusion that DDP-resistant OC cell-derived exosomal miR-30a-5p enhanced cellular sensitivity to DDP, highlighting a potential strategy to overcome drug resistance.
Offspring reverse transcriptome responses to maternal deprivation when reared with pathogens in an insect with facultative family life

Offspring of species with facultative family life are able to live with and without parents (i.e. to adjust to extreme changes in their social environment). While these adjustments are well understood on a phenotypic level, their genetic underpinnings remain surprisingly understudied. Investigating gene expression changes in response to parental absence may elucidate the genetic constraints driving evolutionary transitions between solitary and family life. Here, we manipulated maternal presence to observe gene expression changes in the fat body of juvenile European earwigs, an insect with facultative family life. Because parents typically protect offspring against pathogens, expression changes were recorded in pathogen-free and pathogen-exposed environments. We found that manipulating maternal presence changed the expression of 154 genes, including several metabolism and growth-related genes, and that this change depended on pathogen presence. Specifically, localization and cell transporter genes were downregulated in maternal absence without pathogens but upregulated with pathogens. At least one immunity gene (pathogenesis-related protein 5) was affected by pathogen exposure regardless of maternal presence. Overall, our findings explicate how offspring adjust to parental deprivation on a molecular level and reveal that such adjustments heavily depend on pathogens in the environment. This emphasizes the central role of pathogens in family life evolution.