Miracle-dz/UniFAR-FTUs huggingface dataset:
TA-ABC: Two-Archive Artificial Bee Colony for Multi-objective Software Module Clustering Problem

Abstract Multi-objective software module clustering problem (M-SMCP) aims to automatically produce clustering solutions that optimize multiple conflicting clustering criteria simultaneously. Multi-objective evolutionary algorithms (MOEAs) have been a most appropriate alternate for solving M-SMCPs. Recently, it has been observed that the performance of MOEAs based on Pareto dominance selection technique degrades with multi-objective optimization problem having more than three objective functions. To alleviate this issue for M-SMCPs containing more than three objective functions, we propose a two-archive based artificial bee colony (TA-ABC) algorithm. For this contribution, a two-archive concept has been incorporated in the TA-ABC algorithm. Additionally, an improved indicator-based selection method is used instead of Pareto dominance selection technique. To validate the performance of TA-ABC, an empirical study is conducted with two well-known M-SMCPs, i.e. equal-size cluster approach and maximizing cluster approach, each containing five objective functions. The clustering result produced by TA-ABC is compared with existing genetic based two-archive algorithm (TAA) and non-dominated sorting genetic algorithm II (NSGA-II) over seven un-weighted and 10 weighted practical problems. The comparison results show that the proposed TA-ABC outperforms significantly TAA and NSGA-II in terms of modularization quality, coupling, cohesion, Pareto optimality, inverted generational distance, hypervolume, and spread performance metrics.
Modelling of partial discharge characteristics in electrical tree channels: Estimating the PD inception and extinction voltages

Partial discharge (PD) characteristics are inherently linked to associated electrical tree growth characteristics. This paper employs both phase-resolved patterns and pulse-sequence analysis (PSA) to study PDs in a short branch-tree grown from a metallic needle tip. It is found that the shape of PSA dV-dV patterns vary with applied voltage; in particular the values of characteristic voltage difference increase with applied voltage. At 10 kV the value of characteristic voltage difference decreases with tree growth, the average number of PDs per cycle increases, the PD magnitudes decrease, and the characteristic wing-like phase-resolved pattern evolves into a turtle-like pattern. This paper proposes a physical model relating PD activity to tree development, considering space charge accumulation inside the tree channel. All key features of experimental results are explained by the model. It is shown that the characteristic voltage differences are determined by the local PD inception and extinction electric fields. These key parameters determining the PD events can be calculated giving a clear physical interpretation of PD measurements, which is invaluable to those using PD as a condition monitoring or asset management tool.
Research on Inductance Unbalance and Thrust Ripple Suppression of Slot-Less Tubular Permanent Magnet Synchronous Linear Motor

Affected by the end effect, the inductances of the slot-less tubular permanent magnet synchronous linear motor (TPMSLM) are unbalanced. This paper reports the research on the inductance unbalance and the thrust ripple characteristics for the slot-less TPMSLM, and it presents an optimization scheme adopting modular structure with a three-segment primary component. The inductance unbalance characteristic is obtained and quantitatively investigated based on the finite element models. The causes of the self-inductance unbalance and the mutual-inductance are explained, respectively, and the difference between the two unbalances are quantitate investigated. Then, the thrust ripple is specifically analyzed by establishing the voltage and thrust models according to the laws of the inductance unbalance. Thus, the theoretical connection between the inductance unbalance and the thrust ripple has been established. Moreover, the inductance and thrust ripple characteristics of the modular motor are discussed and compared with those of the conventional motor. The prototypes of the conventional motor and the modular motor are developed to verify the findings of the research. At last, corresponding conclusions obtained by the comparison of modular motor with conventional motor are drawn.
Comparing historical and contemporary maps - a methodological framework for a cartographic map comparison applied to Swiss maps

ABSTRACT Topographic maps are powerful tools for the purpose of identifying land use and cover change (LUCC) as they are among the most reliable representations of past landscapes for the time prior to the existence of aerial photography. In light of the increased availability of historical maps, we argue that there is a need for a standardized process to assess map comparability in a systematic way in order to avoid, or at least minimize, the detection of spurious landscape changes due to incompatible map series. A full understanding of map quality, background and error distributions is fundamental to attain reliable LUCC results. The conceptual framework presented in this study considers the context, distortion and cartographic generalization of topographic maps. Furthermore, it includes an approach to homogenize the level of generalization of landscape elements (e.g. forests) from maps with different scales. To demonstrate its application, we assessed the comparability of seven topographic maps from Canton Zurich covering a time span of 336 years (1664–2000). Overall, for the maps of Canton Zurich, a wall-to-wall comparison of forest cover based on the topographic maps presented here can be problematic for the oldest map from 1664. Based on the results, a wall-to-wall comparison with the later maps is not recommended, due to its substantial distortion. Yet, after re-generalization of natural landscape elements, such as forests, a comparison based on landscape indices is possible, even for the oldest map. Furthermore, our results demonstrate that maps from the mid-19th century onward possess an acceptable level of accuracy. This framework can be applied to a wide range of maps at regional, national, or global levels, providing the opportunity to look at land cover history over multiple centuries.
FashionNet: Personalized Outfit Recommendation with Deep Neural Network

