Miracle-dz/UniFAR-FTUs huggingface dataset:
Are Internet Tunnels Worthwhile?

Despite a significant increase in capacity of the Internet regional congestion remains an issue at certain times of day. Dimensioning the system to provide minimal delay under these transient conditions would be uneconomical, particularly as various forms of application data are more or less sensitive to these delays, as are different end-users. We therefore investigate a scheme that allows end-users to selectively exploit a sequence of mini tunnels along a path from their origin to a chosen destination. We assume the availability of such tunnels is advertised centrally through a broker, with the cooperation of the Autonomous System (AS) domain operators, allowing end-users to use them if so desired. The closest analogy this scheme is that of a driver choosing to use one or more toll roads along a route to avoid potential congestion or less desirable geographic locations. It thus takes the form of a type of loose source routing. Furthermore, the approach avoids the need for inter-operator cooperation, although such cooperation provides a means of extending tunnels across AS peers. In this paper we ascertain the benefit in terms of delay for a given degree of tunnel presence within a portion of the Internet. The expectation is that a relatively small number of tunnels may be sufficient to provide worthwhile improvements in performance for some users at least.
Identification of Cell Pathology by Using Stacked Auto-Encoders Combination with Rotation Forest

Cytopathology is an effective method for obtaining pathological diagnosis in clinic. Because of its safety and convenience, it has been widely used in clinical diagnosis. The classification and recognition of cell pathology images by computer can provide effective diagnostic basis for medical personnel and improve the accuracy and efficiency of diagnosis. Therefore, it is of great significance to use the computational method to identify the pathological image of the cell. In this paper, we propose a new method to identify cell pathology images based on the depth learning algorithm. First, we standardize the processing of cell pathology images, and get images that are suitable for computer algorithm; then extract the advanced features automatically by using automatic encoders; finally use rotation forest classifier to identify the extracted features. In the experiment, we use 5-fold cross-validation method to ensure the stability of the experimental results. Excellent experimental results show that our model can effectively identify cell pathology images, and provide quantitative and objective diagnostic evidence for medical staff.
Delay-Aware Task Offloading in Shared Fog Networks

Offloading computation tasks from resource-poor end devices to powerful backend clouds has become a prevalent solution thanks to the rapid development of cloud computing. However, modern Internet of Things applications, such as augmented reality and real-time monitoring, bring stringent delay requirements to the computation tasks in the device-to-computing-facility communications. To better accommodate the delay requirements of the computation tasks, the recently proposed fog computing architecture suggests that these computation tasks can be extensively offloaded to the distributed computation facilities along the cloud-to-things continuum. These computation facilities, including central clouds and the computation facilities standing at the network edge, jointly form an overlay network, named a fog network, to provide fog computing services for end devices. This paper targets a practical and efficient scheme to schedule tasks with heterogeneous delay sensitivities in a shared fog network. A mathematical model is first constructed to capture the major characteristics of a fog network. The model enforces lexicographic max–min fairness, an enhanced metric compared to conventional max–min fairness. The task offloading problem is modeled as an integer nonlinear program. An efficient and exact solution method is proposed based on problem-specific analysis. Finally, synthesized-trace-driven simulations demonstrate the efficacy of our proposed offloading scheme.
High-Accuracy Scene Recognition and Its Application to Highly-Safe Intelligent Systems

It is expected that a highly-safe intelligent system which makes risk avoidance in the real-world environment where human lives. In order to detect dangerous situations, it is indispensable to accurately recognize risk events from the input image information, i.e., high-accuracy scene recognition. In this paper, it is clarified that the high-accuracy scene recognition can be achieved by combination of the convolution neural network which extracts the feature of an input image and the support vector machine for classification. In the scene recognition of a single danger event, it is not possible to recognize multiple event occurrences (multi-class classification). We propose a method to realize multi-class classification without decreasing the recognition accuracy by selectively using an individual classifier generated for each event.
Bi-Static Time Domain Study for Microwave Breast Imaging

In this paper a BAVA (Balanced Antipodal Vivaldi Antenna) antenna is designed and optimized to be used in direct breast contact to operate in the 3.1-10.6 GHz band. A bi-static study in the time domain is proposed to investigate the presence of the tumor. The tumor was first inserted in the fat tissues at 4 mm from the skin and then inserted deeper at 2 cm in the glandular tissues to validate the used approach. Finally a microwave breast image was constructed based on the recovered time domain signals for both cases.
Readiness of Japanese Elementary School Teachers to Begin Computer-Programming Education

