Miracle-dz/UniFAR-FTUs huggingface dataset:
Broadband circularly polarized slot antenna arrays using linked elliptical slots for C- and X-band applications

Compact broadband circularly polarized elliptical slot antenna arrays (CPESAAs) are devised based on 2 × 2 and 4 × 4 circularly polarized (CP) elliptical slot antennas and a sequential phase feed network. To realize the circular polarization, eight rectangular stubs are embedded in the feed structure to excite two orthogonal E vectors in 2 × 2 CPESAA configuration. Moreover, by taking four rectangular slots out from each array element, return loss and axial-ratio bandwidths are noticeably increased. The investigated simple array element has a compact size of 20 × 20 × 1 mm3. As well, 2 × 2 CPESAA occupies a compact size of 55 × 60 × 1 mm3, which operates within the frequency band 4.18–9.35 GHz. Thus, 76.4% of S11 < −10 dB and 51.4% of CP bandwidth (4.43–7.5 GHz) are covered. Moreover, proposed 4 × 4 CPESAA is printed on 110 × 110 × 1 mm3 FR4 substrate and covers the frequency band of 1.15–12.98 GHz with CP at 2.95–8.82 GHz. Throughout the study, the design process of the proposed antenna arrays are presented and discussed in detail.
Corpus for Cyberbullying Prevention

Cyberbullying is the use of digital media to harass a person or group of people, through personal attacks, disclosure of confidential or false information, among other means. That is to say, it is considered cyberbullying, or cyber-aggression to everything that is done through electronic communication devices with the intended purpose of harming or attacking a person or a group. In this paper we present a starting project to prevent cyberbullying between kids and teenagers. The idea is to create a prevention system. A system which is installed in the mobile of a kid and, if a harassment is detected, some advice is given to the child. In case of serious or repeated behavior the parents are alerted. The focus of this paper is to describe the characteristics of the database to be used to train the system
Coronary Arteries Segmentation Based on 3D FCN With Attention Gate and Level Set Function

Coronary heart disease is one of the most serious health problems in the world nowadays. By segmenting and examining the coronary arteries in medical images, we can find artery stenosis and plaque, which are the main causes of this certain disease. Segmenting coronary arteries manually is time-consuming and subjective, and the traditional segmentation method requires a good initial point, which is also difficult to apply for the 3D coronary computed tomography angiography (CTA) data. In this paper, we propose a joint framework for coronary CTA segmentation based on deep learning and traditional level set method. A 3D fully convolutional network (FCN) is used to learn the 3D semantic features of coronary arteries, which provides an excellent initial point for the traditional level set. Moreover, an attention gate is added to the entire network, aiming to enhance the vessels and suppress irrelevant regions. The output of 3D FCN with attention gate is optimized by the level set to smooth the boundary to better fit the ground truth segmentation. The proposed algorithm is evaluated by the Jaccard index (JI) and the Dice similarity coefficient (DSC) scores. The experimental results show that the proposed framework provides significant better segmentation results than the vanilla 3D FCN intuitively and quantitively.
Public Sentiment on Political Campaign Using Twitter Data in 2017 Jakarta's Governor Election

The campaign period is the time when candidates introduce themselves to the public and socialize their vision and mission. Some media, including social media, can be used as a medium for campaigning. To know the public view of political campaigns can use sentiment analysis using Twitter data. This research analyzes public sentiment toward the political campaign for candidate pair of a governor and vice governor of DKI Jakarta in 2017. We use sentiStrength, a program using the lexicon-based approach as a classification method. We classify each tweet into three classes: positive, negative, and neutral classes. The results show that, in general, positive sentiments dominates negative sentiments for each candidate for governor and vice governor. The results also show that the positive sentiments of all pairs have the same sequence as the election results.
Spectral Linewidth vs. Front Facet Reflectivity of 780 nm DFB Diode Lasers at High Optical Output Power

The influence of the front facet reflectivity on the spectral linewidth of high power DFB (distributed feedback) diode lasers emitting at 780 nm has been investigated theoretically and experimentally. Characterization of lasers at various front facet reflections showed substantial reduction of the linewidth. This behavior is in reasonable agreement with simulation results. A minimum linewidth of 8 kHz was achieved at an output power of 85 mW with the laser featuring a front facet reflectivity of 30%. The device with a front facet reflectivity of 5% reached the same linewidth value at an output power of 290 mW.
Learning Pyramidal Hierarchical Features for Neonatal Face Detection

