Miracle-dz/UniFAR-FTUs huggingface dataset:
Fine Tuning Consumer Feedback Based Recommender Systems Using Deep Learning Models on Word Vector Space Representations

In almost every consumer-based service domain today, there exists a feedback system which captures how the consumer felt about the services they received. This feedback system makes or breaks the business of the service provider as a lot of future purchase decisions are driven based on this. Based on the feedbacks received, organizations are trying to drill down on the negative aspects and market the positive aspects with more outreach. Our innovation is focused on extracting maximum value from the underlying feedback system - by automatically deriving semantic context and lexical associations for each document sub-unit of the written feedbacks using word vector representation and word embeddings. Further, basis the information extracted from the analysis unit, a secondary recommendation unit will be set up that automatically analyzes the output from the analysis unit and accordingly performs latent structure identification and polarity analysis to suggest substitute options or viable alternatives based on the consumers' behavior and feedback. The ultimate objective is to produce automated contextualization based feasible recommendations using deep learning models, having minimal supervision and error rate.
A Reduce Device Count Nine Level MLI Using Switched Capacitors

Multilevel inverters are costly when it employs a higher number of dc sources and power semiconductor switches. This paper suggests a topology which uses one dc source, and three self-balanced capacitors and six switches to generate nine-level along with polarity generation using H-bridge. The switching scheme is made in such a manner that capacitors get balanced automatically. The modes of operation and charging and discharging of capacitors is presented with illustrations. The simulation result for this topology is verified on MATLAB 2018a. The simulated results show the output voltage levels, capacitor voltage profile.
Measurement of Principal Modes in Few-Mode Fibers by S2 Method

Using principal modes (PMs) can avoid the inter-mode crosstalk in fiber-optic communication and reduce the complexity of digital signal processing. In this paper, a new detection method for PMs based on spatially and spectrally resolved imaging (S2) is used to recover their intensity distributions and inter-mode dispersion parameters. This method collects the optical interference information on a two-dimensional plane at different frequencies. The collected data can be used to characterize the principal modes, including their patterns and differential mode group delays. Due to the frequency invariance of PMs even in mode coupling state, which is studied carefully, this proposed method is shown to be robust. Analyses based on a four-mode fiber show that the distributed fluctuation of the coupling coefficients break the frequency invariance of PMs. We experimentally measure the mode characteristics of a four-mode fiber with S2 method. The results show that the mode group delays of degenerate modes can be separated in mode coupling state, which is exactly appropriate for the modified measurement method.
A Multifactorial Short-Term Load Forecasting Model Combined With Periodic and Non-Periodic Features - A Case Study of Qingdao, China

In this paper, the load time-series measured from 2016 to 2018 in Qingdao is investigated in order to make a prediction more accurately by using an artificial neural network model combined with some regular and irregular features. The results of spectral analysis show that several periodic variations in diurnal, semidiurnal and weekly frequencies are prominent, and considered as critical parts of predictior variables of the training and test sets in this forecasting model. However, a significant decline of load happens during the national statutory festivals in China, and is more obvious with the longer holidays, which should be taken into account as abnormal conditions. Moreover, both temperatuer and hummity filtered out by mutual information method are also added as two essential weather factors to improve the prediction accuracy, especially in the hot summer. Finally, the comparative results of five different experiments in term of mean absolute percent error show that the forecasting model combined with all periodic and non-periodic factors outperforms the others with one single or a few of factors. This multifactorial model, taking fully account of internal charateristics of load data and external important influences, is proved more suitable to predict the load trend in Qingdao.
Emotion Recognition from Text Stories Using an Emotion Embedding Model

In this paper, we analyze emotions in a story text using an emotion embedding model. Firstly, we collected 144,701 tweets, and each tweet is given an emotional hashtag. Using the emotion hashtag as an emotion label, we built a CNN model for emotion classification. We then extracted the embedding model created during the learning process. We then extracted word embedding layer created during the emotion classification learning process. We defined this as an ‘Emotion embedding model’, and applied it to classify story text emotions. The story text used in emotion analysis was ROC story data, and those story sentences are classified into eight emotions based on plutchik’s emotion model.
Adaptive Tracking Control Based on GFHM for a Reconfigurable Lower Limb Exoskeleton

