Miracle-dz/UniFAR-FTUs huggingface dataset:
An Energy Balanced Efficient and Reliable Routing Protocol for Underwater Wireless Sensor Networks

Underwater Wireless Sensor Networks (UWSNs) face numerous challenges due to small bandwidth, long propagation delay, limited energy resources and high deployment cost. Development of efficient routing strategies is, therefore, mandatory and has remained the focus of researchers over the past few years. To address these challenges and to further improve the performance of the existing protocols, many routing protocols have been designed. In Weighting Depth and Forwarding Area Division-Depth Based Routing (WDFAD-DBR), the forwarding decision is based on the weighting depth difference, which is not an efficient way for void hole avoidance. In this paper, we propose a depth-based routing mechanism called Energy Balanced Efficient and Reliable Routing (EBER2) protocol for UWSNs. First, energy balancing among neighbors and reliability are achieved by considering residual energy and the number of Potential Forwarding Nodes (PFNs) of the forwarder node, respectively. Secondly, energy efficiency is enhanced by dividing the transmission range into power levels, and the forwarders are allowed to adaptively adjust their transmission power according to the farthest node in their neighbor list. Thirdly, duplicate packets are reduced by comparing depths, residual energy and PFNs among the neighbors. Moreover, network latency is decreased by deploying two sinks at those areas of the network that have high traffic density. The results of our simulations show that EBER2 has higher Packet Delivery Ratio (PDR), lower energy tax, and lesser duplicate packets than the WDFAD-DBR routing protocol.
Extensible and Compressible Continuum Robot: A Preliminary Result

Continuum robot is with better flexibility and manipulability compared to traditional rigid multiple joints robot in operating in confined flexuose space. Among continuum robots, tendon-driven robot with compressible and extensible length has bigger working space than that of the non-extensible robot, though both of them can travel along the nonlinear spatial curved path. However, for the existing extensible robots, they realize extensible action based a pre-compression action, i.e., not from the natural no-load status for elastic components, like springs. This type of designs imposes restrictions on the length of the whole robot. In this paper, we present a novel tendon-driven continuum robot with extensible length from the natural no-load status for elastic components to overcome this limitation. The main contribution of our work is that the new design achieves extension based on extension of nitinol alloy super elastic bar from natural no-load status. Therefore, continuum robot based on this new design shows deeper reachability in natural orifices. Kinematic model of the proposed robot is derived and the workspace of the robot is demonstrated for one and two bending modules. A prototypical robot is built and its contractible and extensible performance is illustrated through experiments. This type of continuum robot may be useful in diagnosis and therapies for clinical scenarios, like laparoscopic surgeries and natural orifice transluminal endoscopic surgery (NOTES).
Cross-Subject MEG Decoding Using 3D Convolutional Neural Networks

Traditional MEG-based brain decoding approaches require to manual design and extract various features from raw MEG data and often ignore the subtle spatial information contained in the MEG signal. Motivated by this fact, we present a 3D-CNN method to tackle these obstacles. In this approach, a 3-Dimensional Convolutional Neural Networks (3D-CNN) is applied to classify magnetoencephalography states by effectively learning spatial-temporal representation of raw MEG data. And the 3D data representation, which is used as the data input for the proposed 3D-CNN model, is converted from the multi-channel MEG signal to retain the spatial correlations between physically neighbouring MEG signals. An improved self-training phase is developed to enhance the cross-subject performance of the proposed 3D-CNN approach. Experiments on an MEG dataset of face vs. scramble decoding task demonstrate that the proposed method can achieve promising performance.
Miniaturized Virtual Array Dual Band Loop Quasi – Yagi Antenna Design for 5G Application

