Miracle-dz/UniFAR-FTUs huggingface dataset:
Identifying the Right Person in Social Networks with Double Metaphone Codes

With English being considered as a universal language of communication and interaction, the study of the words that sound the same but spell differently has become an extensive area of research. In the context of social networks, people names can have different spellings yet the same pronunciation (i.e., homophonic names). Chris and Kris, John and Jon, as well as Justin and Justyn are some examples of these homophonic names. When exploring historical records, there may be situations in which two different entries correspond to the same individual, and the only difference in the two entries might be the spelling of the individual's name. Similar situations occur when individual's names are translated from their native language to another language. In this paper, we present a solution to identify the right person in social networks. The solution incorporates graph theory and linear algebra, and makes good use of double metaphone codes to measure the phonetic distance between two names. Based on the measured phonetic distance, homophonic names are grouped into the same cluster. This phonetic distance-based clustering helps to identify the right person in social networks.
Heuristically Speeding up Anonymous Dynamic Network Computations

Motivated by the current large gap between lower and upper bounds for Counting, i.e. the problem of computing the number of nodes, in Anonymous Dynamic Networks, we study heuristic modifications of the deterministic Methodical Multi-counting (MMC) protocol [1] aimed to reduce its running time in practical applications. The heuristic presented here is the result of an extensive experimental study tuning all the parameters of the algorithm while maintaining correctness. We evaluate our heuristic MMC with thorough simulations on worst and average case topologies under different assumptions of dynamicity. Our results achieve speed-ups up to one or two orders of magnitude, which demonstrates that, as expected, the theoretical analysis overestimates the necessary running time to cope with adversarial scenarios, but in practice it is possible to tune the algorithm for specific topologies or other network characteristics without sacrificing correctness.
Quadric-based Traffic Sign Landmarks Initialization for Object-oriented EKF-SLAM

Visual Simultaneous localization and mapping (vS-LAM) are estimation problems in which a vehicle is localizing itself while reconstructing a map of the environment. Such approaches are usually solved using handcrafted 2D features resulting as simple geometrical shape landmarks. The main problems of such features are: (1) they are not robust enough and (2) are often lost or incorrectly matched over consecutive frames and (3) they do not carry any semantic information. In this paper, we propose to consider higher level semantic objects as landmarks. We focus on Traffic Signs that are excellent candidates for this role: they have a simple geometrical shape, can be easily detected and carry a strong semantic information. We propose to include such new high level landmarks into an EKF-SLAM framework. For this purpose, we developed an efficient way to estimate the 7 degrees of freedom of polygonal and circular traffic signs from a single stereo-vision observation to initialize such landmarks in a very accurate way so it can be easily used in the EKF based estimation process.
Multi-user Beam Alignment for Millimeter Wave Systems in Multi-path Environments

Directional transmission patterns (a.k.a. narrow beams) are the key to wireless communications in millimeter wave (mmWave) frequency bands which suffer from high path loss, severe shadowing, and intense blockage. In addition, the propagation channel in mmWave frequencies incorporates only a few number of spatial clusters requiring a procedure, called beam alignment (BA), to align the corresponding narrow beams with the angle of departure (AoD) of the channel clusters. In addition, BA enables beamforming gains to compensate path loss and shadowing or diversity gains to combat the blockage. Most of the prior analytical studies have considered strong simplifying assumptions such as i) having a single-user scenario and ii) having a single dominant path channel model for theoretical tractability. In this study, we relax such constraints and provide a theoretical framework to design and analyze optimized multiuser BA schemes in multi-path environments. Such BA schemes not only reduce the BA overhead and provide beamforming gains to compensate path loss and shadowing, but also provide diversity gains to mitigate the impact of blockage in practical mmWave systems.
Network Monitoring and Analysis

Increase in the development of Intranet usage take part a key role in monitoring and controlling Internet traffic within an organization. For getting good cyber security solutions, it is necessary to have proper network monitoring in any organizations. Network can be monitored with variety of tools available in the market. This paper discusses about network monitoring approaches in CPU and GPU along with that compares some of the network monitoring tools available in the market.
Global Connectivity in Software Defined Network

Today internet has moved from being a means of convenience to a platform to do business and be successful in it. Current computer network architecture is scalable but lacks provision for innovation. It is shown that Software defined network (SDN) is flexible and offers huge scope for innovation in terms of application programming interface (APIs). Topology discovery is essential in computer networks for management of the network, routing and application performance prediction. This paper discusses topology discovery in software defined network and thus highlighting research avenues in SDN topology discovery process.
Block Chain Techniques to Detect Attacks on VANET System: A Survey

