Miracle-dz/UniFAR-FTUs huggingface dataset:
Uplink Performance Analysis of User- Centric Small Cell Aided Dense HCNets With Uplink-Downlink Decoupling

The deployment of low power small base stations (SBSs) in high density user areas enhances the performance of heterogeneous cellular networks (HCNets). Such user-centric SBS deployment (UCSD) is more appropriate as compared with the conventional uniformly deployed user equipment (UE) based HCNets, because it captures the correlation between UEs and SBSs. In the literature, the downlink (DL) performance of UCSD based HCNet (UC-HCNet) has been analyzed under maximum received signal power (MRSP) association strategy. In this paper, we develop and analyze the uplink (UL) performance of UC-HCNets with decoupled UL-DL association (DUDA) using stochastic geometery. We evaluate the total UL interference considering two special cases of Poisson cluster process (PCP), i.e., Matern cluster process (MCP) and Thomas cluster process (TCP), in UC-HCNet. The proposed DUDA scheme enhances the system performance gain by assuming dual connectivity of UE and BSs in the UL and DL directions. Furthermore, fractional power control (FPC) policy is considered to limit the UL power consumption. The system performance is evaluated in terms of outage probability and rate coverage. The numerical results are validated through simulations. Numerical results show that the performance improvement of DUDA in sparse scenario is higher as compared with dense scenario. Furthermore, it is investigated that the performance gain of DUDA is higher for larger UE variance and cluster radius in case of MCP and TCP, respectively.
A Computational Approach to Include Extraction for Recognizable Proof of Self-Destructive Ideation in Tweets

Suicide is increased in recent years compared with the past and social media has a great impact on these suicides among youngsters. The internet and social media have a great influence on suicide-related behavior. Between 2007 and 2019, technology emerged into human life as the most important aspect of society. Twitter has Increased in investigating as a means of detecting mental health status, including suicidality and depression in the population. In twitter, knowledge will mechanically analyze the feelings of those tweets. To investigate a tool data mining to extract more helpful information for the classification of tweets. Suicidal ideation is detected by using several informative sets of features, including syntactic, statistical, linguistic and topic features. To examine whether suicide-related posts on tweeter could be determined by human coders and then simulate by machine learning, which verifies the effectiveness of performance in terms of precision, recall, and accuracy on sentient analysis.
An Integrated Detection and Tracking System Applied to Unmanned Surface Vehicles

Perceiving surrounding environment is essential in the navigation, obstacle avoidance and other tasks of Unmanned Surface Vehicles (USV), in which object detection and tracking play vital roles. However, it is susceptible to the influence of occlusions, scale variations and severe shaking, in the meantime requiring higher speed. We introduce an integrated detection and tracking system applied to USV, which combines the tracking algorithm based on improved adaptive scale correlation filter with the detection algorithm based on deep learning. After tracking the fixed number of frames, we perform the re-detection mechanism and utilize the bounding boxes selection strategy to determine subsequent tracking boxes. We conduct experiments in practice and the results demonstrate that the system can continuously detect and track objects on the water at real time with the speed of 15FPS and the accuracy of 0.87, which illustrates robustness is improved on the condition that time consumption of detection and tracking is reduced.
Hunting IoT Botnets with Wide-area-network Flow Data

With the rise of Internet-of-Things (IoT) technology, botnets like Mirai start to exploit IoT devices and pose severe threats to cybersecurity. In this paper, big data analysis is conducted on wide-area-network session records in an attempt to perceive the influence of botnets on the cyberspace. cNetS, a practical analysis system that can detect and profile botnets is introduced. With this system, infected devices are located with their behaviors reconstructed in detail. Data analysis methods employed can serve as a guidance to detect and combat Mirai-like botnets on the full Internet scale.
Outline Feature Extraction of Positron Image Based on a 3D Anisotropic Convolution Operator