In this paper, numerical and experimental analyses of both dual band loop Quasi – Yagi Antenna and miniaturized virtual array of the same structure are studied. These both design operates at free band (2.4-2.48 GHz) and planned 5G mobile communication band (3.4-3.8 GHz). Here, by introducing the virtual array concept, comparison between single element and the array structure are done. Proposed design is validated by High Frequency Structure Simulator (HFSS) and also measurements for each antennas are carried out on fabricated prototype.
Radar Antenna Scan Pattern Intelligent Recognition Using Visibility Graph

Radar antenna scan pattern (RASP) reconnaissance is a major problem in electronic warfare (EW). The RASP exerts a considerable influence on target identification, jamming decision making, and electronic support measures and thus plays a critical role in modern electronic warfare. A visibility graph (VG) is a tool for converting a time series into complex graphs with excellent noise immunity. This paper proposes a novel method for the intelligent recognition of the RASP based on the VG, including the circular, sector, helical, raster, conical, phased array, phased array azimuth and circular elevation scans. The changes in the signal amplitude received from the EW receiver are determined. Moreover, the related features are extracted from the VG and utilized to classify the RASPs. The comparison experiments performed with different classifiers, such as machine learning, neural network, and deep learning, confirm that the proposed method can improve the robustness of the recognition rate to the noise and recognition accuracy.
“Where is My Parcel?” Fast and Efficient Classifiers to Detect User Intent in Natural Language

We study the performance of customer intent classifiers designed to predict the most popular intent received through ASOS.com Customer Care Department, namely “Where is my order? ”. These queries are characterised by the use of colloquialism, label noise and short message length. We conduct extensive experiments with twowell established classification models: logistic regression via n-grams to account for sequences in the dataand recurrent neural networks that perform the extraction of these sequential patterns automatically. Maintaining the embedding layer fixed to GloVe coordinates, a Mann-Whitney U test indicated that the F1 score on aheld out set of messages was lower for recurrent neural network classifiers than for linear n-grams classifiers (M1=0.828, M2=0.815; U=1,196, P=1.46e-20), unless all layers were jointly trained with all other network parameters (M1=0.831, M2=0.828, U=4,280, P=8.24e-4). This plain neural network produced top performance on a denoised set of labels (0.887 F1) matching with Human annotators (0.889 F1) and superior to linear classifiers (0.865 F1). Calibrating these models to achieveprecision levels above Human performance (0.93 Precision), our results indicate a small difference in Recall of 0.05 for the plain neural networks (training under 1hr), and 0.07 for the linear n-grams (training under 10min), revealing the latter as a judicious choice of model architecture in modern AI production systems.
Multi-Objective Scientific-Workflow Scheduling With Data Movement Awareness in Cloud

Due to serving several purposes simultaneously, running scientific workflows on dynamic environments such as cloud computing, has become multi-objective scheduling. Among these purposes, Cost and Makespan are probably the most two primitive objectives. Another critical factor in a large-scale scientific workflow is tremendous amount of data during execution. Therefore, this work also includes Data Movement as an additional objective as it has a major impact on network utilization and energy consumption in network equipment in cloud data center. In considering these three objectives, this work proposes a framework for scheduling solutions which combines a new nodes clustering technique in Directed Acyclic Graph (DAG) model known as Multilevel Dependent Node Clustering (MDNC) and the multi-objective optimization, Extreme Nondominated Sorting Genetic Algorithm-III (E-NSGA-III). E-NSGA-III is the recent extension of Nondominated Sorting Genetic Algorithm (NSGA-III). Five well-known scientific workflows, CyberShake, Epigenomics, LIGO, Montage, and SIPHT are selected as testbeds, while the commonly known Hypervolume is chosen as the performance metric. In this work, MDNC is also experimented with both NSGA-III. Comparison among three approaches, E-NSGA-III alone, E-NSGA-III with Peer-to-Peer clustering and E-NSGA-III with MDNC are carried out. The superiority of the proposed framework among them and its limitation are discussed.
Cross-Domain Knowledge Transfer for Incremental Deep Learning in Facial Expression Recognition

