Miracle-dz/UniFAR-FTUs huggingface dataset:
Using heuristic approach to build Anti-malware

The security threats to mobile devices are growing exponentially as the degree of sophistication for these smart devices increase. Mobile devices are not only used for phone calls, they process several enterprise and personal information on daily basis. The comprise to such crucial and private information can cause devastating effects at personal and enterprise levels. In this work, we present a new approach to develop anti-malware programs that can adapt to themselves to the new definitions of the malwares and can detect them in realtime. We propose a machine learning algorithm that can be trained to identify a malicious activity on a smart-phone. We develop a malware and also used existing malwares to generate a training data set to train a malware for detecting the patterns of malicious activities on devices. The paper also presents the key indicators for pattern matching in such cases. The results show that the new definitions of malicious codes are not detectable by existing sophisticated anti-malware programs by using sandboxing techniques. The results also show that all definitions of malwares work under within the same patterns of indicators.
A Novel Positioning System of Potential WiFi Hotspots for Software Defined WiFi Network Planning

WiFi networks are playing an increasingly important role in the current networks towards the future 5G heterogeneous networks. The pertinence and effectiveness of WiFi network planning and construction, however, is demanding. To solve this problem, a novel positioning system for the potential WiFi hotspots is presented in this paper. The system can analyze the scale of the WiFi users for each site and then locate the potential WiFi hotspots accurately through detecting WiFi network card signals without contact. Accordingly, the WiFi network planning, especially software defined WiFi network planning can be refined and optimized.
A Comparison of Different Dynamic Routing Protocols in FANETs

Among the technological advances, the use of Unmanned Aerial Vehicles (UAVs) saw an evolution in this last decade, their employment in civil applications, such as firefighting, disaster management, environmental monitoring and policing, is increased recently. The UAVs are connected with Ad- hoc modes like Mobile Ad-hoc Networks (MANETs) and Vehicular Ad-hoc Networks (VANETs), this is another innovation in the wireless network where requirements change largely from long-established networking systems. Furthermore, the cooperative work is required in both civil and military applications to accomplish intensive missions. Flying Ad-hoc Networks (FANETs) have been developed using techniques that assist the transmission of information in real-time. The high mobility and quick topology change in UAV networks caused the difficulty for specialists to insert routing in FANETs. Routing protocols have a useful role to improve the performance of these networks. This paper presents a comparison between the different dynamic routing protocols to attain the high quality of service using the NS3 simulator.
Gesture Recognition for Traffic Hand-Signals Training Simulator Using Kinect

Human gesture recognition is a way to interpret human movement and/or posture automatically. In this paper, it is used as the main interaction for the developed traffic hand-signals training simulator, based on the Kinect skeleton tracking system. Therefore, the gestures defined in this work are traffic hand-signals used in Thailand. They consist of both static postures and dynamic movements. The recognition is trained and constructed using the rule-based system. The rules must be trained to distinguish these traffic hand-signals, based on both movement information and depth-map information of hands. Then, in a part of the simulator, the artificial intelligent techniques are applied to make it realistic and challenge. The techniques include finite state machine, pathfinding, and path following. They are implemented and used for individual vehicles in the scene. Then, the performances of these two key components of the developed system, the hand-signals recognition and the traffic simulator, are evaluated. It is shown that the system can achieve a very promising performance in both aspects of the recognition accuracy and the user satisfaction.
Plasmonic Ferroelectric Modulators

Integrated ferroelectric plasmonic modulators featuring large bandwidths, broad optical operation range, resilience to high temperature and ultracompact footprint are introduced. Measurements show a modulation bandwidth of 70 GHz and a temperature stability up to 250 °C. Mach–Zehnder interferometer modulators with 10-μm-long phase shifters were operated at 116 Gbit/s PAM-4 and 72 Gbit/s NRZ. Wide and open eye diagrams with extinction ratios beyond 15 dB were found. The fast and robust devices are apt to an employment in industrial environments.
Grasping with a tube-feet inspired soft gripper

In this work, a pneumatic soft gripper is developed from silicone elastomer. A finger for the proposed soft gripper is inspired by a tube foot of a sea star which can deform in multiple directions. The proposed gripper has three soft silicone fingers which are driven pneumatically and controlled by a 9 bit-binary command. The construction of each finger consists of a three sided air chamber, thus it can be curved in 6 directions. This gripper can grasp different objects using two different modes of grasping: force grasping and cage grasping.
Detection of Fonts and Characters with Hybrid Graphic-Text Plate Numbers