This study investigates the application of positron annihilation techniques to the interior of dense metal cavities for Three-dimensional (3D) imaging and extraction of contour features inside the cavities. A feature extraction algorithm based on a 3D anisotropic convolutional operator is proposed for profile feature extraction in low-resolution, low-contrast, and low-signal-to-noise positron image. First, aiming at the problem of positron image noise caused by inconsistent detectors and metal scattering effects, an image preprocessing algorithm combining filtering and full pixel correction is proposed. A 3D anisotropic convolution operator is then designed to extract contour features. To solve the contour feature discontinuity in the extracted contour feature, a 3D path search algorithm is proposed to obtain the centroid coordinate set of the contour feature, and then the centroid coordinate set is subjected to 3D curve fitting to obtain a smooth and continuous contour feature. The study is carried out on the raw 16-bit Digital Imaging and Communications in Medicine (DICOM) data of the positron image, and the data are processed from a 3D perspective, taking full advantage of the correlation between slices in the 3D positron image. In the actual testing, positron images with different kinds of foreign objects in the cavity are extracted using a 3D anisotropic convolution operator, and the contour feature extraction resolution reaches 2 mm.
Mobility Prediction Based Multi-Directional Broadcasting for Both Highway and Urban Vehicular Sensor Networks

This paper proposes a mobility prediction based multi-directional broadcasting (MPMB) protocol for both highway and urban Vehicular Sensor Networks (VSNs). VSNs have to ensure the prompt dissemination of critical sensing data to all vehicles within the Region of Interest (ROI) to avoid various road dangers. Several research studies have proposed various broadcasting protocols, but most of them just focus on either highway or urban scenarios. MPMB protocol is derived from the mobility prediction based directed broadcasting (MPDB) protocol which is a single directional broadcasting protocol for highways. The main purpose of MPMB is to improve broadcasting efficiency in both highway and urban scenarios. To do this, MPMB adopts the adaptive directional sectors and the Store-Carry-Forward (SCF) scheme to select the next broadcasting (rebroadcasting) vehicles based on the mobility prediction. MPMB protocol consists of (i) mobility prediction stage and (ii) broadcasting stage. In the mobility prediction stage, each vehicle predicts the Link Available Time (LAT) of its all neighbor vehicles through periodical beaconing. In the broadcasting stage, each broadcasting vehicle adaptively selects a vehicle with the largest LAT value predicted in the previous mobility prediction stage as a rebroadcasting vehicle in each possible directional sector. The simulation results show that MPMB protocol has better performance in terms of packet delivery rate and packet delay in comparison with three well-known reference protocols in both highway and urban scenarios with various traffic density.
An Android Malware Detection Approach Based on SIMGRU

With the rapid development of the Internet era, the number of malware has reached an unprecedented peak, and therefore malware is threatening global network security seriously. In this article, we propose an Android malware detection approach based on SIMGRU, which belongs to the static detection approach. The similarity of clustering is widely used in static analysis of android malware, so we introduce the similarity to improve Gated Recurrent Unit (GRU), and obtain three different structures of SimGRU: InputSimGRU, HiddenSimGRU, and InputHiddenSimGRU. The InputHiddenSimGRU is the combination of InputSimGRU and HiddenSimGRU. The experiment shows that InputSimGRU, HiddenSimGRU, and InputHiddenSimGRU outperform the conventional GRU model and other methods.
Quadrant Base Location Tracking Technique in MANET

Infrastructure less functioning of Mobile Ad-Hoc Network has grabbed the attention of the researcher in last few decades. Enormously beneficial in the natural phenomenon operations like flood, battlefield etc. Highly dynamic nature, restricted range of communication and rapid change in the topology of the network makes it more critical to handle. Depending upon the topology different routing protocol provides different significant benefits. Position based routing exploits nodes location for packet forwarding makes it performing significantly better than other routing protocols. Conversely, Position based Routing [2] endure from assorted security issues. In this paper proposed a Quadrant base location tracking technique in MANET and reduce the overhead of network like routing packet overhead, end to end delay The nodes are continuously changes its location but with respect to centroid node identified the location of each node in network. The nodes are changing the coordinate then maintain the record on nodes location by that the sender is easily identified the receiver location. The proposed scheme improves the performance by reducing the overhead and provides the transparency in location identification. The modified approach increases the data receiving percentage in fast and efficient way.
Data Augmentation Methods for Low-Resource Orthographic Syllabification