For robotics and AI applications, automatic facial expression recognition can be used to measure user’s satisfaction on products and services that are provided through the human-computer interactions. Large-scale datasets are essentially required to construct a robust deep learning model, which leads to increased training computation cost and duration. This requirement is of particular issue when the training is supposed to be performed on an ongoing basis in devices with limited computation capacity, such as humanoid robots. Knowledge transfer has become a commonly used technique to adapt existing models and speed-up training process by supporting refinements on the existing parameters and weights for the target task. However, most state-of-the-art facial expression recognition models are still based on a single stage training (train at once), which would not be enough for achieving a satisfactory performance in real world scenarios. This paper proposes a knowledge transfer method to support learning using cross-domain datasets, from generic to specific domain. The experimental results demonstrate that shorter and incremental training using smaller-gap-domain from cross-domain datasets can achieve a comparable performance to training using a single large dataset from the target domain.
An Evaluation of Machine Learning Algorithms To Detect Attacks in Scada Network

Today, with the digitization of manufacturing, industries are becoming more and more competitive and offer personalized products and services. With The Industry of the Future also known as Industry 4.0 or Fourth Industrial Revolution, architecture has fundamentally changed. It is based on intelligent automation and integrates a new technologies that allow permanent communication between machines and systems to ensure an increasing improvement in production. Despite the advantages of industry 4.0, there are also challenges to be addressed: Network security has become very important than before, therefore we have to guarantee better intrusion detection, based on machine learning algorithms. To this end, we examine four supervised learning methods, namely: Naïve Bayes, Support Vector Machines (SVM), Trees J48 and Random Forest, and compare the results using different performance measures (accuracy, recall, time to build the model etc). The data set used in this study comes from a laboratory scale gas pipeline, entitled “10% Random Sample Gas Pipeline The objective of this research is to select the best algorithm to detect and predict attacks in SCADA Network, in order to take preventive measures against the risk of intrusion.
Preference-based and Homogeneous Coalition Formation in Fog Computing

In this paper, we propose a Markov Chain Monte Carlo (MCMC) algorithm to handle fog nodes coalition formation problem. The need to construct coalitions emanates from the fact that some nodes could not be able to handle the required computation on its own due to their limited computational capabilities. Also, we introduce a set of constraints on the formed coalitions. Specifically, nodes could have preferences with whom to coalesce. Even more the formed coalitions have to be of semi-equal computational powers.
DEVELOPMENT OF MEMS ANGULAR RATE SENSOR FOR HIGH-DYNAMICS APPLICATIONS

Abstract In this paper, presented are a design and test results of MEMS angular rate sensor of wide input range and wide bandwidth. Open loop performances of conventional MEMS rate sensor are very limited because of its nonlinear input-output relationship and very small bandwidth. In this study, a phase lead control scheme is applied to enlarge the operating range of the sensor. The structure of angular rate sensor is fabricated using MEMS manufacturing process and the detection circuit is designed applying the control circuit. From the test results, it is confirmed that the designed sensor has wide input range up to 1,000 deg/sec and bandwidth of 80 Hz.
Embedding Parabolic Reflector on Hologram for Redirecting Surface Leaky Waves into Forward Ones

This paper presents an approach for increasing the bandwidth of leaky wave holograms. The non-forward surface waves on a center-fed two-dimensional (2D) hologram are the main cause of the limited operating bandwidth of leaky wave metasurfaces. Embedding a parabolic surface wave reflector next to the cylindrical surface wave launcher can redirect the non-forward surface waves to forward. A leaky wave hologram excited by forward surface waves operates in a much broader bandwidth compared to the conventional holograms. The proposed reflector-embedded hologram is capable of elevation angle scanning by frequency variation. The radiation characteristics, such as gain, are significantly increased and stable over the operating bandwidth.
Vision-based Adjustment with Toleration of Error before Grasping

