Miracle-dz/UniFAR-FTUs huggingface dataset:
Ontology Based Web Page Classification System by Using Enhanced C4.5 and Naïve Bayesian Classifiers

Today, web is a huge repository of information which needs for accurate automated classifiers for Web pages. Classification of Web page is essential to many tasks in Web information retrieval such as maintaining, web directories and focused crawling. So, this system proposes as the web page classification system based on semantic logic. For semantic, this system uses the ontology that stores each concept of each word. For classification, this system proposes the enhanced C4.5 decision tree and Naive Bayesian (NB) classifiers. In the original C4.5 classification algorithm, the traditional entropy measure is unable to measure the appropriateness of nodes when the class labels are the same. By using semantic technology, this system can effectively support to classify web pages into each category. To show the effectiveness, this system is tested by using HTML documents in the computer science domain.
Differential Multi-Layer Compact Grid Antenna Array for 79 GHz Automotive Radar Applications

A novel wide band compact grid antenna array is developed for 79 GHz MIMO radar applications. The key issue is the stable radiation pattern with flat gain characteristic along the whole frequency bandwidth, which is very advantageous for the targeted radar applications. The antenna is taped out with the new high resolution multi-layer PCB technology, called "Any-Layer PCB". This technology provides the possibility to stack micro-vias in PCB boards and reduce the fabrication cost compared to other multi-layer technologies in mm-wave bands. With this feature, the width of the array has been compacted with 17.2%, which leads to a higher side lobe suppression and an enhancement of the field of view in radar applications. A combined impedance and 3-dB gain band width of 9.6% is achieved. The gain and SLL are 13.52 dB and −14 dB at 79 GHz, respectively.
Multimodal Face Spoofing Detection via RGB-D Images

While it has been shown that using 3D information might significantly benefit face anti-spoofing systems, traditional color images are still generally used, due to several issues such as expensive hardware requirement, high time cost, or poor accessibility when obtaining and using true 3D images. Thus, we could use RGB-D images captured by relatively low cost sensors instead, e.g., Kinect cameras, to achieve better performance without consuming huge amount of time or money. This research presents a novel multimodal face anti-spoofing method, which makes full use of available information on RGB-D images and no manually chosen regions are needed. For every pair of RGB-D images, first of all, we calculate the correlation between color and depth images to detect multimodal properties; then, by analyzing the consistency of sub regions extracted from the depth image, we are able to distinguish flat spoofing faces from genuine human beings. Both anti-spoofing features are fused to make final anti-spoofing decisions. Experiments on both self-collected and pubic 3DMAD datasets show that our proposed approach is effective for intra-dataset and cross-dataset testing scenarios, and that our method could deal with different presentation attacks carried by photos, tablet screens, and face masks.
Semi-supervised Rare Disease Detection Using Generative Adversarial Network

Rare diseases affect a relatively small number of people, which limits investment in research for treatments and cures. Developing an efficient method for rare disease detection is a crucial first step towards subsequent clinical research. In this paper, we present a semi-supervised learning framework for rare disease detection using generative adversarial networks. Our method takes advantage of the large amount of unlabeled data for disease detection and achieves the best results in terms of precision-recall score compared to baseline techniques.
Masked Label Learning for Optical Flow Regression

Optical flow estimation is a challenging task in computer vision. Recent methods formulate such task as a supervised-learning problem. But it often suffers from limited realistic ground truth. In this paper, a compact network, embedded with cost volume, residual encoder and deconvolutional decoder, is presented to regress optical flow in an end- to-end manner. To overcome the lack of flow labels, we propose a novel data-driven strategy called masked label learning, where a large amount of masked labels are generated from the FlowNet 2.0 model and filtered by warping calibration for model training. We also present an extended-Huber loss to handle large displacements. With pretraining on massive masked flow data, followed by finetuning on a small number of sparse labels, our method achieves state-of-the-art accuracy on KITTI flow benchmark.
Detecting Phishing Websites and Targets Based on URLs and Webpage Links

In this paper, we propose to extract features from URLs and webpage links to detect phishing websites and their targets. In addition to the basic features of a given URL, such as length, suspicious characters, number of dots, a feature matrix is also constructed from these basic features of the links in the given URL's webpage. Furthermore, certain statistical features are extracted from each column of the feature matrix, such as mean, median, and variance. Lexical features are also extracted from the given URL, the links and content in its webpage, such as title and textual content. A number of machine learning models have been investigated for phishing detection, among which Deep Forest model shows competitive performance, achieving a true positive rate of 98.3% and a false alarm rate of 2.6%. In particular, we design an effective strategy based on search operator via search engines to find the phishing targets, which achieves an accuracy of 93.98%.
The Influence of Community Interactions on User Affinity in Social Networks: A Facebook Case Study