With the rapid growth of fashion-focused social networks and online shopping, intelligent fashion recommendation is now in great need. We design algorithms which automatically suggest users outfits (e.g. a shirt, together with a skirt and a pair of high-heel shoes), that fit their personal fashion preferences. Recommending sets, each of which is composed of multiple interacted items, is relatively new to recommender systems, which usually recommend individual items to users. We explore the use of deep networks for this challenging task. Our system, dubbed FashionNet, consists of two components, a feature network for feature extraction and a matching network for compatibility computation. The former is achieved through a deep convolutional network. And for the latter, we adopt a multi-layer fully-connected network structure. We design and compare three alternative architectures for FashionNet. To achieve personalized recommendation, we develop a two-stage training strategy, which uses the fine-tuning technique to transfer a general compatibility model to a model that embeds personal preference. Experiments on a large scale data set collected from a popular fashion-focused social network validate the effectiveness of the proposed networks.
Performance Analysis of an Uplink Massive MU-MIMO OFDM-Based System

A very well-known technology used nowadays in wireless communication systems is the one in which multiple antennas are employed at the receiver and at the transmitter terminals (MIMO) in order to improve overall system performances. This paper proposes a Massive MIMO system that uses orthogonal frequency-division multiplexing (OFDM) and investigates the performances achieved when the transmission is made over a Rayleigh fading channel, with different modulation schemes involved. The performance of the system will be evaluated depending on the number of the antennas at the base station (BS) and the number of active users, the main performance indicator used being the Bit Error Rate (BER).
Novel Cross-View Human Action Model Recognition Based on the Powerful View-Invariant Features Technique

One of the most important research topics nowadays is human action recognition, which is of significant interest to the computer vision and machine learning communities. Some of the factors that hamper it include changes in postures and shapes and the memory space and time required to gather, store, label, and process the pictures. During our research, we noted a considerable complexity to recognize human actions from different viewpoints, and this can be explained by the position and orientation of the viewer related to the position of the subject. We attempted to address this issue in this paper by learning different special view-invariant facets that are robust to view variations. Moreover, we focused on providing a solution to this challenge by exploring view-specific as well as view-shared facets utilizing a novel deep model called the sample-affinity matrix (SAM). These models can accurately determine the similarities among samples of videos in diverse angles of the camera and enable us to precisely fine-tune transfer between various views and learn more detailed shared facets found in cross-view action identification. Additionally, we proposed a novel view-invariant facets algorithm that enabled us to better comprehend the internal processes of our project. Using a series of experiments applied on INRIA Xmas Motion Acquisition Sequences (IXMAS) and the Northwestern–UCLA Multi-view Action 3D (NUMA) datasets, we were able to show that our technique performs much better than state-of-the-art techniques.
Ultrastructural description of fresh and frozen/thawed sperm derived from collared peccaries (Pecari tajacu Linnaeus, 1,758)

The aim was to describe, through scanning electron microscopy (SEM) and transmission electron microscopy (TEM), the ultrastructure of peccaries' fresh and frozen–thawed sperm. For that, semen derived from three mature males was obtained by electroejaculation and evaluated for motility, membrane integrity, membrane functionality, chromatin integrity, and morphology through light microscopy. Samples were frozen using a Tris extender plus egg yolk (20%) and glycerol (6%). Then, fresh and frozen–thawed semen samples were mixed in different sperm pools that were processed for SEM and TEM. Sperm motility, membrane integrity, and functionality were impaired (p < .05) by freezing–thawing procedures, but sperm morphology, and chromatin integrity evaluated by light microscopy were not significantly affected. The SEM revealed that peccaries’ sperm presents a flattened head in a paddle format, measuring 6.07 μm in length and 3.84 μm in width, with a vastus acrosome (4.46 μm). Normal tails measure 38.11 μm, being formed by an extensive midpiece with 15.52 μm in length. In frozen–thawed samples, both SEM and TEM provide us information about damage undetected through light microscopy as the presence of vesicles in the acrosome, loose plasma membrane, vacuolized mitochondria, dense fibers disorganized, and decondensed chromatin. In conclusion, we provide the first description of the sperm ultrasctruture in collared peccaries. Moreover, SEM and TEM help us to identify some nanometric damage provoked by freezing–thawing procedures, thus providing valuable information for the improvement of such important protocols used for biobanking formation.
Switching Controller Parameterization Realization for an LPV Plant with H∞ Performance