“Programming education” will be compulsory in Japanese elementary schools beginning in 2020. However, implementation barriers include underusing information and communication technology (ICT) and overextended teachers. Here, we analyzed the current status of programming education in elementary schools and evaluated teachers' opinions on the subject. Information was collected by in-person questionnaires from in-service teachers at eight schools. The results showed that teachers generally underuse computers in teaching, which defeats the purpose of requiring programming education. Moreover, many teachers lack programming experience and are not experienced or confident in teaching programming, which could impede the implementation of programming education by the target date. These findings emphasize the need for teacher training to prepare them to provide effective programming education.
A Survey on Source Code Review Using Machine Learning

Source code review constrains software system security sufficiently. Scalability and precision are of importance for the deployment of code review tools. However, traditional tools can only detect some security flaws automatically with high false positive and false negative by tedious reviewing large-scale source code. Various flaws and vulnerabilities show specific characteristic in source code. Machine learning systems founded feature matrixes of source code as input, including variables, functions and files, generating ad-hoc label by distinguish or generation methodologies to review source code automatically and intelligently. Source code, whatever the programming language, is text information in nature. Both secure and vulnerable feature can be curved from source code. Fortunately, a variety of machine learning approaches have been developed to learn and detect flaws and vulnerabilities in intelligent source code security review. Combination of code semantic and syntactic feature contribute to the optimation of false positive and false negative during source code review. In this paper, we give the review of literature related to intelligent source code security review using machine learning methods. It illustrate the primary evidence of approaching ML in source code security review. We believe machine learning and its branches will become out-standing in source code review.
Optimal Sparse Channel Estimation for Unknown Sparse Level in Wireless OFDM Systems

Compressed sensing (CS)has been rapidly adopted as an improved solution to estimate sparse channels for OFDM systems. Nowadays, there exist many algorithms to estimate the channel under this approach. The most popular algorithms belong to the greedy algorithms category. OMP is one of the most useful algorithms due its low computational complexity and good performance. However, the accuracy in this algorithm depends directly on the stopping condition. Currently, there exist many works which try to tackle this problem developing a stopping condition by a threshold. On the other hand, some others works assume this condition known, however in a real scenario it is not true. In this work a simple strategy is proposed permitting to detect the sparse level of the wireless channel and apply it to obtain an optimal solution reducing the MSE in the OFDM channel estimation.
Finite-Time Continuous Robust Generation of Self-Oscillation: A Switched Approach

A continuous robustification methodology for the generation of self-oscillations using continuous switched integral sliding modes is presented. The robustification is achieved in finite time, assuring the frequency and amplitude of the oscillations remain despite the presence of Lipschitz matched uncertainties/perturbations. The sliding modes gains are designed to assure the convergence before the first switching under the assumption that the uncertainties/perturbations are continuous at the switching time. To illustrate the efficacy of the proposed approach, the designed methodology is applied to a Furuta Pendulum.
A Wide Stopband Filtering Patch Antenna and its Application in MIMO System

This communication presents a wide stopband filtering antenna element and its application in multi-input multi-output (MIMO) system. Microstrip terminated coupled lines and a rectangle patch are utilized to generate the filtering function. By varying the length of the parallel coupled lines and open-circuited stubs, multiple transmission zeros can be introduced in the spurious band. Appropriate positioning the location of these transmission zeros enables a wide harmonic suppression. To demonstrate the proposed method, a wide stopband filtering antenna is designed and further applied in a four-element MIMO array. The measured and simulated results agree well. The proposed MIMO antenna obtains an attenuation of 12.6 dB up to $6.6\times $ the center frequency.
Deep Residual Learning-based Reconstruction of Stacked Autoencoder Representation

Stacked autoencoder (SAE) can efficiently represent high dimensional data with low dimensional features via minimizing a reconstruction error. However, the decoder of SAE cannot achieve lossless recovery of original data. If the reestablishment performance of SAE decoder is improved, it can be employed for low-bitrate and high-quality data compression. This paper adopts deep residual learning of convolutional neural network (CNN) to promote the reconstruction capability of SAE decoder. It is demonstrated by experimental results that deep CNN is superior to SAE decoder in rebuilding the performance of image data.
Chiral SRR Metasurfaces for Circular Polarisation Conversion

A circular polarization dual band metamaterial polarization rotator composed of two double split ring resonators rotated 90° with respect to each other is presented. Asymmetric transmission between $T_{xx}$ and $T_{yy}$ and symmetric transmission between $T_{xy}$ and $T_{yx}$ has been achieved. The circular polarization is caused by the symmetric transmission between $T_{xy}$ and $T_{yx}$. The design has been demonstrated numerically at microwaves frequency. The structure holds promise for ultra-compact polarizing devices for any frequency range given the scalability of the approach.
Implementation of the Standardized Human Body Communications - A Feasibility Study