With the advent of social media, one of the biggest concerns has been its suspected impact on democracy by influencing users' opinions during elections. The accusations vary from the propagation of fake news to concerns about Facebook having unfettered power over its users' content. As a first step towards understanding the true nature of social media's influence, we started collecting data prior to Local Election in Sri Lanka, 2018 and continued the collection until the election was over; we collected the data through Facebook API and all of the data is completely anonymized. The dataset covers 44K users from Sri Lanka and their interactions with 44 Facebook groups. The dataset also includes all of the associated events related to each group. As a preliminary step, we have analyzed how the user affinity surrounding these groups have changed surrounding the period of the local election. Our analysis also gives a concrete and quantitative evidence of how users are communicating on Facebook and how active they are surrounding a sensitive event. Further, there is a significant change of affinity of a set of individuals over the time corresponding to the external event. We believe our analysis is the first of its kind in providing scientific analysis of the influence of social media in Sri Lanka.
High-Efficiency Bidirectional Three-Phase LCC Resonant Converter With a Wide Load Range

This paper details the design and implementation of an efficient 4-kW bidirectional converter with a wide load range, based on a three-phase LCC resonant converter operated in a continuous conduction model, which has a number of advantages pertaining to a bidirectional power supply. The voltage boost-up function yields a high voltage gain without increasing the transformer turn ratio. The high switching frequency, which is achievable by using the soft-switching condition and multiphase operation, decreases the output ripple and helps minimize the size of the output filter, as the requirement for a filter changes after the power flow. Moreover, this reduction in the size of the filters yields economic advantages. In addition, because of the synchronous rectifier operation employed during switching, the conduction loss in the rectifier diode of the proposed converter is low. The direction of the output power can be changed easily, using phase control. The implemented power converter is connected to a 320-V rated generator on one side and 28-V lead-acid batteries on the other side. The functionality of the proposed converter is verified in experiments. We confirmed the high performance of the developed converter in terms of efficiency (92.2%), operable load range (0–12.5 A in step-up, and 0–142 A in step-down), and voltage ripple (0.05%).
Using SGX-Based Virtual Clones for IoT Security

Widespread permeation of IoT devices into our daily lives has created a diverse spectrum of security and privacy concerns unique to the IoT ecosystem. Conventional host and network security mechanisms fail to address these issues due to resource constraints, ad-hoc network models and vendor-centric data collection and sharing policies. Hence, there is a need to redesign the IoT infrastructure to secure both the device and the data. To this end, we propose a design where users are in the driving seat, devices are less exposed and data sharing models are flexible and fine-grained. Our proposal comprises hardware-secured data banks based on Intel Software Guard Extensions (SGX) to house the data in clouds without the need to trust the cloud provider. Virtual clones (shadows) of devices running on top of these data banks serve as competent proxies of actual IoT devices hiding away device weaknesses. The proposed infrastructure is scalable and robust and serves as a good first step for the community to build on and improve.
A User Comfort Model and Index Policy for Personalizing Discrete Controller Decisions

User feedback allows for tailoring system operation to ensure individual user satisfaction. A major challenge in personalized decision-making is the systematic construction of a user model during operation while maintaining control performance. This paper presents both an index-based control policy to smartly collect and process user feedback and a user comfort model in the form of a Markov decision process with a priori unknown user-specific state transition probabilities. The control policy utilizes explicit user feedback to optimize a reward measure reflecting user comfort and addresses the explorationexploitation trade-off in a multi-armed bandit framework. The proposed approach combines restless bandits and upper confidence bound algorithms. It introduces an exploration term into the restless bandit formulation, utilizes user feedback to identify the user model, and is shown to be indexable. We demonstrate its capabilities with a simulation for learning a user's trade-off between comfort and energy usage.
Research on High-Fidelity Router Emulation Technologies Based on Cloud Platform

A cloud platform that incorporates virtualization technologies, and SDN technologies has the advantages of strong scalability and a high fidelity of host node emulation. However, this type of cloud platform is not designed specifically for network emulation, and the shortcoming of low fidelity remains in router emulation. For this reason, based on the cloud platform, this paper combines virtualization technology with router software technology, proposes a programmable router emulation method that supports multiple routing protocols, and designs a highly realistic congestion control strategy emulation scheme. Experiments show that the proposed router emulation scheme can support the emulation of multiple routing protocols while maintaining high fidelity in virtual router congestion control.
Direct Input Power Control for Drive System of Single-Phase to Three-Phase Power Converter Without Electrolytic Capacitor