An <inline-formula> <tex-math notation="LaTeX">${n}$ </tex-math></inline-formula>-gram syllabification model generally produces a high error rate for a low-resource language, such as Indonesian, because of the high rate of out-of-vocabulary (OOV) <inline-formula> <tex-math notation="LaTeX">${n}$ </tex-math></inline-formula>-grams. In this paper, a combination of three methods of data augmentations is proposed to solve the problem, namely swapping consonant-graphemes, flipping onsets, and transposing nuclei. An investigation on 50k Indonesian words shows that the combination of three data augmentation methods drastically increases the amount of both unigrams and bigrams. A previous procedure of flipping onsets has been proven to enhance the standard bigram-syllabification by relatively decreasing the syllable error rate (SER) by up to 18.02%. Meanwhile, the previous swapping consonant-graphemes has been proven to give a relative decrement of SER up to 31.39%. In this research, a new transposing nuclei-based augmentation method is proposed and combined with both flipping and swapping procedures to tackle the drawback of bigram syllabification in handling the OOV bigrams. An evaluation based on <inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula>-fold cross-validation (<inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula>-FCV), using <inline-formula> <tex-math notation="LaTeX">$k= 5$ </tex-math></inline-formula>, for 50 thousand Indonesian formal words concludes that the proposed combination of the three procedures relatively decreases the mean SER produced by the standard bigram model by up to 37.63%. The proposed model is comparable to the fuzzy <inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula>-nearest neighbor in every class (FkNNC)-based model. It is worse than the state-of-the-art model, which is developed using a combination of bidirectional long short-term memory (BiLSTM), convolutional neural networks (CNN), and conditional random fields (CRF), but it offers a low complexity.
Evaluation of the Maintainability Aspect of Industry 4.0 Service-oriented Production

Current markets are characterized by rapid changing in requirements and new customer needs. Thus, fast changeable production is a fundamental goal of Industry 4.0 scheme, which aims to make the manufacturing more changeable and efficient by interconnecting the various factory components, and representing the factory assets virtually in digital twins.In order to study the Industry 4.0 changeability capability, we apply scenario-based evaluation. We derive three change scenarios that can be observed in a plant: Change of product flow depending on quality, depending on product type and the introduction of a new product. Using these scenarios, we compare between the third industrial revolution (Industry 3) and Industry 4.0 architectures based on the resultant change impact.For our evaluation, we utilize the Architecture-Level Modifiability Analysis (ALMA) method and describe its instantiation to the given context, providing ALMA 4.0, a guideline for Industry 4.0 Maintainability scenario-based evaluation. The result shows that the change impact on Industry 4.0 is less than Industry 3. Thus, we provide quantitative evidence that changing Industry 4.0 architecture incurs less efforts than Industry 3 changes.
Cirrus Detection Based on Tensor Multi-Mode Expansion Sum Nuclear Norm in Infrared Imagery

Infrared small target detection systems are an important part of space infrared imaging satellites. However, small infrared target detection is often affected by cirrus false alarm sources with similar grayscales. In this article, an infrared cirrus detection method based on the tensor robust principal component analysis model (TRPCA) is proposed. The method treats multiple bands of remote sensing data as tensors, but classical tensor nuclear norms cannot represent the tensor rank well; therefore, we use tensor multi-mode expansion sum nuclear norm (TMESNN) to approximate the tensor rank better. First, a set of Landsat-8 data is transformed into a tensor model, and a TRPCA model is constructed by TMESNN and the $L_{1} $ norm. Then, this model is solved by Ket augments and the alternating direction method of multipliers (ADMM). Finally, Mallat wavelet transform is used to supplement information and remove clutter, and the final detection result is obtained by adaptive threshold segmentation. Compared with other optimization-based methods, this method has better detection performance and accuracy.
Removal of Unnecessary Files using Predictive Data Mining Method

