Miracle-dz/UniFAR-FTUs huggingface dataset:
Resource Allocation for Symbiotic Radio System With Fading Channels

In this paper, we investigate the resource allocation problem for the symbiotic radio system, in which the passive backscatter transmission is parasitic in the primary transmission, and the cooperative receiver simultaneously detects the signals transmitted from the active primary transmitter (PT) and the backscatter device (BD). In particular, channel fading is considered, and the ergodic weighted sum rate of the primary and backscatter transmissions is maximized by jointly optimizing the transmit power at the PT and the reflection coefficient at the BD under either long-term or short-term transmit-power constraint over the fading states. Two practical transmission setups are considered, where the relationship between the primary and backscatter transmissions is either commensal or parasitic. For the commensal setup, we fix the reflection coefficient as the maximum value and derive the optimal power allocation scheme based on the standard convex optimization procedure. For the parasitic setup, the weighted sum-rate maximization problem is non-convex, and we resort to the concave–convex procedure to derive a suboptimal solution. Simulation results demonstrate that in the commensal setup, the BD may realize transmission while causing no harmful interference to the primary system; in the parasitic setup, a higher backscatter data rate can be achieved through slightly sacrificing the primary transmission rate.
Centralized DNS over HTTPS (DoH) Implementation Issues and Risks

The DNS over HTTPS (DoH) protocol is specified in RFC8484. This document considers Centralized DoH deployment, which seems one likely way that DoH may be implemented, based on recent industry discussions and testing. This describes that implementation model, as well the potential associated risks and issues. The document also makes recommendations pertaining to the implementation of DoH, as well as recommendations for further study prior to widespread adoption.
Abstract Argumentation Frameworks and Their Semantics

Argumentation Frameworks and Their Semantics Pietro Baroni, Martin Caminada, Massimiliano Giacomin abstract. The current chapter presents an overview on the state of the art of Dung’s abstract argumentation frameworks and their semantics, covering both some of the most influential literature proposals and some general issues concerning semantics definition and evaluation. As to the former point the chapter reviews Dung’s original notions of complete, grounded, preferred, and stable semantics, as well as a variety of notions subsequently proposed in the literature namely, näıve, semistable, ideal, eager, stage, CF2, and stage2 semantics, considering both the extension-based and the labelling-based approaches with respect to their definitions. As to the latter point the chapter analyzes the notions of argument justification and skepticism comparison and discusses semantics agreement. The current chapter presents an overview on the state of the art of Dung’s abstract argumentation frameworks and their semantics, covering both some of the most influential literature proposals and some general issues concerning semantics definition and evaluation. As to the former point the chapter reviews Dung’s original notions of complete, grounded, preferred, and stable semantics, as well as a variety of notions subsequently proposed in the literature namely, näıve, semistable, ideal, eager, stage, CF2, and stage2 semantics, considering both the extension-based and the labelling-based approaches with respect to their definitions. As to the latter point the chapter analyzes the notions of argument justification and skepticism comparison and discusses semantics agreement.
Towards Automated Characterization of Malware's High-level Mechanism using Virtual Machine Introspection

One of the goals of malware analysis is to figure out the intent ion of an attacker, namely high-level mechanism. Since malicious activities are typically performed b y combining multiple APIs, to identify the malicious intention, it is needed to inspect the series of APIs to analy ze its semantics. In traditional malware analysis, this task generally relies on manual efforts of experts. The re is no methodology for associating multiple APIs and identifying the malicious intention in an automated man ner. In this paper, we propose a virtual machine introspection-based method for automatically identifyin g high-level mechanisms. We developed Spaniel, a prototype system, which uses taint analysis to track malici ous processing that derives from the data read from a specified file and collects the traces of malicious activiti es. For evaluation, we used adversary behavior models defined in ATT&CK and Spaniel identified key indicator s that cover 26% of those models.
Control and Analysis of MPPT Techniques for Standalone PV System with High Voltage Gain Interleaved Boost Converter

In photovoltaic (PV) power generating systems DC-DC converters acts as an interface between the PV source and the load. This paper deals with the use of a high voltage gain three- phase interleaved DC-DC boost converter (IBC) for the standalone PV system applications. Interleaving technique provides high power capability and reduces the voltage stress on the power semiconductor devices. In PV system maximum power point tracking (MPPT) technique is used to track the maximum power from the PV panel. To extract the maximum power from the PV panel at different irradiation levels radial basis function network (RBFN) based MPPT algorithm is developed. The output power of the high voltage gain three-phase IBC with RBFN based MPPT controller is compared with traditional P&O and fuzzy logic based MPPT controllers at different irradiation levels. The performance analysis of P&O, fuzzy logic and RBFN MPPT algorithms with high voltage gain three-phase IBC is done by using MATLAB/SIMULINK.
An Introduction to Data Aggregation Techniques in Wireless Sensor Networks and Their Application