Philippine license plates have different plate styles and character fonts making the plate character recognition challenging. This paper focuses on improving the segmentation method to recognize characters of different formats of Philippine license plates. The proposed system comprises of license plate classification, character segmentation and character recognition. License plate series was classified using color level of pixels in the image. Plate characters were segmented using 3-Class Fuzzy Clustering with Thresholding and Connected Component Analysis and were recognized using Template Matching. The system achieved an accuracy of 95% and 70% for the 2003 plate series and 2014 plate series, respectively, having tested 20 license plates from each series.
Automatic Generation of Labanotation Based On Extreme Learning Machine with Skeleton Topology Feature

Labanotation is one of the most widely used notation systems for recording and analyzing human movement. In this paper, we propose a novel method for automatic generation of Labanotation based on skeleton topology feature and Extreme Learning Machine. Firstly, according to the principle of human body organization structure, we design the feature that represent the characteristics of human motion better. Then we adopt the efficient and fast Extreme Learning Machine algorithm to recognize human movement. Finally, we draw the Labanotation score according to the identified movement categories corresponding to the symbols of Labanotation. This is the first time that a neural network based approach has been used in the research of automatically generating Labanotation. Experimental results show that the proposed approach achieves much better performance than the sate-of-the-art methods in recognition accuracy and computational time, demonstrating the efficiency of our method.
Syntax Rectification of Speech Using Neural Machine Translation

Speech Recognition is the identification of speech and conversion of the audio signals into textual data. The process of Speech Recognition has been accelerated with advancements in computing and progress in the development of Deep Learning. The mathematics behind Speech Recognition stems from Hidden Markov Models and has evolved into its more advanced form – End to End Speech Recognition. The aim of this paper is to focus on the syntax correction of speech in English language which will make use of Sequence to Sequence Models. The paper will delineate three major domains of grammatical correction of speech which will be achieved through real time interaction with the user.
Optimal Allocation of Distributed Generations and Capacitor Using Multi-Objective Different Optimization Techniques

In this paper, the performance of different optimization techniques namely, Moth Swarm Algorithm (MSA) and Whale Optimization Algorithm (WOA) are presented and compared for minimization of single and multi-objective namely, (total network power losses, and voltage deviation) in radial distribution systems by identify the Optimal Placement of Distributed Energy Resources (DER) together with capacitor (C). In this regard, multiple-DER units are analyzed under two load power factors (i.e., unity and optimal) with and without C. The proposed algorithms have been tested on the 33-bus radial distribution network. The performance of the different optimization algorithms has been compared with other evolutionary optimization technique under different system operating conditions. The simulation results observed the impact of integrating the proper size of DER at the suitable placement based on different techniques with and without C.
Latent Fingerprint Enhancement Using Sparse Representation

Latent fingerprints are fingerprints which are smudged, overlapped, and distorted. They are poor quality images with unclear ridge structure. Latent fingerprints are seen at crime scene, so it plays a vital role in identifying and declaration of criminal in law enforcement.Therefore it is important to enhance the latent finger prints which are distorted by most of the non-finger print patterns. By enhancing the latent fingerprints it becomes easy to match with the large database of know persons fingerprints.By seeing the advances of sparse representation in image denoising, the work proposes the method of enhancement using sparse representation. In Sparse representation initially total variation model will be used for latent fingerprint image, where image is disintegrated into cartoon and texture components. Cartoon component contains noise, which will be discarded while texture component contains main information. Second sparse enhancement algorithm is implemented on texture component of the image. Experimental results are carried on NIST SD4 to show the results of the proposed algorithms.
A 1.35 m Long 0.18 gf Resolution Differential Capacitive Force Sensor for Contact Force Monitoring

This paper introduces a 1.35 m long differential capacitive force sensor with a small form factor for a catheter integration. The differential force sensor is realized with two polydimethylsiloxane (PDMS) layers having two different rigidity inserted between flexible printed circuit board (FPCB) electrodes. To achieve the higher precision, we also propose the differential driving method which allows the higher driving voltage even in a low supply system, resulting in the higher SNR. By using the driving voltage of 18 Vp-p under 2 V supply system, the proposed sensor interface has accomplished SNR of 58.22 dB at 100 gf and the force error has reduced from 0.24 gf to 0.18 gf with the equivalent capacitance resolution of 22.2 aF. This work has achieved the state-of-art force resolution by using both the differential driving and CP cancellation technique with a miniature sensor. The proposed sensor can be used for a real time contact force measurement during a catheter ablation or the stiffness detection.
The Impact of Orbit Prediction Error on Bistatic Interferometric ISAR Imaging of Space Targets