Paraplegic patients can gain the ability of walking, sitting, standing, ascending stairs and slopes and vice versa by wearing lower limb exoskeletons. As a result of the direct contact between the human and robot, challenges such as unknown disturbances and dynamic uncertainties, which further emphasizes the performance of the control, arise. In this paper, an adaptive tracking control based on Generalized Fuzzy Hyperbolic Model (A-GFHM), a simple structured controller with low computational cost, is proposed for a lower limb exoskeleton. The proposed controller has proven to be highly capable of controlling MIMO non-linear systems with the challenges mentioned. Finally, the proposed real-time method is tested by experimental implementation on an exoskeleton robot manufactured at the Robotic Laboratory of the Ferdowsi University of Mashhad (FUM-Exoskeleton or FUME). By comparing the results with a PID controller, it becomes clear that the proposed method reduces the tracking error by 33.56% and 77.73%, in controlling the knee and hip joints, respectively.
Spatial Shape Estimation of a Tendon-Driven Continuum Robotic Arm Using a Vision-Based Algorithm

Due to the nonlinearity of the continuum manipulators and the absence of direct relation between the actuator space and task space, shape configuration cannot be achieved by knowing the actuator position. To resolve this problem, an estimation algorithm based on vision processing is proposed in this research. In order to estimate the shape of a two-section tendon-driven continuum robotic arm, each section is considered as a circular arc of multiple curvature in the 3D space. To implement the proposed method, few LEDs are implanted along the length of the manipulator; whereas two webcams are used to capture the photos of LEDs which are exploited to determine their position. These positons are next used for shape estimation using the proposed algorithm. Finally, the efficiency of the proposed algorithm is experimentally evaluated on a calibration setup of the robot and the results are discussed.
Energy optimization in WSNs using Successive Addition and Successive Reduction Methods

Power optimization is an important issue in wireless sensor networks, the objective of this research is to design and implement algorithms to optimize the power consumption for the network. Two methods have been proposed in this study. The first method is the Successive Addition Method, SAM, where the algorithm tries to build the operational sensor sets by incrementally adding sensors. The second is the Successive Reduction Method, SRM, where the algorithm removes sensors that have the largest overlap with other sensors in the set. The results of simulation showed that the two algorithms are able to choose the optimum sets and achieve the required levels of area coverage for the optimum total time. It is observed that the SAM outperforms the SRM at lower target coverage. However, the SRM’s performance improves when the target coverage is higher.
Peer Influence and Centrality in Online Social Networks

In this paper we investigate the relationship between the social influence of a friend in a personal friend network in Facebook and the centrality of the friend in that network. In a large-scale experiment, we asked 206 volunteers to allow us access to their personal Facebook friend network. We then analyzed these 206 networks and interviewed each respondent about selected Facebook friends from their personal network. The questions captured the social influence of the friend on Facebook. This included elements such as, response to status reports, trust in the person and interaction with the Facebook friend. The social influence of that peer is then related to the centrality of this Facebook friend. To measure the centrality of a Facebook friend, six network centralities were calculated and related to the respondent's statements. The results show a clear connection between social influence and the centrality of a friend in the personal peer network on Facebook.
A Data Mining Approach to Student Performance Prediction Within Internet-Worked Environments

The use of educational data mining approaches presents numerous data from a variety of educational databases. This data is mined to give useful knowledge and information that can assist in the prediction of student performance perceptions. Considering the use of supervised learning approaches, decision trees can be obtained to assist with the perception predictions. A decision tree is a data classification technique that is used in displaying a result after the data mining process. The use of data mining techniques on data related to student performance assists in extracting valuable information from large datasets available within the institutions. This paper discusses the factors that influence the student learning process in an internet mediated environment. Additionally, this paper uses a decision tree to show which factors affect student performance in these environments. This will enable us determine whether a student is likely to perform well or not to perform well while using the technology in the learning process.
Assessing Impact on System Strength Under High Wind Power Penetration

Integration of renewable energy into grid is increasing rapidly world-wide. Though renewable energy has a good impact on environment, integration with grid weakens the system strength when fault occurs. This study shows the effect on grid system strength at various renewable energy penetration level under fault conditions. The result indicates that the system strength of a grid under high wind power penetration decreases significantly. This paper also shows a possible solution to improve system strength in this situation.
Analysis of Permanent Magnet Synchronous Machines with Fractional Slot Concentrated Windings