This paper propose a novel design procedure for output-feedback switching linear parameter-varying (LPV) controllerS. The switching controller which include several LPV controllers designed beforehand and independently with corresponding $\text{H}_{\infty}$ control performance criteria. The new procedure based on the Youla parameterization ideas, state space realization of free parameters of these LPV controllers is achieved. This realization ensures that each switching point satisfy the $\text{H}_{\infty}$ performance under arbitrary switching. The effectiveness of the proposed method is demonstrated with a numerical example.
Shallow Convolutional Neural Networks for Pattern Recognition Problems

Paper describes an investigation of possible usage of shallow (limited by few layers only) convolutional neural networks to solve famous pattern classification problems. Brazilian coffee scenes, SAT-4/SAT-6, MNIST, UC Merced Land Use and CIFAR datasets were tested. It is shown that shallow convolution neural networks with partial training may be effective enough to produce the result close to state-of-the-art deep networks but also limitations are found.
Industrial Antenna Development for 77-GHz Level-Crossing Monitoring Radar [Antenna Applications Corner]

Preventing accidents at level crossings is a global-size problem. Despite all the European railways having planned to replace them with underpasses or overpasses, it is expected that, for the next 20 years, there will still be a strong need for additional automatic protection to be achieved with, for example, a radar detection system. In this article, we present and compare the different antenna solutions that have been designed and manufactured for a 77-GHz level-crossing monitoring frequency-modulated continuous-wave (FMCW) radar, from its first release to the final commercial product. Antenna performances, reliability, and reproducibility characteristics are analyzed in light of manufacturing costs.
ERN: Edge Loss Reinforced Semantic Segmentation Network for Remote Sensing Images

The semantic segmentation of remote sensing images faces two major challenges: high inter-class similarity and interference from ubiquitous shadows. In order to address these issues, we develop a novel edge loss reinforced semantic segmentation network (ERN) that leverages the spatial boundary context to reduce the semantic ambiguity. The main contributions of this paper are as follows: (1) we propose a novel end-to-end semantic segmentation network for remote sensing, which involves multiple weighted edge supervisions to retain spatial boundary information; (2) the main representations of the network are shared between the edge loss reinforced structures and semantic segmentation, which means that the ERN simultaneously achieves semantic segmentation and edge detection without significantly increasing the model complexity; and (3) we explore and discuss different ERN schemes to guide the design of future networks. Extensive experimental results on two remote sensing datasets demonstrate the effectiveness of our approach both in quantitative and qualitative evaluation. Specifically, the semantic segmentation performance in shadow-affected regions is significantly improved.
LTNN: A Layerwise Tensorized Compression of Multilayer Neural Network

An efficient deep learning requires a memory-efficient construction of a neural network. This paper introduces a layerwise tensorized formulation of a multilayer neural network, called LTNN, such that the weight matrix can be significantly compressed during training. By reshaping the multilayer neural network weight matrix into a high-dimensional tensor with a low-rank approximation, significant network compression can be achieved with maintained accuracy. An according layerwise training is developed by a modified alternating least-squares method with backward propagation for fine-tuning only. LTNN can provide the state-of-the-art results on various benchmarks with significant compression. For MNIST benchmark, LTNN shows <inline-formula> <tex-math notation="LaTeX">$64 \times $ </tex-math></inline-formula> compression rate without accuracy drop. For Imagenet12 benchmark, our proposed LTNN achieves <inline-formula> <tex-math notation="LaTeX">$35.84 \times $ </tex-math></inline-formula> compression of the neural network with around 2% accuracy drop. We have also shown <inline-formula> <tex-math notation="LaTeX">$1.615 \times $ </tex-math></inline-formula> faster on inference speed than the existing works due to the smaller tensor core ranks.
Binarization Free Layout Analysis for Arabic Historical Documents Using Fully Convolutional Networks

We present a Fully Convolutional Network based method for layout analysis of non-binarized historical Arabic manuscripts. The document image is segmented into main text and side text regions by dense pixel prediction. Convolutional part of the network can learn useful features from the non-binarized document images and is robust to degradation and uncontrained layouts. We have evaluated the proposed method on a private dataset containing challenging historical Arabic manuscripts to demonstrate it effectiveness.
Classification with Multi-Modal Classes Using Evolutionary Algorithms and Constrained Clustering

Constrained clustering has been an active research topic in the last decade. Among the different kinds of constraints, must-link and cannot-link are the most adopted ones. However, most algorithms assume that the number of clusters are known a priori. Besides this usually unrealistic assumption, one often ignores the fact that must-link constraints may correspond to objects in different density regions of the input space, thereby requiring a more complex structure to represent the underlying concept. Aimed at overcoming these limitations, we present the Feasible-Infeasible Evolutionary Create & Eliminate for Expectation Maximization (FIECE-EM), which identifies a Gaussian Mixture Model that is a good fit for the data, while meeting the constraints provided. We compare FIECE-EM with a state-of-the-art algorithm. Our results indicate that FIECE-EM obtains competitive results, without the need for fine-tuning a tradeoff parameter as in the state-of-the-art algorithm under comparison.
Molecular analysis of peripheral lung adenocarcinoma in brush cytology obtained by EBUS plus fluoroscopy‐guided bronchoscopy