Road incidents and their sequences are growing worldwide significantly, increasing the need for strategies to include road protection and regulation. This is a big goal for modern countries to enhance the quality of life of people through the development of an intelligent system of transport (ITS). Adhoc vehicle networks (VANETs) are recognized for their efficiency in applying such a concept. VANET will enhance road safety and convenience for travelers. Such technology is however already vulnerable to several bugs, which result in many security challenges, which must be overcome before virtually and securely deploying VANET technology. The attack is one of the key threats to VANET availability. In Blockchain, the Merkle tree is also a hash-based data structure that can generalize the hash list. It is really a tree structure and that each node is a hash of data, and all nodes are a hash of their offspring. Blockchain are some Features which is immutability, distributed and trust-less environment, privacy, transparency and faster transactions In this paper, the emphasis is on analyzing the major attacks on VANET as well as discuss possible solutions for such attacks with a focus on Blockchain Technology-based solutions.
Novel Synthesis Methodology of Lowpass Networks Comprising Generalized Cascaded Box Configurations

This work presents a novel methodology enabling the synthesis of lowpass networks comprising generalized cascaded box configurations (GCBCs). The methodology derives the proposed networks from their counterparts comprising generalized cascade quadruplets (GCQs) which are essentially derived from their canonical counterparts. A lowpass filter comprising two GCBCs is synthesized and the steps involved are presented demonstrating the design methodology.
IOT based Smart Water Pump Switch

The world population is increasing day by day, so demands for the resources like water and food also increases because these are the basic need for the people. People need the water for their daily uses. Even though your earth is covered mostly of water but it is not useful for humans to use. There is less amount of water that is useful. To save the water without wasting it. To reduce the water from wasting this is designed an automated switch that can automatically on and off the device when the tank is full of water, These reduce the overflow of water, it uses IOT (sensors)to make these processes automated. When the water level touches the sensor it turns off the water supplying pump. Since the electricity demand is very high, automatic water controllers save energy. To prevent over the labor of pumping machine and prevent it from getting worse. This water pump can be used for many purposes in the companies like it can be used as Liquid level detector and fuel detect the device, It also helps us to indicate the sudden increase of water during floods. A fully automated that is low cost and low maintenance with good compact and design which saves water and energy. This device is cost efficient.
Scaleplus: Towards Fast Scaling of Distributed Streaming Dataflows

Streaming dataflows are usually deployed as longterm services and suffer from the fluctuation in arrival rates. Many scaling controllers provide the elasticity to scale up or down the dataflow to meet the target throughput. However, for most controllers with specific target Service Level Objective (SLO), it is difficult to use a single decision to accurately complete a scaling action. Reaching the target iteratively will increase the completion time of the scaling, which leads to the performance degeneration or the wasting of resources. In this paper, we present Scaleplus, which provides pluggable services for helping iterative scaling controllers make more accurate decisions to complete the scaling rapidly. Scaleplus builds decision models incrementally without the off-line sampling procedure, which can be used out of the box, then a recommendation strategy is leveraged to recommend accurate decisions. Besides, Scaleplus can be flexibly integrated with only 3 HTTP APIs. We evaluate Scaleplus with 3 different scaling controllers on Apache Flink and Apache Heron. For the simulated 9-day traces of Twitter, Scaleplus reduces the scaling duration by 36.5%, 51.1% and 54% respectively compared with DS2, mRB and Dhalion.
Joint Localization and Channel Estimation for UAV-Assisted Millimeter Wave Communications

In this paper, we consider millimeter Wave (mmWave) communications between an Unmanned Aerial Vehicle (UAV) and a base station. By assuming that both communication nodes are equipped with arrays of multiple antenna elements, we focus on the joint estimation of the UAV position and the Multiple Input Multiple Output (MIMO) channel coefficients. Capitalizing on the line-of-sight signal propagation conditions and the beamspace representation of the wireless channel, we first estimate the UAV position. Using this estimation, we then present a matrix completion formulation for the recovery of the remaining non-line-of-sight components of the MIMO channel matrix, which is efficiently solved via the alternating direction method of multipliers. Selected simulation results for a 28GHz channel model verify that the proposed scheme is beneficial both in terms of estimation accuracy and resources utilization.
Interference Mitigation for Cooperative MIMO Channels with Asymmetric Feedback

Current cooperative transmission strategies for distributed MIMO systems are typically designed by assuming perfect, or at least perfectly shared, channel state information at the transmitters (CSIT). However, when this assumption is not met, the naïve application of existing schemes encounters severe performance degradation. Recently, an information theoretical result unveiled the intriguing phenomenon that, for distributed MIMO systems with asymmetric CSIT, optimal encoding may require the transmission of a number of data streams that goes beyond the classical upper bound predicted by the number of antennas in the system. In this work, we explore the implications of the aforementioned result from precoder design point of view. More precisely, we propose a method for distributed precoding design which optimally minimizes the expected interference. As in the information theoretical result, the key idea lies in allowing the transmission of additional data streams. We show that this trick helps in transforming an otherwise difficult robust optimization problem into a convex formulation. Numerical simulations corroborate the robustness gain in terms of achievable sum-rate of the proposed method over traditional schemes.
A Wide Scanning-Angle mmWave AiP Antenna Module for 5G Portable Terminals