This research paper focuses on using the data mining methodology and soft computing approach to the field of clinical data. The predictive model (classifier) takes the historical clinical data of patients from a multiple dataset and performs the learning and training through an algorithm. The dataset for the multiple diseases of patients is used to elaborate the application and to explain the design and working of framework and multiclass classifier. And doing so it is found that the algorithm works correctly and this multiclass class classification is beneficial for saving time required for classification process on large scale, also for saving the storage space on storage space by avoiding repetition of data.
Research on Load Forecasting Method of Distribution Transformer based on Deep Learning

Electrical load forecasting is of great significance, which can guarantee the system stability under large disturbances and optimize the distribution of energy resources in the smart grid. Load forecasting of distribution is a very active research field and an important aspect, as the load power of distribution transformer is closely related to the health and reliable of the power grid. Deep Learning technology have shown to be very powerful in load forecasting of distribution. Based on the deep learning technology, this paper uses the historical load data of distribution transformer to establish an appropriate prediction model to predict the future load of transformer. In this paper, the long short-term memory(LSTM) model is used to predict the distribution transformer load. The idea of integrated learning is used to improve the accuracy of LSTM network in load forecasting. The input dimension of the model is enriched to solve the problem that the model cannot be built due to the lack of historical data. Finally, the experimental results show the superiority of the proposed method.
A Network Pruning Method for Remote Sensing Image Scene Classification

Deep convolutional neural networks have been widely used to improve remote sensing image scene classification performance. However, most of these networks include many parameters and need many computational resources. Which hinders the applications of these networks when facing the satellite, plane or other mobile platforms. In this paper, we introduce a network pruning framework which can reduce the size of the network model and maintain the classification accuracy. In this framework, we train the pruned model using both the original unpruned model's output and training dataset. Which can learn more information than retrain using dataset only. In our experiments, we evaluate our method for remote sensing scene classification on NWPU-RESISC45 dataset. The results demonstrate that our method was effective and maintained the model classification accuracy.
An Ensemble of Filters and Classifiers for Fault Diagnosis

Filters, a class of feature selection methods, play a crucial role in mechanical fault diagnosis since they have the ability to improve diagnosis accuracy and reduce computational costs. Most existing fault diagnostic methods only take advantage of a single filter and a classifier, which may lead to a variable performance easily. To overcome this shortcoming, an ensemble of filters and classifiers approach is proposed for fault diagnosis. In the proposed method, six filters, based on distinct metrics, as well as a K-nearest neighbor (KNN) classifier are adopted. Each filter is paired with a KNN to form a base model. For each base model, the filter first selects a subset of features which are then utilized to train and to test KNN. The outputs of six base models are aggregated by a developed weighting technique. The performance of the proposed approach is examined by means of two bearing datasets. Experimental results consistently demonstrate that the proposed an ensemble of filters and classifiers approach significantly outperforms the methods that use a single filter and a classifier on recognizing different bearing fault categories.
Filter-Clamped Two-Level Three-Phase Transformerless Grid-Connected Photovoltaic Inverter for Leakage Current Reduction

Transformerless Photovoltaic (PV) grid-connected systems benefit from improved cost, size, weight, and efficiency compared to the isolated alternatives. A drawback of the trasnsformerless PV inverters is the leakage current that flows through the parasitic capacitance formed between the PV cells and panel metal frame connected to the earth. The leakage current increases the current harmonics injected into the utility grid, the radiated and conducted electromagnetic interference (EMI), and losses. In this paper, a two-stage three-phase Filter-Clamped (FC) transformerless PV system is employed that reduces the leakage current and output current THD compared to the conventional three-phase inverters. Unlike other transformerless PV inverters, the FC inverter does not require any additional component. The FC transformerless PV inverter is analyzed mathematically and its excellence is compared to the conventional three-phase inverter through simulation results.
Characterization and Evaluation of the Solubility and Oral Bioavailability of Rutin–Ethanolate Solvate