Automatic grasp has always been a fundamental topic in the field of robotics research. Only with the stable and high success rate grasp, can the robots behave well in some industrial scenes and man-machine interaction scenes which usually contain grasping tasks. However, the error of various devices caused by the diversity of the complicated environments mentioned above is always ignored by the traditional grasp frame assuming that the sensor and the actuator are precise. In this paper, we take error tolerance as the core idea, proposing a novel frame of grasp which includes global search and local adjustment. In addition to an under-actuated gripper, we also use a depth camera and an ordinary micro camera in the whole grasp system. Based on the data obtained by the cameras and an output from ResNet, the overall state of the gripper and the target object is estimated to make appropriate adjustments. This improvement enables the grasp system to adapt to more various unstructured environments. Finally, we conducted experiments on three common application scenarios proving the validity of the improvement.
Investigation of Different CNN-Based Models for Improved Bird Sound Classification

Automatic bird sound classification plays an important role in monitoring and further protecting biodiversity. Recent advances in acoustic sensor networks and deep learning techniques provide a novel way for continuously monitoring birds. Previous studies have proposed various deep learning based classification frameworks for recognizing and classifying birds. In this study, we compare different classification models and selectively fuse them to further improve bird sound classification performance. Specifically, we not only use the same deep learning architecture with different inputs but also employ two different deep learning architectures for constructing the fused model. Three types of time-frequency representations (TFRs) of bird sounds are investigated aiming to characterize different acoustic components of birds: Mel-spectrogram, harmonic-component based spectrogram, and percussive-component based spectrogram. In addition to different TFRs, a different deep learning architecture, SubSpectralNet, is employed to classify bird sounds. Experimental results on classifying 43 bird species show that fusing selected deep learning models can effectively increase the classification performance. Our best fused model can achieve a balanced accuracy of 86.31% and a weighted F1-score of 93.31%.
Dual-Beam and Tri-Band SIW Leaky-Wave Antenna With Wide Beam Scanning Range Including Broadside Direction

In this letter, a novel dual-beam and tri-band leaky-wave antenna (LWA) based on substrate integrated waveguide (SIW) structure is proposed, which has the capability of wide beam scanning range including broadside direction. The antenna consists of two kinds of periodic structures which can excite two −1st spatial harmonic waves and result in two radiation beams simultaneously. Through theoretical dispersion diagram analysis of the unit cells of two periodic structures and by applying the techniques of impedance-matching and reflection-cancelling, the open-stopbands at broadside are suppressed. Then the main beam of the proposed LWA can scan from backward to forward through broadside when frequency changes. Moreover, a tri-band application can be achieved in the dual-beam antenna by optimization of the second periodic structure. The measured results validate that the proposed SIW LWA has three operating frequency bands. In band 1 from 8.6 to 9.2 GHz, there is one beam scanning from 42° to 71° in the forward, in band 2 from 10 to 12 GHz, there is one beam scanning from −40° to 4° in the backward, and in band 3 from 12.5 to 15 GHz, there is a dual-beam scanning from −55° to 54° including broadside direction, which show good agreements with the simulated results.
Preliminary Results on 2-D Simultaneous Localization and Mapping for Aerial Robots in Dynamics Environments

This paper presents the design and validation of an Extend Kalman Filter (EKF) for Simultaneous Localization and Mapping with Moving Objects Tracking (SLAMMOT) with application to unmanned aerial vehicles (UAVs) in uncertain and dynamic environments. The proposed solution includes the tracking of Moving Objects (MO) using the Multiple Hypothesis Tracking (MHT) method, as well as the identification of the motion models of the environment’s objects applying the Interacting Multiple Model (IMM) algorithm. The consistency and performance of the devised SLAMMOT filter is successfully confirmed with simulation results.
An Automated Grader for Chinese Essay Combining Shallow and Deep Semantic Attributes