Millimeter-Wave (mmW) communication in 5G mobile terminals requires mmW antenna to have low-profile design structure, wide-bandwidth and large-angle beam scanning. In this work, $\mathrm{a}4 \times2$ array is presented which can be applied for the mmW communications of 5G mobile terminals. The array element design adopts a patch antenna structure with a shorting pin, which can excite an extra zero mode resonant frequency. Combined the 2nd zero mode and the original TM01 mode, the proposed array can give a broadband from 23.5-28 GHz, a large-angle beam scanning with ±60° coverage, as well as a low-profile substrate with 0.508 mm. Then, we designed an antenna in package (AiP) antenna module based on the proposed array, and the measured results have demonstrated good performance.
Genetic Algorithm based Hardware Trojan Detection

There is an increasing concern about possible hostile modification done to ICs, which are used in various critical applications. Such malicious modifications are referred to as Hardware Trojan. A novel procedure to detect these malicious Trojans using Genetic algorithm along with the logical masking technique which masks the Trojan module when embedded is presented in this paper. The circuit features such as transition probability and SCOAP are used as suitable parameters to identify the rare nodes which are more susceptible for Trojan insertion. A set of test patterns called optimal test patterns are generated using Genetic algorithm to claim that these test vectors are more feasible to detect the presence of Trojan in the circuit under test. The proposed methodologies are validated in accordance with ISCAS ’85 and ISCAS ’89 benchmark circuits. The experimental results proven that it achieves maximum Trigger coverage, Trojan coverage and is also able to successfully mask the inserted Trojan when it is triggered by the optimal test patterns.
Data Fusion for Multipath-Based SLAM: Combining Information From Multiple Propagation Paths

Multipath-based simultaneous localization and mapping (SLAM) is an emerging paradigm for accurate indoor localization constrained by limited navigation resources. The goal of multipath-based SLAM is to support the estimation of time-varying positions of mobile agents by detecting and localizing radio-reflective surfaces in the environment. In existing Bayesian methods, a propagation surface is represented by the mirror image of each physical anchor (PA) across that surface – known as the corresponding “virtual anchor” (VA). Due to this VAs representation, each propagation path is mapped individually. Existing methods thus ignore inherent geometrical constraints across different paths that interact with the same surface, which limits accuracy and speed. In this paper, we introduce an improved statistical model and estimation method that enables data fusion in multipath-based SLAM. By directly representing each surface with a MVA, geometrical constraints across propagation paths are also modeled statistically. A key aspect of the proposed method based on MVAs is to check the availability of single-bounce and double-bounce propagation paths at potential agent positions by means of ray-tracing (RT). This availability check is directly integrated into the statistical model as detection probabilities for propagation paths. Estimation is performed by a sum-product algorithm (SPA) derived based on the factor graph that represents the new statistical model. Numerical results based on simulated and real data demonstrate significant improvements in estimation accuracy compared to state-of-the-art multipath-based SLAM methods.
Photoplethysmography Based Detection of Social Stress

Social stress is one of those major psychological ailments which are endured by all pupils in life as a sequel of fiscal privations, fretting over laborious academic schedules & nervous agitation owing to companionships; which eventually lead towards the aberrant functioning of physiological systems. Beaucoup tacks have been debuted to Figure out the intensity of stress via conventional means like questionnaires, psychological therapies, and scrutinization of multifarious bio-signals, for instance; electrocardiography (ECG), galvanic skin resistance (GSR), photoplethysmography (PPG), electroencephalography (EEG) and electromyography (EMG). Here, we have proposed a simple PPG-based model which examines the alterations of the pupil’s circulatory system non-invasively. Five pupils who were enduring social stress in life were taken into account and a questionnaire was fabricated to conduct their 30 minutes interviews separately. The total of acquired samples during interview sessions were 222 of which 110 were of normalcy in which questions related to happiest nostalgic memories were asked and 112 were of stress in which factors behind social stress were asked. The recorded samples were firstly pre-processed followed by segmentation using empirical mode decomposition (EMD) to get the region of interest after which testing & training through Ensemble Classifier (Bagged Trees) was carried out using 25 features. This framework is 92.8% accurate using 10fold cross-validation with 12 features among whom median and mean frequencies are the most valuable features in this regard.
A Reflection of Driver Drowsiness Detection Techniques