In interferometric inverse synthetic aperture radar (InISAR) imaging of space targets, the orbit prediction error will bring error to the estimation of height coordinate. A height estimation error model for bistatic InISAR imaging of space targets is described in this paper. Based on this model, a standardized threshold that can be utilized in any bistatic InISAR scenarios is proposed to evaluate the impact of orbit prediction error on height estimate accuracy. Simulations are utilized to illustrate the theoretical analysis.
Early Warning Device for Brown Plant Hopper Detection in Palay Using Wireless Sensor Networks

Rice (oryza sativa) is one of the most in demand staple foods in the world because of its versatility in processing and consumption. But due to the ever increasing human population, rice farmers and producers are pressured to produce more from just one to three cropping seasons. Climatic change also affects rice production as well as the heavy use of chemical inputs. These factors contribute to the problems of pest outbreaks and disease attacks to rice. Brown planthoppers (BPH, Nilaparvata lugens) are among the outbreak pests of rice that have brought heavy losses in several Asian countries including the Philippines. Its early detection is crucial because this pest is a virus carrier that can cause tremendous yield loss to rice. With this, designing an early warning device is a critical element for BPH pest management system. Image processing is one of the early detection mechanisms proposed in the literature. In this research, an early warning device using Wireless Sensor Network (WSN) through Haar feature-based cascade classifiers for BPH detection is being proposed which can be installed in strategic locations in the palay-farm-area under study. This device is programmed to send warning messages when acceptable range of the BPH cascade classifiers is obtained; thereby, allowing the end-users to check the status of the palay farm area. The researcher develops an early warning device prototype and performs an extensive set of experiments measuring its image detection capability.
Object Detection and Tracking based on Recurrent Neural Networks

Robust and accurate visual tracking is one of the most challenging computer vision problems nowadays. The development of deep learning improves the performance of tracking, but in non-constrained scenarios, the accuracy will decrease dramatically. To tackle this issue, we put forward our own tracking framework on the basis of recurrent neural networks (RNN). Firstly, we use the temporal correlation of RNN to predict the direction of the object at next frame. Secondly, we determine region of interest (ROI) using the direction predicted by RNN. And then, we detect the object within ROI. Finally, the output of the object detection model is transmitted to the direction prediction model, which is the input of the next frame, for realizing object tracking framework. We perform comprehensive experiments on two benchmark datasets: OTB and VOT2016. Our approach achieves an accuracy of 0.826 and a speed of 41fps on OTB100, which is effective and efficient.
Simultaneous Speech Detection and Magnitude Squared Spectrum Estimation Approach for Speech Enhancement

A simultaneous speech detection and estimation algorithm under speech presence uncertainty is proposed for noisy speech enhancement. By minimizing a novel cost function that takes into account both detection and estimation errors in magnitude squared spectrum domain, a generalization of the minimum square mean square (MMSE) estimator is derived. By adjusting the cost parameters, the tradeoff between speech distortion caused by noise false detection and missed detection of speech components can be availably controlled. The simulation results show that the proposed algorithm is easy to implement, and the enhanced speech signal is closer to the clean speech signal than the MMSE estimators of the magnitude squared spectrum.
Common Pilot Signal Design for Non-Orthogonal Multiple Access in 5G

In this paper, common pilot signal design for channel estimation of users with different channel strengths simultaneously served by non-orthogonal multiple access (NOMA) is considered. The problem is approached based on two design criteria: minimization of weighted sum mean-square error (MSE) and maximization of weighted sum conditional mutual information (CMI) between the channel and the received signal given the pilot signal. It is shown that the two design problems reduce to semi-definite programming (SDP). Furthermore, in the practical case of two-user clustering, closed-form solutions to the two problems are obtained under some additional assumption on the channel’s statistical information. Numerical result shows that the pilot signal design exploiting the multi-group structure of NOMA yields better channel estimation performance than conventional design.
A Prototype Performance Analysis for V2V Communications using USRP-based Software Defined Radio Platform

Autonomous driving is usually recognized as a promising technology to replace human drivers in the near future. To guarantee the safety performance in the daily life scenario, multiple-car intelligence with high quality inter-vehicle communication capability is necessary in general. In this paper, to figure out the potential practical issues in the vehicle-to-vehicle transmission, we present a software defined radio platform for V2V communication using universal software radio peripheral (USRP). Based on the LTE framework, we modify the frame structure, the signal processing mechanisms and the resource allocation schemes to emulate the updated LTE-V standard and generate the corresponding numerical results based on the real measured signals. As shown through some empirical studies, three to four dB back-off is in general required to guarantee the reliability performance for V2V communication environments.
Factotos Influencing ERPs Implementation in UAE