The electrolytic capacitor is the key component that not only lowers the reliability of single-phase to three-phase power converter for drive system, but also makes high current harmonics at the AC grid. Recently, the drive system with a film capacitor has been researched for its long lifetime and the characteristics of small volume and low cost. However, the input power factor is reduced and the AC input current waveform is distorted when the motor is driven at light load conditions. In order to deal with this problems, the direct input power control method that related to the AC input voltage and current is proposed in this paper. Furthermore, in order to keep the dc-link voltage higher than the inverter output line-to-line voltage, the flux-weakening control that varies synchronously with the double frequency of the AC input voltage is adopted. The results verified the correctness and effectiveness of the proposed method.
Thermal-Impact-Based Protection of Induction Motors Under Voltage Unbalance Conditions

Voltage unbalance and mechanical overload generate negative effects on induction motors, producing thermal damages in the stator insulation and a reduction of the motor lifetime. In this regard, the development of protection devices is crucial as they can help to maintain the integrity of the motor and avoid irreversible damages such as insulation system breakdown, short-circuits, and so on. In this work, a methodology to obtain a time-protection model for induction motors from voltage unbalance is presented. The methodology is based on the thermal impact on the motor produced by mechanical overload and voltage unbalance conditions. In general, it consists of the following steps: i) induce in the motor different overloads and voltage unbalance levels and monitor their thermal profiles at the stator winding, ii) obtain the time–overload curve of the motor, iii) determine both the thermal level according to both the time-overload curve and the overload thermal profile, and iv) estimate the parameters of the time-unbalance model using both the thermal level obtained and the unbalance thermal profiles. The model is validated through its implementation in an online protection scheme, the results show a similar behavior between overload and unbalance protections in the safe thermal level defined by the protection models. The protection scheme is carried out over a 750 W three-phase induction motor under different overload and voltage unbalance operating conditions.
Text Keyword Extraction Based on Meta-Learning Strategy

Keyword extraction is an important content in many fields and it is a key step to achieve document retrieval, information retrieval, scientific and technological literature indexing, news reading, text clustering and classification, Machine Translation and so on. In order to improved the accuracy of keyword extraction for text, we put forward a framework of keyword extraction based on meta-learning. This framework not only integrates the keyword extraction algorithm selection, parameter adjustment algorithm, but also integrates a variety of algorithms. Experimental results show that keyword extraction based on meta learning is not only simple, but also significantly improved the accuracy of keyword extraction.
Performance of Full Duplex Relaying with Dual Antenna and Soft Combining

The performance of a full-duplex (FD) relaying system is investigated in this paper. The relay is equipped with two antennas (both are trans-receiver). The energy constrained relay is powered by RF energy harvesting. A time-switching architecture is adopted to make communication between a source and a destination. Information received at the destination from both the trans-receiver is combined using a soft combining technique, i.e., Maximal Ratio Combining (MRC). The performance of two protocols, decode-and- forward (DF) relaying and amplify-and-forward (AF), are also studied and compared. It is found that use of double antenna instead of single antenna based transmission at the relay improves the performance significantly. The impact of MRC on the throughput performance is noticeable.
Electrically Small, Low-Profile, Planar, Huygens Dipole Antenna With Quad-Polarization Diversity

An electrically small, low profile, planar, Huygens dipole antenna with four reconfigurable polarization states is presented. The design incorporates both electric and magnetic near-field resonant parasitic elements and a reconfigurable driven element. The four polarization states include two orthogonal linear polarized (LP) and two circular polarization (LHCP and RHCP) states. A 1.5 GHz prototype was fabricated (partially with 3-D additive manufacturing), assembled, and tested. The measured results, in good agreement with their simulated values, demonstrate that even with its simple configuration, electrically small size (<inline-formula> <tex-math notation="LaTeX">$ka =0.944$ </tex-math></inline-formula>), and low-profile height (<inline-formula> <tex-math notation="LaTeX">$0.0449\lambda _{0}$ </tex-math></inline-formula>), this reconfigurable Huygens antenna possesses stable broadside radiation performance in all of its four polarization states. The measured results demonstrate that in its <inline-formula> <tex-math notation="LaTeX">$x(y)$ </tex-math></inline-formula>-LP state, the peak realized gain, front-to-back ratio, and radiation efficiency values are, respectively, ~3303 dBi (2.97 dBi), ~10.7 dB (9.9 dB), and ~68.2% (67.5%). For the LHCP (RHCP) states, they are, respectively, ~2.82 dBi (2.74 dBi), ~11.4 dB (12.5 dB), and ~67.1% (65.9%).
Connectivity Modulations induced by Reaching&Grasping Movements

