Miracle-dz/UniFAR-FTUs huggingface dataset:
A Generic Approach CNN-Based Camera Identification for Manipulated Images

Camera model identification has been attracting a lot of attention lately, as a powerful forensic method. With the promising breakthroughs in the artificial intelligence applications, such systems were revisited to increase the expected accuracy or to solve the still persisting deadlocks. One of the most still-to-be-solved dilemmas is the image manipulations effect on the overall accuracy of the identification systems. A huge degradation in the performance is noticed, when images are post-processed using commonly used methods as compression, scaling and contrast enhancement. Using the state of the art Convolutional Neural Network (CNN) architecture proposed by Bayar et al to estimate the manipulation parameters, and dedicated feature extractor models to estimate the source camera. Multiplexers are used to shift the input image between the dedicated models through the output of the CNNs. Our proposed methods significantly outperform state of the art methods in the literature, especially in case of heavy compression and down sampling. The images used for testing were extracted from 10 different cameras, including different models from the same manufacturer. Different devices were used to investigate the methodology robustness. Moreover, such generic approach could revolutionary change the whole design methodology for camera model identification systems.
Enhanced Language Model Personalization Based on Deep Belief Network

Since every mobile device is owned primarily by an individual person, it is possible to have a customized recognizer that well matches the qualities of that particular user. In personalisation of language models the need of much huge corpora(database) and massive amount of errors in the previous technologies makes them outdated and also they show poor performance during their execution. The previous technologies includes N gram based language model and recurrent neural network language model(RNNLM). Although projects on acoustic model personalisation has undergone lot of research over many years, but much less work has been reported on linguistic model personalisation. The primary section is the speaker recognition, which is done using MFCC(Mel frequency cepstrual coefficient processor). In the secondary section we will convert the speech input to text with predictive actions, like when we give a voice input to the system it will detect the word and convert it into the personalized text. This is called personalized, because the neural network were trained with the database of the specific user who is supposed to use the the system individually. For this, the data base will be created first with his personal slang. There might be grammatical errors posed by the individual user whilst training the neural network, however the same shall be used to create the database without correction along with other common words used by the individual user, so that it can generate the personalized language while he is talking to the system. Also it will display the next word prediction and the same will be taken from the personalized database. During the work of this thesis, we tend to propose improved customized model by using deep belief network. Here the deep belief network (DBN) shows higher performance than the N gram based models, recurrent neural network and conventional neural network technologies. Presumptively the deep belief network needs only less corpora and shows higher end in word error rate and running time. Also it is possible to have multiple word prediction with more accuracy using deep belief network, whereas with recurrent neural network it is possible only to have single prediction.
Real-Time Dengue Prediction Using Naive Bayes Predicator in the IoT

Dengue is a baleful disease. This disease is found in several developed as well as developing countries like India. In India at very first dengue was detected in 1963 at Kolkata. This disease is caused by biting of Agypti Aedes mosquito. However, this can be prevented by taking proper measures. The datasets contains symptoms which are BP, heart rate, fever temperature, and platelets. It posses symptoms which affect patient and verify. In this paper we consultation about Naive Bayes algorithm for classification that describes utilization for dengue disease prediction. In this method, the real-time data is accepted and is stored on the server. Analysis of previous records of the patient. And that accepted real-time data compared with the dataset and it gives the probability of the dengue. Which is in the form of prediction result.
Internet of Things Connectivity in Deep-Indoor Environments

Internet of Things (IoT) applications are becoming more and more popular for the transmission of sensor data in various environments. Due to required battery lifetime, Low-Power WAN technologies such as NB-IoT and LoRaWAN are promising candidates to provide connectivity in so called deep-indoor environments. In this paper, we compare these two technologies in terms of Received Signal Strength Indicator (RSSI) in a practical field test and to a simulation study.
Experimental Design of Fixed Switching Frequency Model Predictive Control for Sensorless Five-Level Packed U-Cell Inverter

In this paper, a fixed switching frequency model predictive current controller is designed and implemented on a sensorless five-level packed U-cell (PUC5) inverter interfacing the utility. The predictive control methodology is based on a voltage vector generation technique for the cost function optimization by nullifying its derivative. The aim of the controller is to allow a fixed-frequency operation with no iterations while operating as a utility line current regulator. A pulsewidth modulator based on employing PUC5 redundant switching states fixes the PUC5 auxiliary dc bus capacitor voltage to its desired level with no necessity of a regulator; the overall technique reduces system complexity with less sensors and no loops in the predictive algorithm making it suitable for industrial applications. System discrete model is developed and experimental results verify the performance of the designed controller in making a fast response in regulating the grid current and balancing the capacitor voltage.
Multi-Losses Function Based Convolution Neural Network for Single Hyperspectral Image Super-Resolution