Rutin has many biological activities, but poor solubility and absorption limit its oral application. This study aimed to investigate the characterization of metastable rutin–ethanolate and its solubility and oral bioavailability. In this research, a soluble rutin/CH3CH2OH solvate (Form Π) was prepared by solvent crystallization. High-performance liquid chromatography, gas chromatograph, and 1H-NMR showed that Form Π was formed by rutin and ethanol in a molar ratio of 1:1. The changes of Fourier transform infrared spectroscopy and 1H-NMR spectrum and the density functional theory (DFT) calculation predicted hydrogen bond formation between 4’-O of rutin and –OH of ethanol. The results of morphology, solid state CP/MAS 13C-NMR, X-ray diffraction, and differential scanning calorimetry (DSC) revealed that Form Π is a novel polymorph that differs from Form Ι (rutin trihydrate). Form Π can be stored for a long time under sealed and dry conditions at 40°C but would gradually transform into Form Ι under humid conditions. Although Form Π is a new metastable polymorph relative to Form Ι, Form Π has better solubility and faster dissolution rate. Moreover, the bioavailability of Form Π was 2.04 times higher than that of Form Ι. This outcome implied that Form Π would be a prospective raw material of oral preparation.
Sex ratio and mating structure in the parasitoid Hymenoptera.

Assumptions about mating structure have been important in the development of sex ratio theory. Fisher's theory of frequency dependent selection for sex ratio equality, and Charnov's theory of sex allocation when host quality varies, assume panmixis, whereas Hamilton's theory of local mate competition assumes mating occurs only at the natal patch. An important group of organisms in stimulating the development of sex ratio theory, and in its empirical testing, have been the parasitoid Hymenoptera. These exhibit many life history variations and possess a mechanism by which females can adaptively control their progeny sex ratio. Although many parasitoid species may conform closely to both the assumptions and predictions of the above models, others do not
Bankruptcy Decision-Making: An Empirical Study of Small-Business Bankruptcies

It is well known that a large fraction of firms seeking to reorganize under the U.S. Bankruptcy Code will be liquidated instead, and that the liquidation decision will often be made by a bankruptcy judge. Little is known, however, about the characteristics of optimal judicial decisionmaking in this context and whether the behavior of actual judges is consistent with these characteristics. Despite the paucity of theory and evidence, it is widely suspected that judges are poor decision-makers who allow failing firms to linger under the protection of the court. Drawing on optimal stopping theory, this study develops several new models of optimal judicial decision-making. Each generates the same implications: the probability (or hazard rate) of shutdown should be hump-shaped over time and decreasing in the uncertainty (or volatility) surrounding a firm’s going-concern value. These implications are tested against new data on decision-making by bankruptcy judges of the Northern District of Illinois, Eastern Division, a jurisdiction covering Chicago and outlying areas. The Associate Professor of Law, Columbia Law School. This is a revised version of my Ph.D. thesis. In addition to my committee—Gary Becker (chair), Douglas Baird, and Steven Kaplan—I owe thanks to Jennifer Arlen, Kenneth Ayotte, Ian Ayres, Theodore Eisenberg, Jeffrey Gordon, Marcel Kahan, Avery Katz, Mark Ramseyer, Robert Rasmussen, Mark Roe, David Skeel, Susan Pierson Sonderby, Eugene Wedoff, and workshop participants at Vanderbilt, Georgetown, Yale, Columbia, Northwestern, University of Pennsylvania, NYU, Cornell, and Harvard for their help. I am especially grateful to the judges of the United States Bankruptcy Court for the Northern District of Illinois for giving me access to the court’s electronic records and to the staff of the court and the Seventh Circuit Court of Appeals (especially Steve Horvath, Jean Dalicandro, and David Dusenberry) for helping me download the data. Basil Alsikafi, Felton Booker, Alan Littmann, and Christopher Swart provided excellent research assistance. Financial support from the John M. Olin Foundation and the Lynde and Harry Bradley Foundation is gratefully acknowledged.