Because the facial features of the neonates are quite different from those of adults, most of face detection algorithms designed for adult samples aren't suitable for neonatal face detection. In this paper, a novel neonatal face detection algorithm is put forward, which utilizes convolutional neural network with pyramidal hierarchical features. Firstly, the convolutional neural network is used to extract the implicit features of the normalized neonatal image. Then, multi-scale feature maps are selected to predict and detect different sizes of neonatal faces. Finally, a classifier is used to classify the face or not, and the regressor generates the location of the neonatal face. The experimental results based on neonate facial image database prove that: (1)compared with the traditional face detection algorithms, the detection performance is effectively improved, (2)the network is better promoted by using data augmentation or fine-tuning, and (3) selecting multi-scale feature maps from different layers will give different detection performance. The proposed algorithm is superior to the state-of-the-art algorithms in the accuracy on the neonatal facial image database, while keeping real-time performance.
Principles of Implementation and Estimation of Influence Factors in Network Centric Systems

Rapid development of technical means and information technologies has created a new control concept -network centric. It arose in the military environment, but quickly captured multinational corporations, large network production and organizational systems, retail chains. The paper considers the principles of organization and criteria for the functioning of network centric systems, the evaluation of influence factors and synergetic effects in large-scale systems
Machine Translation from Big Data Perspective

In this exploratory study we attempt to draw parallels between so called dimensions of Big Data (i.e. Volume, Veracity, Variety, Velocity, or 4V -s) and various aspects of machine translation. For this purpose we set up a number of experiments that in our opinion help to relate machine translation to Big Data. In addition to the experiments, which are rather compact and, frankly, pale in comparison to industry level Big Data applications, we discuss possible scenarios where some of the aspects of machine translation fit firmly in the Big Data realm. This work, however, is not meant to be any sort of guidance on Big Data, machine translation, or a combination of both; the reader is advised to treat the material as an opinion piece.
A novel HBT distributed amplifier design topology based on attenuation compensation techniques

We report on a novel HBT distributed amplifier (DA) design which achieves the highest gain-bandwidth product (GBP) per device f/sub T/ so far reported for HBT distributed amplifiers. This paper introduces a new design topology for HBT DA's which incorporates attenuation compensation on both the input and output transmission lines. A four-section HBT DA using this novel topology achieves a gain of 15 dB and a 3-dB bandwidth of >15 GHz. The resulting gain-bandwidth product is 84 GHz. When normalized to the device f/sub T/, this DA achieves the highest normalized gain-bandwidth-product figure of merit for HBT DA's, /spl ap/3.67, which is a 55% improvement over existing state-of-the-art performance. The new device configuration offers 15-20 do more available gain for the device unit cell and results in gain-bandwidth product improvements of 200% over a conventional common-emitter DA configuration. <<ETX>>
Development of Parallel Restoration Strategy for Chongqing Power System in China

This paper applies the community detection theory and greedy algorithm to develop a practical parallel restoration strategy for the power system in Chongqing, China. The system is first partitioned by a modified label propagation algorithm considering the system topology and operation before a blackout. With the purpose of the shortest restoration time, all subsystems are restored simultaneously and independently by the greedy algorithm. Comparison study results show that the proposed strategy uses less time than the present strategy. It is helpful for enhancing the system reliability and reducing the loss.
Detection of Pavement Cracks Based on Non-local Image Denoising and Enhancement

Non-local method is a very effective method to eliminate Gaussian white noise in images, but the pavement image noise is usually non-Gaussian. In this paper, a non-local based method of denoising and enhancement of pavement crack images is proposed. First, morphological corrosion operator is used to convert the image noise of pavement into near-Gaussian noise; Second, using an improved (Block-match 3D Filtering) BM3D denoising and enhancement method to denoise and enhance pavement images; Finally, the remaining noise is removed by block cosine transform combined with gradient value. The experimental results show that the proposed method can achieve ideal pavement cracks detection results.
Single layered swastika‐shaped flexible linear‐to‐circular polarizer using textiles for S‐band application

A new swastika‐shaped single layered flexible linear‐to‐circular polarizer is proposed for Cube Satellite application in S‐band. The proposed linear‐to‐circular polarizer is designed fully using ShieldIt Super conductive textile on a lightweight felt substrate. Principle of operation is discussed comprehensively with an equivalent circuit model and its interaction with different orthogonal polarized wave components along with a succinct study of surface currents in different polarizations. Another linear‐to‐circular polarizer implemented on Polydimethylsiloxane (PDMS) substrate is designed and compared with the textile‐based design. A preliminary deployment scheme is also proposed and studied by analyzing its performance under different bending conditions. Both textile and PDMS structures are confirmed to be operational within the range of 2.39 to 2.45 GHz. The textile polarizer is then prototyped and validated to be exhibiting similar conversion efficiency (CE) of 98% (simulated) and 93% (measured) at 2.45 GHz. Moreover, the fractional bandwidth defined by their 90% CE limits are 7.35% (from 2.36 to 2.55 GHz) in simulations and 6.6% (from 2.36 to 2.52 GHz) in measurements. The resulting axial ratio produced is 1.4 dB (in simulations) and 2 dB (in measurements), indicated its suitability for S‐band application.
Energy-Efficient Prefix Code Based Backscatter Communication for Wirelessly Powered Networks