Recently deep convolutional neural network (CNN) has made significant achievement in Single Image Super-Resolution (SISR). Most CNN-based SISR methods used the default L2 norm of the error. However, for Hyperspectral Image (HSI), this loss function may bring spectral inconsistencies. The main reason is that most methods did not pay much attention to spectral loss. To HSI, the loss function should capture not only spatial information but also spectral consistency. In this paper, a Multi-Losses Function Network (MLFN) simultaneously considering spatial and spectral information is proposed, and is composed of two parts: one is Concatenate Dense Residual Network (CDRN), and the other is Loss Network (LN). CDRN is an image reconstruction network which can utilize the hierarchical features extracted from the low-resolution image. LN includes pixel-wise spatial loss and spectral loss which drive the learning of the entire reconstruction model. The experimental results prove that the proposed MLFN can enhance spatial resolution with the consistency of the spectrum of HSI preserved.
Decoupled Imaging for Coherent FMCWMIMO Radar with Compressive Sensing

We investigate a framework for coherent multiple input multiple output (MIMO) radar imaging using frequency modulated continuous wave (FMCW). The structure of the FMCW signal decouples the joint estimation problem into range and azimuth imaging. Decoupling the problem reduces the computational complexity of the approach. The proposed method is generalized to near-field scenarios in case the point spread function of the output signal can be approximated by the product of the transmit and receiver antenna point spread functions. The range imaging problem is solved with the help of a Fast-Fourier-Transform (FFT) of the beat signal. In order to avoid forward-backward spacial smoothing techniques we rely on a compressive sensing-based direction of arrival estimation. The performance of the proposed approach is tested and evaluated using simulations. Furthermore we investigate the computational complexity of the framework.
A Multi-branch Linear Amplification with Nonlinear Components Technique

Single carrier modulations with high order constellations or orthogonal frequency division multiplexing (OFDM) modulations can be employed to obtain spectrally efficient communications. However, such modulations lead to signals with large envelope variations, where the amplification is challenging and usually energy-inefficient. The linear amplification with nonlinear components (LINC) makes use of two efficient, nonlinear power amplifiers for an efficient amplification. In this work, we propose the multi-branch LINC (MB-LINC) technique, which allows the use of highly-efficient nonlinear amplifiers without increasing the nonlinear distortion effects, even if the amplifiers are not balanced1.
A Software-Defined Intranet Dynamic Defense System

The rise of Bring Your Own Device (BYOD) now poses new challenges to the traditional intranet, which used to deploy boundary-based defenses to guarantee internal security. The bringing of personal devices has threatened the internal security. Based on the idea of isolation and dynamic, this paper designs and implements a Software-defined Intranet Dynamic Defense System (SIDD) to harass cyber kill chain. Firstly, to solve the issue that network can be easily reconnoitered due to its static attributes, we allocate virtual IP address space for intranet terminals and implement the dynamic mapping between real IP addresses and virtual IP addresses to hide the real IP address. Secondly, we propose a software-defined dynamic defense architecture scheme, which manages to provide a general control of the intranet, including three core modules (e.g. DNS, virtual & real address assignment and virtual address maneuvering). Finally, we implement a dynamic defense system oriented to the production environment, based on the OpenDaylight controller. Our experiments indicate that this method can achieve a definable IP address, which frequency and space are maneuverable, thus it could significantly reduce the availability of network reconnaissance and increase the difficulties of attacker's realtime attack without affecting network applications.
IoT Networks: Frequency Control Considerations

Emerging Internet of Things (IoT) networks enable internetworking of remote devices with simple transceiver devices that are low cost and consume low amounts of power. However, such characteristics also limit the flexibility in how such devices and networks operate. An auxiliary channel scheme is presented that enables peer-to-peer communications between networked IoT devices, while having minimal impact on power budgets.
Person Tracking and Frontal Face Capture with UAV

In this paper, we present a method using an unmanned aerial vehicle (UAV) to track the specified walking person and automatically capture a frontal photo of the target. The proposed method is composed by three parts: person detection and recognition, face detection and feature points localization, and vision based UAV control. In the person tracking part, we employ the deep neural network YOLOv3 for person detection and Locality-constrained Linear Coding (LLC) method to match the specified target person. In terms of frontal face perception, we use Multi-task Cascaded Convolutional Neural Networks (MTCNN) for face detection. Based on the vision information obtained from the two modules, the UAV can fly around the target person and obtain the target's frontal face image. The outdoor experiments based on a Parrot Bebop2 drone verify the effectiveness and practicability of our method.
Neural Network Based Classification of Virtual Machines in IaaS