Due to the disparity of the measured human body path losses published in the literature, it is proposed here to make a feasibility study about the implementation of the Human Body Communications (HBC) defined in the IEEE 802.15. 6 standard. In this way, the path loss required by the standard is firstly computed. Next, a measurement setup of the human body path loss is presented. Finally, measurement results are compared to the required one and pave the way to studies closer to real use cases.
Design and Practice of Exploratory Virtual Experiment in Physics Discipline

Exploratory experiments are fundamental to physics courses. Virtualization technologies like XR can enhance students' learning effectiveness with a suitable experimental instructional design. At present, the research on the theory and strategy of designing virtual experiment is relatively rare, especially in physics discipline. This paper concludes the design principles of exploratory virtual experiment. Physics experiment in the K-12 textbooks titled “polarization of light” has been chosen for its mapping into virtual domain. Finally, a preliminary assessment and result will be presented.
A Recommendation System using Interviewers Preferences for Ranked Candidate Selection

Selection of an appropriate candidate among a group of applicants by multiple interviewers require decision making. The present work presents the design details of a recommendation system that generates a ranked list of selected candidates. The proposed recommendation system considers interviewers preferences and the sequence in which they wanted their preferred candidates to appear in the final selection list. However, finding an agreement (maximum satisfaction) among multiple preferences and variation in ordering for preferred candidates makes the problem more challenging. The proposed system models the input into score vector, interviewers' preference matrix and interviewers' satisfaction matrix to compute the final ranked candidate list. The Hungarian Aggregated Method (HAM) and the Greedy Aggregated Method (GRAM) are used for computing the final decision vector. The proposed system was evaluated using Fleiss's Kappa and prototype application. Results shows a better and confident recommendations by the proposed system.
Fe/C Nanocapsule-Decorated Fe2B/C Nanocapsule Hybrids With Improved Gigahertz Electromagnetic Absorption Properties

We report an obvious improvement in gigahertz electromagnetic (EM) absorption properties in novel core/shell-structured magnetic/dielectric nanocapsule-decorated nanocapsule hybrids, featuring Fe/C nanocapsules of ~4 nm mean diameter decorated on the surfaces of Fe<sub>2</sub>B/C nanocapsules of ~50 nm mean diameter (denoted as Fe/C@Fe<sub>2</sub>B/C hybrids), as a result of the simultaneously enhanced dielectric and magnetic losses by an increased interfacial polarization at the Fe/C and Fe<sub>2</sub>B/C heterogeneous interfaces and an additional tip effect by the decoration of small Fe/C nanocapsules. The phase, morphology, microstructure, and magnetization of the Fe/C@Fe<sub>2</sub>B/C hybrids are investigated using various methods, and their EM absorption properties are evaluated in paraffin-bonded composites with 50 wt. % hybrids over the 2–18 GHz range. The results indicate a giant reflection loss (<italic>RL</italic>) of −49.5 dB at 10.5 GHz and a broad effective absorption bandwidth (for <italic>RL</italic> < −10 dB) of 8 GHz at a thin composite thickness of 2.1 mm. An extremely broad coverage of effective absorption bandwidth from 2.5 to 18 GHz is obtained at a very wide composite thickness range of 1–6 mm. The present study provides a new prospective for realizing high-performance EM absorbers at gigahertz frequencies.
A Game-Theoretical Modelling Approach for Enhancing the Physical Layer Security of Non-Orthogonal Multiple Access System

This paper investigates the physical layer security of a downlink non-orthogonal multiple access (NOMA) communication system, wherein a base station is communicating with two paired active users in the presence of an eavesdropper and multiple idle nodes (helpers). In order to enhance the secrecy performance, a two-phase harvest-and-jam null-steering jamming technique is deployed. In the first phase, the base station provides the helper with power in addition to active users and eavesdropper’s information via simultaneous wireless information and power transfer technique. The helpers exploit the harvested energy and the information received in the first phase to build a null-steering beamformer and jam the eavesdropper, during the information exchange between the base station and the legitimate users in the second phase. A game theory is introduced to the proposed scheme, and the base station-helpers interactions are modeled as a Stackelberg game, where the helpers play the leader role and the base station is the follower. The utility functions of both the leader and follower are formed, and the Stackelberg equilibrium is reached by means of the backward induction technique. The proposed scheme demonstrates better secrecy performance when compared with the artificial noise-aided secure NOMA system.
Crime Analysis Through Machine Learning