Writing is a pivotal part of the language exam, which is considered as a useful tool to accurately reflect students’ language competence. As Chinese language tests become popular, manual grading becomes a heavy and expensive task for language test organizers. In the past years, there is a large volume of research about the automated English evaluation systems. Nevertheless, since the Chinese text has more complex grammar and structure, much fewer studies have been investigated on automated Chinese evaluation systems. In this paper, we propose an automated Chinese essay evaluation system called AGCE (Automated Grader for Chinese Essay), which combines shallow and deep semantic attributes of essays. We implement and train our AGCE system on a Chinese essay dataset, which is created by ourselves based on more than 1000 student essays from a Chinese primary school. Experimental results indicate that our AGCE system achieves the quadratic weighted Kappa of 0.7590 on a small dataset, which is of higher grading accuracy compared with other four popular neural network methods trained on large-scale datasets. In addition, our AGCE system can provide constructive feedback about Chinese writing, such as misspelling feedback and grammatical feedback about writers’ essays, which is helpful to improve their writing capability.
Hate Speech Detection in Code-switched Text Messages

Not only does it happen in America, but also in Asia, in Africa and all over the world: Hate Speech. The exponential growth of user-generated content on social media bordering hate speech is increasingly alarming. Several efforts to monitor this phenomenon by social media network companies and the research community are on-going with various degrees of success. One gap in previous studies that this study addresses is the identification of hate speech in codeswitched text messages. The alternation of words in different languages within a message is a common occurrence among multilingual persons or communities. The study explored the performance of different features across various machine learning algorithms and established that character-level Term Frequency-Inverse Document Frequency, performed best given a codeswitched dataset of 25k annotated tweets using support vector machine algorithm as compared to six other conventional and two deep learning algorithms.
Kinematic Influence Coefficient based Dynamic Modeling of a 6-DOF Parallel Manipulator System*

A six-degree-of-freedom parallel manipulator system based on 6-PURU structure is developed for vehicles' motion simulation. The main research in this paper is focused on dynamic modeling of the proposed parallel system. The kinematic model of the proposed system is established via kinematic influence coefficient method, in which the velocity and acceleration mappings between the passive components and actuated components are more accurate compared to the equivalent 6-PUS or 6-PSS systems. Based on the kinematics analysis, the dynamic model of the parallel kinematic machine is then formulated via the principle of virtual work. Finally a numerical calculation is performed based on the proposed dynamic model which matches the simulation results of commercial software. The approach outlined in this paper is generic and can be extended to a variety of applications.
Classification of brain tumor types by deep learning with convolutional neural network on magnetic resonance images using a developed web-based interface

Automated machine learning (AutoML) algorithms developed using deep learning algorithms have been the focus of interest in many studies recently. This study aims to develop a free web-based software based on deep learning that can be utilized in the diagnosis and detection of brain tumors (Glioma/Meningioma/Pituitary) on T1-weighted magnetic resonance imaging. The Keras library, which is used in Python programming language, is utilized in the construction of the deep learning algorithm in this software. The experimental results show that this software can be used for the detection and diagnosis of three types of brain tumors. This developed web-based software can be publicly available at http://biostatapps.inonu. edu.tr/BTSY/ in both English and Turkish.
A Stochastic Information Diffusion Model in Complex Social Networks

The number of potential participants in information diffusion is dynamically changing, and the topological structure of a real social network is affected by environment noise. In this paper, a stochastic information diffusion model considering both population perturbation and connectivity variation has been proposed. For the model on homogeneous network, we show the existence and uniqueness of the global positive solution and give a sufficient condition of extinction of information. A series of numerical simulations on Watts–Strogatz (WS) network, Barabási–Albert (BA) network and real-world Facebook network have been conducted to verify the theoretical analysis and evaluate the sensitivity of the proposed model to relevant parameters. We find that both population perturbation and connectivity variation have great impacts on information diffusion process. In general, the connectivity variation noise promotes the spread of information, and the population perturbation noise corresponding to I-infected individuals inhibit the spread of information. In the case of large noise intensity, the population perturbation noise corresponding to I-infected individuals plays a decisive role compared with the connectivity variation noise.