Successful implementation of Enterprise Resource Planning (ERP) technology has always been a subject of interest among researchers. Adoption of such system involves addressing critical success factors (CSF). Relevant literature offers limited understanding of this topic in the United Arab Emirates (UAE) context in particular. Hence, this study is undertaken to investigate what are the critical success factors for successful implementation of ERP; the results show positive association between National and Organizational factors, and successful implementation of ERP systems in the federal authorities of the UAE government.
Chemical dosimetry in gamma irradiators of 60 Co and 137 Cs for semi-industrial and research purpose

Departamento de Química de Radiaciones y Radioquímica, Instituto de Ciencias Nucleares. Universidad Nacional Autónoma de México, Ciudad de México. E-mail: albarran@nucleares.unam. mx © Universidad De La Salle Bajío (México) Dosimetría Química de irradiadores gamma de Co y Cs de uso semi-industrial e investigación Chemical dosimetry in gamma irradiators of Co and Cs for semi-industrial and research purpose G. Albarrán, E. Mendoza y E. Cruz-Zaragoza Palabras clave: Dosímetro de Fricke; fuentes gamma de Co y Cs; razón de dosis; irradiador para investigación; irradiador semi-industrial
Evaluation of Low Voltage Rectifier Design Using IGBT, MOSFET, and GaN FETs

The adoption of IoT enabled devices have led to higher risks of EMI issues, especially in their power management. Particularly, rectifiers for IoT devices are especially concerning. To mitigate this, one approach is to use wide-bandgap semiconductor devices for power management, which reduces the devices’ susceptibility and emission of conducted EMI. One such example are GaN semiconductors. However, for low voltage, low frequency systems, the literature concerning GaN performance with regards to conducted EMI have been sparse. This is particularly important for industries such as the medical field which might require low voltage power circuits with components more adept to handling EMI. In this paper, an evaluation will be carried out to compare transistors’ performance in the design of a low voltage, low frequency rectifier of 12 Vac at 50 Hz. The results of the simulation using IGBT, GaN FETs and MOSFETs are then shown and discussed.
Reactive Hybrid Particle Swarm Optimization Based Job-shop Scheduling Problems Considering Energy Management

This paper presents reactive hybrid particle swarm optimization based job-shop scheduling problems considering energy management. Since reduction of energy costs is important, operational planning of energy plants in factories should be optimized. However, the scheduling in factories and optimization of energy plants have been solved separately. Namely, energy costs have been ignored when the scheduling is optimized. However, it should be considered from the management point of view. This paper tries to optimize production scheduling and energy plant in order to minimize the maximum end time of all factory operations (makespan) and total energy costs simultaneously. Effectiveness of the proposed method is verified through simulations with 10 jobs 10 machines problem and it is verified that the proposed method can obtain higher quality solutions than the conventional method.
Exploiting Symmetries in Reinforcement Learning of Bimanual Robotic Tasks

Movement primitives (MPs) have been widely adopted for representing and learning robotic movements using reinforcement learning policy search. Probabilistic movement primitives (ProMPs) are a kind of MP based on a stochastic representation over sets of trajectories, able to capture the variability allowed while executing a movement. This approach has proved effective in learning a wide range of robotic movements, but it comes with the necessity of dealing with a high-dimensional space of parameters. This may be a critical problem when learning tasks with two robotic manipulators, and this work proposes an approach to reduce the dimension of the parameter space based on the exploitation of symmetry. A symmetrization method for ProMPs is presented and used to represent two movements, employing a single ProMP for the first arm and a symmetry surface that maps that ProMP to the second arm. This symmetric representation is then adopted in reinforcement learning of bimanual tasks (from user-provided demonstrations), using relative entropy policy search algorithm. The symmetry-based approach developed has been tested in an experiment of cloth manipulation, showing a speed increment in learning the task.
Dynamical Sampling Associated with the Fractional Fourier Transform

In this paper, we mainly study the dynamical sampling of sequence spaces and shift-invariant spaces in the fractional Fourier transform domains. We show that how to recover the signals in sequence spaces or shift-invariant spaces from their dynamical sampling values. We provide a necessary and sufficient condition for the dynamical sampling problem of sequence spaces and shift-invariant spaces in the fractional Fourier transform domains to be solvable. Our results generalize similar ones in the Fourier transform domains.