Functional neuroimaging enables the assessment of the brain function in both rest and active conditions. While traditional functional connectivity studies focus on determining distributed patterns of brain activity, the analysis of pair-wise correlations in the time series associated to brain regions allows a paradigm shift to graph theory making available a whole set of parameters for the analysis of the functional network. Then, the study of the properties of the networks as well as of their modulations can be performed in the space of the so-identified features potentially leading to the detection of condition-specific (static or dynamic) fingerprints. Following this guideline, this study is a first attempt to using graph-based measures for capturing task-specific signatures of a reach&grasp movement. The weighted clustering coefficient (CW), characteristic path length (SW) and small-worldness (SW) were considered and performance was assessed against classical measures (event-related (de)synchronization). Neurophysiological data were collected through high-density EEG and a stereophotogrammetric system was used for capturing the onset and end of the movement. Though not reaching statistical significance, these preliminary results witness the modulation of the function network due to reach&grasp and provide evidence in favour of the possibility of capturing such a modulation through graph-based properties. This would allow to shed light on the movement-induced reorganization of the network, which has a clear translational impact for the assessment of the recovery of patients after acute stroke.
Connecting With Fundamental Mathematical Knowledge Directly: The Organizational Features of Good Mathematical Cognitive Structure

This paper reported on the study of a good mathematical cognitive structure (GMCS) based on 43 top university students and 82 concepts of Calculus materials, using the social network analysis method. The results indicated that the GMCS has the following organizational features: (1) The mathematical knowledge (MK) in GMCS interconnected widely, especially in MK with a higher connection tightness; (2) Most connections between MK were direct; (3) MK of the basic and higher inclusive level had a greater impact; and (4) There were multiple MK accumulation points connecting others to form subsets. These new findings enrich the results of previous GMCS studies and promotes further exploration of GMCS. In view of this, teachers should pay closer attention to basic and abstract MK and help their students construct various direct connections of the MK in their mind.
A Compact Broadband Circularly Polarized Slot Antenna With Two Linked Rectangular Slots and an Inverted-F Feed Line

In this communication, a broadband circularly polarized slot antenna with an inverted-F-shaped microstrip feed line and two linked rectangular slots in the ground plane is proposed. This antenna offers a wide 3 dB axial ratio (AR) bandwidth in a compact size (previously proposed slot antennas with a similar AR bandwidth are almost double in size) without employing vias or other passive components. Measured AR bandwidth is 83.5% (4.3 and 3–7.3 GHz) which is completely overlapped by the measured <inline-formula> <tex-math notation="LaTeX">$S_{11}$ </tex-math></inline-formula> bandwidth of 92.6% (5 and 2.9–7.9 GHz).
Enterprise Architecture Assimilation: An Institutional Perspective

Enterprise architecture (EA) has long been propagated in information systems research as an approach for guiding diverse local stakeholders toward a common holistic perspective. Despite its maturation over the past decades, organizations still encounter institutional obstacles with realizing EA’s intended outcomes. Literature addressing this challenge mainly understands EA as an exogenous phenomenon that needs to be brought into the organization. In the paper at hand, we aim to go one step further. We focus on EA assimilation by studying the influence of institutional pressures that make EA part of the organization’s worklife and thus contribute to EA’s intended outcomes. By capturing all institutional pressures through which EA may become an inherent part of the organization’s worklife, we empirically confirm their influence on EA assimilation and EA outcomes. In addition, we find the engagement of local organizational stakeholders to sig-nificantly mediate the relation between institutional pressures and EA assimilation.
Development of a Sound Recognition System Using STM32 Microcontrollers for Monitoring the State of Biological Objects

The analysis of the main trends in the development of automation in the field of beekeeping, in particular the use of modern network technologies, sound recognition technologies for monitoring the state of bee-families. Considered the main existing recognition technologies, requirements for the hardware of monitoring systems. The possibility of implementing a prototype based on the Arduino platform with Wi-Fi modules ESP8266, advantages and disadvantages of this solution was considered. As a result, the conclusion of the feasibility of using the 32-bit STM32 microcontroller with a core ARM Cortex-M4, and for developing a prototype device - the STM32F4DISCOVERY evaluation board. The features of development of projects on STM32, a choice of software for development are also considered.