One data aggregation method in Wireless Sensor Networks (WSN) is sending local representative data to the sink node based on the spatial correlation of sampled data. In this paper, we highlight the problem that the recent spatial correlation model of sensor nodes’s data is not appropriate for measuring the correlation in a complex environment. In addition, the representative data are inaccurate when compared with real data. Therefore, we propose the data density correlation degree, which is necessary to resolve this problem. The proposed correlation degree is a spatial correlation measurement that measures the correlation between a sensor node’s data and its neighboring sensor node’s data. Based on this correlation degree, a data density correlation degree (DDCD) clustering method is presented in detail so that the representative data have a low distortion on their correlated data in a WSN.
A Three-Tier Architecture for Securing IoV Communications Using Vehicular Dependencies

Internet of Vehicles (IoV) incorporates environmental entities and networks for information exchange and service provisioning in road-side communications. Smart vehicle-assisted communications are exposed to higher risks as they communicate using wireless medium. Denial of service (DoS) types of attack gain control over the wireless medium to isolate resources and services to the end users. Reputation-based vehicle-assisted communication (RVAC) proposed in this paper mitigates the DoS attack by using global and local dependences of the vehicles. Global and local dependences are modeled by observing the transmission attributes associated with the vehicles. To ease adversary mitigation, the process of assessing vehicle attributes and decision-making is distributed across three tiers, categorized in the IoV architecture. The central authority (CA) works in a co-operative manner with the road-side units (RSUs) to ensure only legitimate vehicles participated in the communication process. The efficiency of the proposed RVAC is assessed and verified through the extensive simulation results. The RVAC performance is evaluated based upon detection time, communication loss, delay, false positive factor, and vehicle selection. The impact of vehicle density and adversary ratio is considered in the performance evaluation. The experimental results illustrate that the proposed RVAC retains a highly reputed vehicle selection rate with fewer false positives, less communication loss, and shorter communication delay and detection time.
Image Restoration by Combined Order Regularization With Optimal Spatial Adaptation

Total Variation (TV) and related extensions have been popular in image restoration due to their robust performance and wide applicability. While the original formulation is still relevant after two decades of extensive research, its extensions that combine derivatives of first and second orders are now being explored for better performance, with examples being Combined Order TV (COTV) and Total Generalized Variation (TGV). As an improvement over such multi-order convex formulations, we propose a novel non-convex regularization functional which adaptively combines Hessian-Schatten (HS) norm and first order TV (TV1) functionals with spatially varying weight. This adaptive weight itself is controlled by another regularization term; the total cost becomes the sum of this adaptively weighted HS-TV1 term, the regularization term for the adaptive weight, and the data-fitting term. The reconstruction is obtained by jointly minimizing w.r. t. the required image and the adaptive weight. We construct a block coordinate descent method for this minimization with proof of convergence, which alternates between minimization w.r. t. the required image and the adaptive weights. We derive exact computational formula for minimization w.r. t. the adaptive weight, and construct an ADMM algorithm for minimization w.r. t. to the required image. We compare the proposed method with existing regularization methods, and a recently proposed Deep GAN method using image recovery examples including MRI reconstruction and microscopy deconvolution.
Optimization Weighted Matrix of Non-Negative Matrix Factorization for Image Compression

Many methods have been applied in image compression and Non-negative matrix factorization (NMF) is one of some approach which could be applied in image compression. Non-Negative Matrix Factorization (NMF) was a realtively new approach to decompose data into two factors with non-negative entries. This paper shows that the NMF method can be applied in image compression using a model of 2x2 pixels weighted matrix rather than using the model of 200x200 pixels, 10x10 pixels and 4x4 pixels weighted matrix. This paper also shows that the 2x2 Weighted matrix model example of ((65536, 1), (1, 65536)) was the best weighted matrix for NMF image compression. Finally, this research proved that by using the weighted matrix with higher determinant value could gain smaller size compressed image.
Commercial wireless versus standard stationary EEG systems for personalized emotional brain-computer interfaces: a preliminary reliability check