Machine Learning algorithms are being widely used to make intelligent systems. In Cloud Computing, issues like server consolidation, load balancing and virtual machine to physical machine mapping have been addressed using clustering analysis which is unsupervised learning. It is important that Cloud Service Providers address these challenges in order to provide enhanced user experience and superlative Quality of Service. Neural Networks, which fall under supervised learning algorithms, have emerged tremendously and are used in different areas such as Bio-Informatics, Pattern Recognition and Classification. Using Neural Networks and Back Propagation Algorithm, this work classifies different types of virtual machines that are providing heterogeneous services. Further, the technique is compared to K-means Clustering Algorithm which groups similar types of virtual machines into several clusters. The objective of this work is to form labeled clusters of virtual machines with the help of Neural Networks based on their types and loads and perform server consolidation. The proposed work achieves higher performance in terms of number of virtual machine samples correctly classified, as compared to K-Means Clustering algorithm. Migrating dissimilar virtual machines onto a single physical host improved machine utilization with the trade-off of slight increase in response time of the machines.
Control Plane Performance in Tactical Software Defined Networks

Software Defined Networking can be beneficial in tactical networks to increase flexibility, provide programmability and to simplify management. The immense dynamics in tactical communication networks can, however, lead to excessive control traffic. As capacity in such networks is limited, this is a challenge that must be resolved. In this paper we investigate the control traffic overhead and present an analytical model that can predict the number of control messages for SDN networks with a given size and packet loss probability. We verify the soundness of the model using network emulations, and show that there is a good match between the analytical estimates of control traffic and real measurements. The tools provide some important insights that can be used to design and improve tactical SDN networks.
Analysis of the Cybersecurity Threats in Smart Grid University of Telecommunications and Post, Sofia, Bulgaria

In this article is presented an overview of the existing solutions for smart grids, and an analysis of the majority cases of cybersecurity threats. The purpose of this article is to define the risks of compromising the confidentiality of the transferred and stored data, and the ability to install a new device into the network. The main hazards coming from unauthorized access to the smart grid devices are analyzed and systematized and recommendations are given to improve security. The proposed smart grid design aims at making the system as safe as possible from the viewpoint of information protection and communication using intelligent communication and control devices.
Recurrent Neural Network Language Model Adaptation for Multi-Genre Broadcast Speech Recognition and Alignment

Recurrent neural network language models (RNNLMs) generally outperform <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula>-gram language models when used in automatic speech recognition (ASR). Adapting RNNLMs to new domains is an open problem and current approaches can be categorised as either feature-based or model based. In feature-based adaptation, the input to the RNNLM is augmented with auxiliary features whilst model-based adaptation includes model fine-tuning and the introduction of adaptation layer(s) in the network. In this paper, the properties of both types of adaptation are investigated on multi-genre broadcast speech recognition. Existing techniques for both types of adaptation are reviewed and the proposed techniques for model-based adaptation, namely the linear hidden network adaptation layer and the <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula>-component adaptive the RNNLM, are investigated. Moreover, new features derived from the acoustic domain are investigated for the RNNLM adaptation. The contributions of this paper include two hybrid adaptation techniques: the fine-tuning of feature-based RNNLMs and a feature-based adaptation layer. Moreover, the semi-supervised adaptation of RNNLMs using genre information is also proposed. The ASR systems were trained using 700 h of multi-genre broadcast speech. The gains obtained when using the RNNLM adaptation techniques proposed in this paper are consistent when using RNNLMs trained on an in-domain set of 10M words and on a combination of in-domain and out-of-domain sets of 660 M words, with approx. <inline-formula><tex-math notation="LaTeX">$\text{10}{\%}$</tex-math></inline-formula> perplexity and <inline-formula><tex-math notation="LaTeX">$\text{2}{\%}$</tex-math></inline-formula> relative word error rate improvements on a 28.3 h. test set. The best RNNLM adaptation techniques for ASR are also evaluated on a lightly supervised alignment of subtitles task for the same data, where the use of RNNLM adaptation leads to an absolute increase in the F–measure of <inline-formula><tex-math notation="LaTeX">$\text{0.5}{\%}$</tex-math></inline-formula>.
Demonstration of 95 GHz Single RAU Linear Cell Radar Over Fiber and Radar Propagation Study in Malaysia