Creating savvy frameworks to stop vehicle mishaps are extremely powerful in limiting catastrophes cost. One among the elements which assume a pivotal part in accidents is that the human mistakes including driving exhaustion relying on new brilliant strategies. A spread of tiredness recognition techniques subsists which screen tiredness of a person who drives the vehicle also, warn the chauffeur in the event that they are not focusing on driving. The applicable highlights are regularly removed from outward appearances like mouth opening state, eye conclusion, and upper portion of the body movements for deriving degree of sluggishness. Natural state of the drivers’ body, likewise as motor conduct, is investigated for driver sleepiness discovery. This analysis grants a complete examination of the winning techniques for driver laziness identification and presents an inside and out investigation of broadly utilized grouping procedures during this respect. This particular survey, we group the predominant strategies into three classifications: social, transport, and biological boundaries-based strategies
An overview of soft computing techniques on Review Spam Detection

Individuals that work with web services commonly focus on internet-based feedback to make decisions about shopping via online, booking an apartment, renting banquette halls etc., because people believe that making a decision which is based on the views of others, can contribute in making the right decision about their purchases. While publishing negative reviews or comments, brings financial benefit to manufacturers via online review websites and also increases the review spam happenings. Sentimental misinformation in feedback has therefore become a significant challenge for people to make decisions about their purchase and thus decrease the credibility of review on websites. The malicious spam identification of views is therefore an important activity in the fields of machine learning (NLP- Natural Language Processing). The conventional bag-of-words model is used to recognize the text analysis characteristics of most of the current research on review spam detection and to apply traditional machine learning models such as Support Vector Machines, Random forest, Decision tree and Naïve Bayes as classifiers. With increase in the effective applications of Soft computing, the spam detection framework has achieved improved efficiency over traditional machine learning and works well with unique models and provides better solutions to complicated real-life scenarios. This paper discusses the overview of soft computing techniques in spam detection.
A Web API Recommendation Method with Composition Relationship Based on GCN

Mashup belongs to one of the most popular representations of software applications, which is generated by combining different kinds of APIs and data resources. APIs have developed rapidly, which accelerates the development of mashup. However, it brings the challenge of finding the right APIs for developers. Although there are many API recommendation approaches based on collaborative filtering (CF) techniques, they suffer the problem of data sparsity and the composition relationship between Web APIs in the mashup is not explored comprehensively by existing recommendation methods. To address the challenges, this study puts forward SVGAE (Semantic Variational Graph Auto-Encoder) based on the graph convolutional network, which is an end-to-end approach. Firstly, we utilize the API composition relationship in the mashup to construct the API graph. Next, side information of APIs is incorporated into the model, in which doc2vec is employed to process textual description of API and the sum pooling layer is used to extract multi-categorical features. We finally utilize a variational graph auto-encoder model as a link prediction task for API recommendation. The superiority of the proposed method has been proved by the experimental results.
Lie Detection Using Fuzzy Ensemble Approach With Novel Defuzzification Method for Classification of EEG Signals

The ability to analyze deceit behavior in humans is an extremely vital issue where legal and security purposes are considered. Using the P300 component of event-related potential (ERP) [electroencephalography (EEG)], we have developed a concealed information test (CIT), which provides an easy and effective way to identify whether an individual carries some concealed information. The P300 component is elicited when an individual is provoked with stimuli related to his/her memories. Taking advantage of this P300 feature, several images of certain familiar and nonfamiliar faces are shown to the subjects during the experiment. Throughout the image presentation, EEG data of the subject are recorded. Signal preprocessing, processing, and classification algorithms are then implemented on the recorded EEG data. This article aims to classify the recorded EEG data for CIT. For extracting the spatial features from the recorded EEG data (of range 0.5–30 Hz), a common spatial pattern has been used. A fuzzy integrator system is developed using classifier performance measures as the antecedents. A novel defuzzification function called generalized mean of maxima (GMoM) has also been proposed to achieve a classification score or value. The experimental results demonstrate the outperformance of fuzzy-based CIT with significant classification accuracy.
A Meta-analysis of Role of Network Intrusion Detection Systems in Confronting Network Attacks

Traffic on the network is increasing immensely. Unprecedented development and close integration of devices for the Internet of Things (IoT) have contributed to an enormous amount of data in recent years. A persistent concern is the detection and prevention of network intrusions. In this paper, we have analyzed the Network Intrusion Detection System (NIDS) deployment, methodology, and the taxonomy of attacks detected by a NIDS. The purpose of the study is to emphasize the role of NIDS in confronting attacks. Deep Learning (DL), has significantly aided Machine Learning (ML) inefficiencies and produced time and cost-effective security solutions. By analyzing the work done by various researchers, it has been observed that deep learning algorithms give better accuracy and performance in confronting attacks with KDD Cup- 99 Dataset.