More than 60% of patients with lung cancer are diagnosed at advanced stages. The introduction of targeted therapies requires molecular diagnosis to guide treatment. The aim of this study was to evaluate the feasibility of performing mutational analysis with brushing specimens obtained by radial‐miniprobe endobronchial ultrasound (R‐EBUS) plus fluoroscopy‐guided bronchoscopy in patients with peripheral pulmonary adenocarcinoma.
Truncated Impedance Sheet Model for Low-Profile Broadband Nonresonant-Cell Metasurface Antennas Using Characteristic Mode Analysis

A unit-cell-free truncated impedance sheet model is proposed for the modeling and design of low-profile broadband metasurface antennas (MAs) composed of nonresonant unit cells using characteristic mode analysis. Different from conventional MAs with locally resonant unit cells, the nonresonant unit cells of the proposed MAs only contribute to the grid impedance and the MA operates with the global resonances of the metasurfaces. New measures are also reported to incorporate a higher order metasurface mode for wideband radiation. A low-profile broadband MA is designed at the 5 GHz Wi-Fi bands for the proof of concept. Three modes are excited simultaneously, including a quasi-TM<sub>01</sub> mode, a quasi-TM<sub>21</sub> mode, and a dipole mode. With a size of <inline-formula> <tex-math notation="LaTeX">$0.8\lambda _{L} \times 0.8\lambda _{L} \times 0.087\lambda _{L}$ </tex-math></inline-formula> (<inline-formula> <tex-math notation="LaTeX">$\lambda _{\mathbf {L}}$ </tex-math></inline-formula> is the wavelength in free space at the lowest operating frequency), the prototyped MA achieves a bandwidth of 45% with <inline-formula> <tex-math notation="LaTeX">$\vert S_{11}\vert < -10$ </tex-math></inline-formula> dB, boresight gain of 8.5–11.6 dBi, and 3 dB beamwidth of 40°–60°/43°–65° for the E-/H-planes.
Hybridization of Cartesian Genetic Programming and Differential Evolution for Generating Classifiers Based on Neural Networks

Despite the significance of Artificial Neural Networks (ANNs) in practical situations and the several works available in the literature, to adjust its parameters remains as a current problem. Hence, the advent of methods to assist users during this modeling is relevant. Three hybrid techniques based on Cartesian Genetic Programming (CGP) and Differential Evolution (DE) are proposed here for the construction of ANNs. The developed methods carry out an uncoupled evolution of the topology (using CGP) and the weights (using DE). The ANNs are evolved for classification problems, and seven benchmark datasets are used in the computational experiments. Results show the superiority of the proposed methods when compared to other techniques from the literature.
A Novel Intrusion Detection Model for a Massive Network Using Convolutional Neural Networks

More and more network traffic data have brought great challenge to traditional intrusion detection system. The detection performance is tightly related to selected features and classifiers, but traditional feature selection algorithms and classification algorithms can’t perform well in massive data environment. Also the raw traffic data are imbalanced, which has a serious impact on the classification results. In this paper, we propose a novel network intrusion detection model utilizing convolutional neural networks (CNNs). We use CNN to select traffic features from raw data set automatically, and we set the cost function weight coefficient of each class based on its numbers to solve the imbalanced data set problem. The model not only reduces the false alarm rate (FAR) but also improves the accuracy of the class with small numbers. To reduce the calculation cost further, we convert the raw traffic vector format into image format. We use the standard NSL-KDD data set to evaluate the performance of the proposed CNN model. The experimental results show that the accuracy, FAR, and calculation cost of the proposed model perform better than traditional standard algorithms. It is an effective and reliable solution for the intrusion detection of a massive network.
A New Query Recommendation Method Supporting Exploratory Search Based on Search Goal Shift Graphs

Exploratory search is an increasingly important activity for Web searchers. However, the current search system can not provide sufficient support for exploratory search. Therefore, we made in-depth analysis for exploratory search processes, and found that there are a lot of search goal shift phenomena in exploratory search. Based on this fact, we have designed a new query recommendation method to support exploratory search. Firstly, according to the behavioral characteristics of searchers in the search goal shift processes, all the queries submitted in the search goal shift processes are extracted from search engine logs using machine learning. And then, we have used the queries to build a search goal shift graph; finally, the random walk algorithm is used to obtain the query recommendations in the search goal shift graph. In addition, we demonstrated the effectiveness of the method for exploratory search by comparing experiments with the other methods.