We present a preliminary data-based assessment of measurement reliability of the commercial 14-electrode Emotiv EPOCTM EEG wireless system in distinguishing between electrophysiological states of emotional function, as compared to a standard research-lab stationary 32-electrode EEG system for personalized single-individual use. Individual observers completed two tasks designed to elicit neural changes in emotional arousal and valence while simultaneously recording their EEGs with both systems in separate sessions. Participants observed emotion-laden words from the ANEW database and images from the IAPS database, both widely used and validated databases for emotional processes in multidisciplinary research. The pattern of results distinguished between high and low arousal and valence states using the stationary traditional system, but not the commercial device. Also, the latter device recorded EEG band frequencies at a much lower resolution and frequency range than the standard system. These findings suggest poor validity when using the commercial device and therefore should be cautioned against in a research setting.
Support Vector Machine State Estimation

ABSTRACT The power system state estimator based on the support vector machine (SVM) and the weighted least squares (WLS) method is presented in the paper. The WLS provides state estimations necessary for creating SVM model which is then used for state estimation. The developed algorithm was tested on the IEEE systems, and the performance indicators were calculated in order to compare the accuracy of estimation and the measurement error filtering. The results indicate that the proposed hybrid model outperforms the classical WLS-based state estimation in terms of accuracy and improves measurement error filtering in comparison to the classical estimator.
Towards a Quantitative Approach for Security Assurance Metrics

The need for effective and efficient evaluation schemes of security assurance is growing in many organizations, especially Small and Medium Enterprises (SMEs). Although there are several approaches and standards for evaluating application security assurance, they are qualitative in nature and depend to a great extent on manually processing. This paper presents a quantitative evaluation approach for defining security assurance metrics using two perspectives, vulnerabilities and security requirements. While vulnerability represents the negative aspect that leads to a reduction of the assurance level, security requirement improves the assurance posture. The approach employs both Goal Question Metric (GQM) and Common Vulnerability Scoring System (CVSS) methods. GQM is used to construct measurement items for different types of assurance metrics and assess the fulfillment of security requirements or the absence of vulnerabilities, and CVSS is utilized to quantify the severity of vulnerabilities according to various attributes. Furthermore, a case study is provided in this work, which measures and evaluates the security assurance of a discussion forum application using our approach. This can assist SMEs to evaluate the overall security assurance of their systems, and result in a measure of confidence that indicates how well a system meets its security requirements. Keywords–Quantitative security assurance metrics; Security testing; Goal question metric (GQM); Common vulnerability scoring system (CVSS); Security metrics.
Design of Compact Tri-Band Gysel Power Divider With Zero-Degree Composite Right-/Left-Hand Transmission Lines

In this paper, a tri-band Gysel power divider (PD) based on zero-degree composite right-/left-handed (CRLH) transmission lines (TLs) is presented. Compared to the generalized design methods in which multiple passbands are realized by replacing conventional impedance transformers with quarter-wavelength CRLH-TLs, the employed zero-degree CRLH-TLs include high design flexibility for the characteristic impedance and phase responses of the composed left-handed and right-handed TLs, which can be used to separately control the bandwidths of the three passbands. In addition, the microstrip-to-slotline transition is utilized to replace the conventional 180° TL, which can expand the realizable range of the three center frequencies and reduce the circuit size, as well. The proposed theory is validated using two designs: One involves a broadband PD with a center frequency of 2.5 GHz, while the other is a tri-band PD centered at 3.5 GHz, 2.5 GHz, and 1.5 GHz. Good agreement between the simulated and measured results are obtained; for the first PD, a fractional 3-dB bandwidth of up to 116% is achieved, whereas, for the tri-band PD, a size reduction of 80% is achieved with good impedance matching and isolation performance.
Information retrieval from facial expression using voting to assert exigency

Abstract In diurnal life, there are circumstances in which communication without words is required, emoticons or facial expressions are used to exchange virtual dialogues. This genre of communication completely injects into situation(emergency), where the sole way to interconnect is by performing some unusual activities, eye blinks or through some emotions. The paper proposes a Sagacious Information Recuperation Technique (SIRT) for sensing an emergency by capturing intelligent information from behavioral (emotions) and biological (eye blinks) changes. The proposed technique receives input from real-time human attributes: eye blinks and emotions. One of the parameter of proposed SIRT is further validated through an implementation of a real time facial expression recognition system(RTFER) that effectively identifies various emotions in real time scenario. LBP features collected from training dataset (cohn-kanade) are further classified using Support Vector Machine (SVM), Multilayer Perceptron(MP) and a Voting system involving SVM, MP and ADAB(ADABoost) classification algorithms. Experiment on Cohn-Kanade (CK) dataset illustrate that Voting system with multiple classifiers outperform methods using single descriptors. Receiver Operating Characteristics (ROC), area under the curve, sensitivity, fall-out, precision, and f-measure are used to show the performance of the system qualitatively and quantitatively. Accuracy with the proposed Voting system with reduced attribute dataset is increased to 81.82% in comparison with existing support vector machine (75.38%) and multilayer perceptron (78.41%).
Abnormal events detection using deep neural networks: application to extreme sea surface temperature detection in the Red Sea