Backscatter communications have been widely applied in wirelessly powered networks. Energy constraint forces the nodes to only backscatter a few bits once, causing backscatter communications unable to be applied in the applications that require to deliver more data (e.g., an image). It is significant to save energy for backscatter communications so that the nodes can deliver more data with the limited energy. In this letter, the energy-efficient code based backscatter communication (CBBC) is proposed, which makes use of the energy consumption disparity between transmitting/receiving bit 0 and bit 1 in the existing backscatter communications. The energy-efficient prefix codebook is derived from the formulated energy consumption minimization problem. In the CBBC, the codebook is shared by the sender and the receiver of a backscatter link, and the sender breaks the original bit stream into equal-length blocks and delivers the energy-consuming blocks by using their corresponding codewords from which the receiver decodes the original data. The experiments show that the proposed CBBC can save energy for backscatter communication.
Towards Multimodal Learning for Android Malware Detection

As the worldwide adoption of smartphones nears complete saturation, the complexity and volume of mobile malware continue to progress, subverting what has become the largest set of targets with the most valuable rewards. At the same time, deep learning has become a powerful tool for data analysis and prediction, demonstrating significant efficacy in the detection of new and unforeseen malicious software through supervised learning. Despite these advances, the proper application of deep learning to malware detection could be further improved through additional data and model construction. In this work, we consider the detection of Android malware using static analysis techniques on multiple extractable data classes. Particularly, both Permission and Hardware Feature data are applied in various multimodal input scenarios and deep network shapes. Through experimental analysis, we demonstrate that the combination of both sets of data could improve overall performance, achieving as high as 94.5% classification accuracy. In addition, using only a limited grid search, we observe that the largest multimodal network requires the lowest time to train while achieving equivalent or greater accuracy compared with our other models.
EEG-Based Person Authentication Method Using Deep Learning with Visual Stimulation

Biometric authentication methods, such as fingerprint, used nowadays still have numerous potential security concerns and can be forged physically. To mitigate this issue, recent research has established non-physical signals such as brainwaves as viable sources for secured person authentication. As the brain signal has been found to be unique for individuals, electroencephalogram (EEG) signals can be potentially utilized as an identity discrimination tool, especially since EEG reflects the delicate difference in individuals' mental characteristics. However, the existing EEG-based authentication methods not only apply one or few techniques to stimulate the signals but also there are some vulnerabilities in the limited scope of the study. Therefore, we propose to combine steady-state visual evoked potential (SSVEP) and event-related potential (ERP) features to discriminate the distinction between individuals and apply the Long Short-Term Memory (LSTM) network for the analysis. The proposed methodology is divided into three stages. Firstly, we collect raw EEG data from the 20 human subjects, whose brains were stimulated by 7.5 Hz square SSVEP with targeted and non-targeted Snodgrass-Vanderwart's set of images as ERP stimulus. Then, the raw data is pre-processed by a notch filter, band-pass filter, and eye blink artifacts removal. The LSTM architecture is used to process data of individuals for predicting the results. After prediction, the performance is evaluated based on False Acceptance Rates and False Rejection Rates. The EEG-based authentication method with visual stimulus demonstrates high verification accuracy and can be applied with some improvement in the future through the use of brain connectivity techniques.
Green Internet of Things: A Survey of Enabling Techniques

Pervasive computing which enables manipulation, storage and communication amenities to appear everywhere and anytime is a promising paradigm in human's daily life. It requires seamless assimilation and interaction of complex and heterogeneous systems under the umbrella of the Internet of Things. Enormous devices used in Internet of Things sense, gather and communicate important information from their environments. This exchange of large volume of data among billions of connected devices demands massive energy and thus has led to an increase in the carbon footprint. Therefore, new energy efficient architectures with the reservation of renewable energy resources are progressively drawing lot of efforts to attain an ecologically balanced “Green” world. Green Internet of Things visualizes the concept of minimizing the energy consumption by devices. This paper reviews the need and requirement of Green Internet of Things. It explores and discusses efficient utilization of various enabling techniques to achieve Green Internet of Things. The paper also evaluates the strategies such as devising energy efficient data centers, energy efficient transmission of data from sensors, and implementation of energy efficient policies that promise to reduce the energy consumption. Further, the paper critically evaluates existing strategies for Green Internet of Things and recommends few to attain this initiative.
Privacy-Preserving Device Discovery and Authentication Scheme for D2D Communication in 3GPP 5G HetNet