Safety enhancement of airport runway surfaces can avoid unnecessary runway closure and airplane accidents due to foreign object debris. Millimeter-wave linear cell radar system empowered by radio-over-fiber network system is a promising solution for surveillance of the runway surfaces within 10 s. Currently, 96-GHz linear cell radar system has been already installed with four radio access units in Narita International Airport, Japan, as a field trial test. For the application of the linear cell radar system to Kuala Lumpur International Airport as a field trial in a tropical region, some fundamental characteristics need to be measured and analysed before installation of the linear cell radar system. In this paper, we show the summary of the field trial in Kuala Lumpur International Airport, Malaysia and Universiti Teknologi Malaysia, Malaysia.
Credibility Evaluation of Twitter-Based Event Detection by a Mixing Analysis of Heterogeneous Data

Twitter has been recognized as an important data resource for real-time event detection. However, Twitter-based event detection systems cannot guarantee credibility in terms of their detection results. Rumor detection has been studied recently to enable credible event detection. Nevertheless, this problem has not yet been solved because most of the existing studies only focus on the information on Twitter with Twitter-based event detection systems. More specifically, the existing studies detect rumors by identifying and checking special features of incredible information on Twitter. However, values of the identified features can be faked easily and so it is important to conduct a mixing analysis of both Twitter and external credible data resources to solve this problem. The problem is how to harmoniously analyze heterogeneous data since they have different data formats, generation times, and so on. To solve this problem, this paper proposes a method to evaluate the credibility of Twitter-based event detection, which considers the two kinds of data resources for credibility evaluation to exclude influence by falsification. In particular, our method utilizes an event detection result and the number of articles related to the event. We performed comprehensive experiments to evaluate the proposed method. The experiments show that the proposed method gives the detected events high credibility and other events low credibility, correctly. More specifically, event detection accuracy increases by an average of 26.8% by reviewing the detection results according to their credibility evaluated with the proposed method. Additionally, to measure the appropriateness of the credibility evaluation, we filtered out incorrectly detected events from the event detection results referencing their evaluated credibility based on the proposed method. We calculated the F-measure, precision, and recall of the experimental results, and through the experiments, we present the effectiveness of the method.
Learning Radicals From Tangut Characters

Deep learning has been successively applied in Tangut character recognition, the most expensive and time-consuming part of which is the data annotation. Each Tangut character consists of one to several radicals, i.e. graphical components, and the number of radicals is far less than the Tangut characters, therefore we could annotate the Tangut characters with their radicals rather than annotate each characters. We have applied multi-label learning in detecting the radicals contained in Tangut characters, based on which any Tangut character could be represented with its radical set. The multi-label classifier is trained on the character-level annotated data together with the ”Tangut character-radical dictionary” that help generate the radical vector for each Tangut character. We use a similar architecture of deep neural network with the one for recognizing Tangut characters, and replace the softmax activation function of the output layer with sigmoid, and use the cross entropy loss function. The radical extraction neural network has been used in accelerating the Tangut character annotation. Besides Tangut characters, the method could be applied in recognizing other characters that have no annotated dataset.
SMARISA: A Raspberry Pi Based Smart Ring for Women Safety Using IoT

Every day, every woman, young girls, mothers and women from all walks of life are struggling to be safe and protect themselves from the roving gaze of the horribly insensitive men who molest, assault and violate the dignity of women on a daily basis. The streets, public transport, public places in particular have become the dominion of the hunters. Due to these atrocities that women are subjected to in the present scenario, a smart security wearable device for women based on Internet of Things is proposed. It is implemented in the form of a smart ring (SMARISA) and comprises of Raspberry Pi Zero, Raspberry Pi camera, buzzer and button to activate the services. This device is extremely portable and can be activated by the victim on being assaulted just by the click of a button that will fetch her current location and also capture the image of the attacker via Raspberry Pi camera. The location and the link of the image captured will be sent to predefined emergency contact numbers or police via smart phone of the victim thus preventing the use of additional hardware devices/modules and making the device compact.
Nonlinear State Estimation Technique Implementation for Human Heart Model

Human heart is a vital organ therefore proper diagnosis of heart activities is essential. Various parameter estimation techniques have been developed to estimate heart parameters. In this work, we use Ensemble Kalman Filter (EnKF) and Particle Filter (PF) for dynamic assimilation of human heart parameters. EnKF and PF are modified filters specifically designed for state prediction of nonlinear systems with large data samples. A third order mathematical heart model is used to estimate three heart parameters that includes movements of heart muscle fiber, tension in heart muscle and electrochemical activity of the heart. EnKF and PF are applied to heart model and different case studies are performed to observe the prediction accuracy by comparing sum squared error values. Case studies are performed with variable state and measurement noise values. The proposed approach demonstrates promising results in accurately predicting the human heart parameters.