Surface mounted permanent magnet synchronous machines with fractional slot concentrated windings are widely used in modern servodrives for industrial and automotive applications. Despite the sub-and higher order harmonics of MMF, which increase inductance of phases and lead to additional losses, this winding design type provides good heat dissipation, shorter length of end-turns and a compact design, and simplifies production reducing the manufacturing costs. The design of such machines is usually developed with FEM software, it requiring special software, skills and time. The paper presents an analytical unified mathematical model to estimate the parameters of such machines based on the given geometry layout. The analytical estimations were compared to FEM simulation as well as to experimental data obtained from both linear and rotating machine types. The parameter determination accuracy with the analytical model approach is as with a FEM model and lies around 10-15%.
Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000

An ever-increasing number of computing devices interconnected through wireless networks encapsulated in the cyber-physical-social systems and a significant amount of sensitive network data transmitted among them have raised security and privacy concerns. Intrusion detection system (IDS) is known as an effective defence mechanism and most recently machine learning (ML) methods are used for its development. However, Internet of Things (IoT) devices often have limited computational resources such as limited energy source, computational power and memory, thus, traditional ML-based IDS that require extensive computational resources are not suitable for running on such devices. This study thus is to design and develop a lightweight ML-based IDS tailored for the resource-constrained devices. Specifically, the study proposes a lightweight ML-based IDS model namely IMPACT (IMPersonation Attack deteCTion using deep auto-encoder and feature abstraction). This is based on deep feature learning with gradient-based linear Support Vector Machine (SVM) to deploy and run on resource-constrained devices by reducing the number of features through feature extraction and selection using a stacked autoencoder (SAE), mutual information (MI) and C4.8 wrapper. The IMPACT is trained on Aegean Wi-Fi Intrusion Dataset (AWID) to detect impersonation attack. Numerical results show that the proposed IMPACT achieved 98.22% accuracy with 97.64% detection rate and 1.20% false alarm rate and outperformed existing state-of-the-art benchmark models. Another key contribution of this study is the investigation of the features in AWID dataset for its usability for further development of IDS. INDEX TERMS IoT security, intrusion detection, feature engineering, mutual information, machine learning, edge computing.
Evaluation of Monocular Visual-Inertial SLAM: Benchmark and Experiment

Simultaneous Localization and Mapping (SLAM) is being developed as a hot topic issue in computer vision which nowadays, is the main core of self-localization and autonomous navigation in robotic technology and unmanned vehicles. In this way, Visual-Inertial SLAM algorithm is a popular strategy to attain high accurate 6-DOF state estimation. But such an accurate system is vulnerable to extreme movements and texture-less environments, and it sometimes fails in confronting such circumstances. In this paper, a tightly-coupled and optimization-based monocular Visual-Inertial SLAM system is proposed, which can tackle the scale ambiguity - a problem that arises by poor initialization. To perform this, the ORB-SLAM as the most reliable feature-based monocular SLAM algorithm has been selected as the base of our study. Then, to improve the accuracy, a Visual-Inertial Odometry (VIO) is carried out that fuses the camera information and Inertial Measurement Unit (IMU) data. We evaluate the performance of our system on the European Robotics Challenge (EuRoC) dataset and compare it with the state-of-the-art algorithms, providing better accuracy in some sequences owing to the improved initialization. Furthermore, we implement the real-world indoor experiment using a monocular-inertial camera to demonstrate the appropriate performance of our system.
Implementation of Ant Colony Optimization in Economic Load Dispatch Problem

This paper present a solution for the economic load dispatch problem of medium and enormous size power system by means of an Ant Colony Optimization. Ant Colony Optimization is utilized to tune the arrangement get by previous towards finding Global optima . Ant colony Optimization is a metaheuristic approach for taking care of hard combinational enhancement issue . Moving ant release a special chemical from his body namely Pheromone, The motivating sources of ACO is the pheromone trail laying and pursue conduct of genuine ants which use pheromone as a correspondence medium. The pheromone trail in ACO fill in as dispersed, numerical data which the insect, use to probabilistic build answer for the issue being tackled. The goal is to limit Generation cost and to keep up a satisfactory framework execution regarding the acceptable points on the generator limits and reactive power yields, bus voltages, shunt capacitor/reactor and power system of transmission lines . The proposed calculation approved on an IEEE 26 bus and IEEE 118 bus system. The outcomes acquired with the proposed methodology are compared with other optimization algorithms.
Aflatoxin production in seeds of wild Arachis species.