This paper investigates machine-learning-based crime prediction. In this work, Vancouver crime data for the last 15 years is analyzed using two different data-processing approaches. Machine-Learning predictive models, K-nearest-neighbour and boosted decision tree, are implemented and a crime prediction accuracy between 39% to 44% is obtained when predicting crime in Vancouver.
Construction of Equivalent Model of Patch Antenna Using Magnetic Dipole

This paper proposes a fast and efficient technique for constructing an equivalent model of patch antenna for predicting its radiation pattern. The equivalent model of the antenna is constructed based on its radiation properties. First, the initial model of the antenna is developed and then the arrangement of the initial model is optimized using FEKO optimization tool. The electric field of the magnetic dipole is determined based on numerical Green function derivation. The magnetic dipole is used to construct the equivalent model of patch antenna based on the radiation mechanism to predict its radiation pattern. Only two design parameters needed to be optimized making it more computational efficient. The derived radiation pattern from the equivalent model is validated with that of the original antenna model on FEKO to evaluate its efficiency. The simulation results show that the proposed equivalent model based on a magnetic dipole is fast and efficient and do not require the antenna's detailed material information.
Multi-Heuristic State Space Search

State space seek is a procedure utilized as a part of the field of software engineering, including counterfeit consciousness (AI), in which progressive designs or conditions of an occasion are considered, with the objective of finding an objective state with a coveted property. In spite of the very fact that this enterprise has varied applications as well as those for movement arranging, trying with design information heuristics, AND/OR charts then on. This main objective of this paper is to run Multi-Heuristic Planner on a 3D Environment. This framework is build on this believe that multiple searches along with different in-admissible heuristics will guild the path of an object to its desired goal in a way that will preserve completeness and guarantees on suboptimality bounds.
“Revyew” Hotel Maintenance Issue Classifier and Analyzer using Machine Learning and Natural Language Processing

Hospitality and tourism industry websites attract a lot of customers that book hotels on a regular basis. The modern trend to book hotels is through online websites due to the convenience and discounts offered. When a customer visits a hotel they usually post a positive or negative review about their experience on the respective booking website. Identifying maintenance related issues from these reviews is a major problem that even most large hotel chains face. There are many applications for customers related to that area (such as hotel aggregators) but there are only a very few applications for the hotel management to improve their workflow and provide a better service to the customers. This research is to explore a method to analyze hotel reviews and extract maintenance related problems and present them in a user-friendly manner for the hotel management to take the necessary action. It explores the use of machine learning techniques such as binary classifiers, multiclass classifiers along with natural language processing techniques such as sentiment analysis to extract maintenance related issues from text and categorize the issues. A publicly available data source of reviews was used to test and the results show that the SVM classifier performs best for both cases.
Sentiment Analysis using Token2Vec and LSTMs : User Review Analyzing Module

Sentiment analysis can be defined as the process of identifying the speaker’s attitude towards a particular subject. Sentiment analysis is very useful for businesses when it comes to monitoring and analyzing user reviews of their products. Because, it is capable of automatically classifying the overall opinion of customers of a specific product or a brand by analyzing the feedback that they have already given. Furthermore, companies can easily collect relevant user reviews from social media platforms such as Facebook, Twitter, blogger etc. Because of those reasons sentiment analysis has become a popular topic in natural language processing research areas. This paper proposes a Long Short-Term Memory based deep learning approach to perform sentiment analysis of social media user reviews on electronics domain. Specially this model is capable of analyzing the sentiment of reviews which consists emojis and social acronyms.
Industrial Internet of Things: A Systematic Literature Review and Insights

The connection of embedded computing devices via the Internet has dramatically changed the way people live. This concept has also been extended to the industrial sector. It not only provides a more reliable, real-time and secure communications, but also enables the smart factory concept in the fourth industrial revolution to be realized. However, in the current literature, there is still a lack of a formal and objective review that specifically focuses on this topic. This paper aims to address this gap. First, the applied systematic literature review method is explained. Findings and insights are then illustrated through the analysis of the collected data related to the four research questions. Finally, the strengths and limitations of the work are summarized.
Application of open Source Deep Neural Networks for Object Detection in Industrial Environments

Due to dynamics, flexibility and diversity in logistics, perception-controlled, intelligent robots are required to automate logistical handling steps. Due to the additional optical influences of the industrial environment, such as labeling or damage, these applications seem predestined for the use of generalizing deep neural networks (DNN). These showed continuous improvements over the last few years based on publicly available data sets. If these DNNs are re-trained based on training data from the industrial environment, a lower performance can be observed. The additional extension of the experiments to international locations of the vehicle plants also showed that a drop in performance can be observed in the implementation of a network trained in Germany, for example, when it is used in America. However, in order to be able to use such robots in the logistic processes in the future, further measures such as a revised composition of training data or their extension by data augmentation are proposed.