Abstract. We present a method based on deep learning for detecting and localizing abnormal/extreme events in sea surface temperature (SST) of the Red Sea images using training samples of normal events only. The method operates in two stages; the first one involves features extraction from each patch of the SST input image using the first two convolutional layers extracted from a pretrained convolutional neural network. In the second stage, two methods are used for training the model from the normal training data. The first method uses one-class support vector machine (1-SVM) classifier that allows a fast and robust abnormal detection in the presence of outliers in the training dataset. In the second method, a Gaussian model is defined on the Mahalanobis distances between all normal training data. Experimental tests are conducted on satellite-derived SST data of the Red Sea spanning for a period of 31 years (1985–2015). Our results suggest that the Gaussian model of Mahalanobis distances outperformed 1-SVM by providing better performance in terms of sensitivity and specificity.
A Session Initiation Protocol (SIP) Usage for Incremental Provisioning of Candidates for the Interactive Connectivity Establishment (Trickle ICE)

The Interactive Connectivity Establishment (ICE) protocol describes a Network Address Translator (NAT) traversal mechanism for UDP-based multimedia sessions established with the Offer/Answer model. The ICE extension for Incremental Provisioning of Candidates (Trickle ICE) defines a mechanism that allows ICE Agents to shorten session establishment delays by making the candidate gathering and connectivity checking phases of ICE non-blocking and by executing them in parallel. This document defines usage semantics for Trickle ICE with the Session Initiation Protocol (SIP). The document also defines a new SIP Info Package to support this usage together with the corresponding media type. Additionally, a new SDP 'end-of- candidates' attribute and a new SIP Option Tag 'trickle-ice' are defined.
Subpixel Unsynchronized Unstructured Light

This paper proposes to add subpixel accuracy to the unsynchronized unstructured light method while achieving high-speed dense reconstruction without any camera-projector synchronization. This allows scanning faces which is notoriously difficult due to involuntary movements on the part of the model and the reduced possibilities of 3D scanner approaches such as laser scanners because of speed or eye protection. The unsynchronized unstructured light method achieves this with low-cost hardware and at a high capture and projection frame rate (up to 60 fps). The proposed approach proceeds by complementing a discrete binary coded match with a continuous interpolated code which is matched to subpixel precision. This subpixel matching can even correct for erroneous camera-projector correspondences. The obtained results show that highly accurate unfiltered 3D models can be reconstructed even in difficult capture conditions such as indirect illumination, scene discontinuities, or low hardware quality.
New technologies to accelerate additive manufacturing processes on an industrial scale

This research project is a study of the additive manufacturing technology, from a point of view of the current challenges that this technology facesin their process.The main intention of this project isto study and discuss AM technology in today’s world, making also a forecast of the development of this technology.In a primaryphase,will be presented a state of the art of 3D printing and how it stands nowadays, after that,are presented some materials and processes, comparing the materials depending the strengthand weaknesses.Then,a discussion of3D printing’s importance in the overall industrywill take place,as well as an analysis of AMprocess of polymers andmetals. It will bediscussed the current path of AM development in Aeronautical, Automobile and Medical sector.Finally, we analyse some economic issues regarding 3D printing and the changes it can induce on the overall supply chain.We also concluded two interviews, the firstin a technology foundation in Catalunya-Spain, and the other in a medtech company.
Wide and Deep Reinforcement Learning for Grid-based Action Games

For the last decade Deep Reinforcement Learning has undergone exponential development; however, less has been done to integrate linear methods into it. Our Wide and Deep Reinforcement Learning framework provides a tool that combines linear and non-linear methods into one. For practical implementations, our framework can help integrate expert knowledge while improving the performance of existing Deep Reinforcement Learning algorithms. Our research aims to generate a simple practical framework to extend such algorithms. To test this framework we develop an extension of the popular Deep Q-Networks algorithm, which we name Wide Deep Q-Networks. We analyze its performance compared to Deep Q-Networks and Linear Agents, as well as human players. We apply our new algorithm to Berkley’s Pac-Man environment. Our algorithm considerably outperforms Deep Q-Networks’ both in terms of learning speed and ultimate performance showing its potential for boosting existing algorithms.