(In Fr) (In Es) Of 22 accessions of wild Arachis spp. and 5 of groundnut (A. hypogaea) inoculated with Aspergillus flavus, all supported production of aflatoxin B1. A. duranensis and A. cardenasii, earlier reported to possess near absolute resistance to aflatoxin production, supported substantial amounts of aflatoxin B1 but significant differences in levels of aflatoxin production were found between the accessions of the species tested. The groundnut genotype OG 35-1, previously reported to support a low level of aflatoxin production was found to support a substantial level of aflatoxin production. , 8 ref. , (In Fr) (In Es) Of 22 accessions of wild Arachis spp. and 5 of groundnut (A. hypogaea) inoculated with Aspergillus flavus, all supported production of aflatoxin B1. A. duranensis and A. cardenasii, earlier reported to possess near absolute resistance to aflatoxin production, supported substantial amounts of aflatoxin B1 but significant differences in levels of aflatoxin production were found between the accessions of the species tested. The groundnut genotype OG 35-1, previously reported to support a low level of aflatoxin production was found to support a substantial level of aflatoxin production.
TVIDS: Trusted virtual IDS with SGX

Network functions such as intrusion detection systems (IDS) have been increasingly deployed as virtual network functions or outsourced to cloud service providers so as to achieve the scalability and agility, and reducing equipment costs and operational cost. However, virtual intrusion detection systems (VIDS) face more serious security threats due to running in a shared and virtualized environment instead of proprietary devices. Cloud service providers or malicious tenants may illegally access and tamper with the policies, packet information, and internal processing states of intrusion detection systems, thereby violating the privacy and security of tenant's networks. To address these challenges, we use Intel Software Guard Extensions (SGX) to build a Trusted Virtual Intrusion Detection System (TVIDS). For TVIDS, to prevent cloud service providers from accessing sensitive information about the users' network, we build a trusted execution environment for security policy, packets processing, and internal state so that cloud service providers and other malicious tenants can't access the protected code, policy, processing states, and packets information of the intrusion detection system. We implemented TVIDS on the basis of the Snort which is a famous open-source IDS and evaluated its results on real SGX hardware. The results show that our method can protect the security of the virtual IDS and brings acceptable performance overhead.
Model Predictive Control based Collision Avoidance Controller for Octocopter

Octocopter are MIMO systems and have on-board flight controller to maintain flight stability. These flight controllers are usually based on cascaded PID controllers which may not offer satisfactory performance while carrying out complex tasks like collision avoidance. Also the external disturbances and constraints severely effects the 6-DOF states and increases the controller design quite challenging. In proposed work, Linear model predictive control (LMPC) based collision avoidance controller addressing the challenges of controller design for MIMO plants. The design is based on collision cone approach and have potential of increasing the efficiency of the flight controller by reducing computation efforts caused due to cascaded PID loops. The strategy is suitable to deal with real time challenges such as external disturbances and constraints in collision avoidance controller. Simulation results reveal the efficacy of LMPC based collision avoidance controller for slow and fast varying disturbances. Formulation of LMPC also suggests that, it has a advantage over conventional PID control strategy for MIMO system.
Understanding Acdamic Impact Development by Predicting the G-index In Collaboration Networks

Academic impact is an important factor in the research assessment of a scholar. The prediction of the individual future academic impact is beneficial to detecting talented scholars and allocating the academic resources more reasonably. Recently, many evaluation indicators based on academic impact have been proposed, among which the G-index is more objective than the citations and H-index that used more frequently. At present, the main contradiction is formed between the great values of the individual future academic impact in practice and the imperfect evaluation and prediction methods. In order to solve this contradiction, this paper chooses the objective and comprehensive evaluation indicator G-index to measure individual academic impact and proposes the solution of how to predict the G-index at a given time interval for the first time in relevant research fields. Firstly, different characteristic features are extracted based on the structure of the academic social network. Then, a prediction model based on deep learning is proposed to learn the function mapping relationship between the future G-index time with the current feature set. Finally, the proposed model is validated with real academic social network dataset. The experiment results show that the individual G-index development is tightly related to the scholar's characteristic features in the current academic social network and the deep learning model performs well in predicting the future G index.
A Developed Eleven-Level Inverter Topology for Medium-Voltage Application

In this paper work has been done to propose an eleven-level hybrid topology for medium voltage application. The topology consists of two half bridge submodules in the inner part, one flying capacitor and one full bridge capacitor in the outer part. Capacitor voltage balancing and controlling of dc link capacitor has been done in a simple way possible. The actual idea is to reduce THD value while maintaining switching frequency and dv/dt at a desired level. The worked topology can reduce number of components, number of dc sources and it can improve efficiency compared to the existing ones for the generation of same output voltage level for medium voltage application. The effectiveness of the proposed eleven-level inverter for the medium voltage applications has been verified by simulation results.