Device-to-device (D2D) communication as a direct communication technology has many application scenarios and plays a very important role in the 5G era. Using D2D communication in 3GPP 5G Heterogeneous Network (HetNet) can effectively relieve the network traffic pressure and reduce the energy consumption of the base station. However, there are numerous security threats in D2D application since the introduction of D2D communication into the 3GPP 5G Het-Net still remains in the early stage. It is urgent to design a mutual authentication and key agreement protocol between two heterogeneous User Equipments (UEs) with privacy preserving and device discovery. The existing standards and solutions rarely take the heterogeneous access scenarios, privacy protection, and device discovery into consideration. In this paper, we propose a unified privacy protection device discovery and authentication mechanism for heterogeneous D2D UEs using the identity-based prefix encryption and ECDH techniques. Our proposed scheme can be applied to all of the 5G heterogeneous access scenarios of D2D communication. The security analysis and performance results show that our scheme can achieve the mutual authentication, key agreement, identity privacy protection and resist several protocols attacks with ideal efficiency.
Mathematical Analysis for Calculation of an optimum Tilt Angle of Solar Panels for Islamabad

Solar energy is a clean and abundant renewable source of energy. PV modules are the means to generate electrical power form it. The efficiency of PV modules is maximum only when the angle between the panels and sunlight is 90o. As the position of sun over a specific geographical area changes throughout the year, therefore, if we fix the PV modules on a specific angle, the output would not be maximum. The panels are needed to be tilted according to the optimum tilt angles from time to time in order to maximize their efficiency. In this work, the optimum tilt angles for Islamabad, Pakistan region are calculated and presented accordingly.
Fabrication of “18 Weave” Muscles and Their Application to Soft Power Support Suit for Upper Limbs Using Thin McKibben Muscle

In this letter, we propose a novel active weave named “18 Weave” using a thin McKibben muscle and report on the application to a prototype soft power support suit for the upper limb. The active weave utilizes the flexibility of the thin McKibben muscle, which is used for warps and wefts. From the proposed structure, we confirmed that a 19.4% additional displacement is achieved compared to a single artificial muscle. The flexible and lightweight assistance suit can be realized with the active weave that has advantages of flexibility and shape adaptability. We confirmed the compliance and fatigue alleviation effect of the suit. Measurements of the myopotentials confirmed significant decreases in the activities of the anterior deltoid, posterior deltoid, biceps brachii, and triceps brachii muscles in their integrated electromyogram (iEMG).
Sacral nerve modulation for faecal incontinence: influence of age on outcomes and complications. A multicentre study

Faecal incontinence is frequent in the elderly. Little is currently known about the efficacy of sacral nerve modulation (SNM) in the elderly. The present study aimed to assess the impact of age on the outcome of SNM and on the surgical revision and explantation rates by comparing the results of a large data set of patients.
Deep Learning Approach for Intelligent Intrusion Detection System

Machine learning techniques are being widely used to develop an intrusion detection system (IDS) for detecting and classifying cyberattacks at the network-level and the host-level in a timely and automatic manner. However, many challenges arise since malicious attacks are continually changing and are occurring in very large volumes requiring a scalable solution. There are different malware datasets available publicly for further research by cyber security community. However, no existing study has shown the detailed analysis of the performance of various machine learning algorithms on various publicly available datasets. Due to the dynamic nature of malware with continuously changing attacking methods, the malware datasets available publicly are to be updated systematically and benchmarked. In this paper, a deep neural network (DNN), a type of deep learning model, is explored to develop a flexible and effective IDS to detect and classify unforeseen and unpredictable cyberattacks. The continuous change in network behavior and rapid evolution of attacks makes it necessary to evaluate various datasets which are generated over the years through static and dynamic approaches. This type of study facilitates to identify the best algorithm which can effectively work in detecting future cyberattacks. A comprehensive evaluation of experiments of DNNs and other classical machine learning classifiers are shown on various publicly available benchmark malware datasets. The optimal network parameters and network topologies for DNNs are chosen through the following hyperparameter selection methods with KDDCup 99 dataset. All the experiments of DNNs are run till 1,000 epochs with the learning rate varying in the range [0.01–0.5]. The DNN model which performed well on KDDCup 99 is applied on other datasets, such as NSL-KDD, UNSW-NB15, Kyoto, WSN-DS, and CICIDS 2017, to conduct the benchmark. Our DNN model learns the abstract and high-dimensional feature representation of the IDS data by passing them into many hidden layers. Through a rigorous experimental testing, it is confirmed that DNNs perform well in comparison with the classical machine learning classifiers. Finally, we propose a highly scalable and hybrid DNNs framework called scale-hybrid-IDS-AlertNet which can be used in real-time to effectively monitor the network traffic and host-level events to proactively alert possible cyberattacks.