Miracle-dz/UniFAR-FTUs huggingface dataset:
Sentiment Analysis and Classification on Twitter Spam Account Dataset

The amount of people using social media is very large and is increasing day by day. The impact of public figures in social media is quite big. Fake accounts are created in social media platforms and are used for various purposes like inflating the follower list of a particular account. These accounts also called spam accounts usually post spam messages which are used for marketing certain products or spreading particular agendas. Such accounts can be dangerous as they may alter a normal user’s perspective on certain topics. These accounts are used to modify and help in creating a fake sense of popularity which can influence political and social situations. In this project, we try to examine some of the existing methods and approaches for fake Twitter accounts detection. We will make use of a public dataset which contains tweets and account information of both Legitimate accounts as well as spam accounts. We make use of account information to create a classifier which can easily classify whether the given account is a fake account or a legitimate account. We also apply sentiment analysis algorithms on the tweet to find patterns among them. We try to analyse the sentiments behind the tweets of different accounts. Comparing our model with the existing model we will improve the features present in our model. In the process of building a better model, we try to reduce overfitting. The final result is an optimum classifier, which can be used to separate a fake account from a list of legitimate accounts.
Incidence and geographic distribution of maize rayado fino virus (MRFV) in Latin America

Samples of maize (Zea mays L.) exhibiting symptoms of the maize rayado fino virus (MRFV) were collected in eight Latin American countries during August to December of 1992, as part of a survey of virus distribution. Maize with MRFV symptoms were found in all countries and life zones sampled. Of the 103 collected plants tested for the presence of MRFV using hybridization with an MRFV cRNA probe, 75% were positive for the virus. Much variation in MRFV symptoms and symptom expression patterns was found.
Kallikrein 5 overexpression is associated with poor prognosis in uterine cervical cancer

Objective Kallikrein 5 (KLK5), which is frequently observed in normal cervico-vaginal fluid, is known to be related to prognosis in several solid tumors. We investigated the prognostic significance of KLK5 in uterine cervical cancer using tumor tissue microarray and immunohistochemistry staining. Methods We analyzed samples of 165 patients with uterine cervical cancer who received definitive radiation therapy between 2004 and 2012. We divided patients into two groups stratified by their KLK5 activity by immunohistochemistry staining: negative/weak (0–1+) (n=120 patients) and moderate/strong (2–3+) group (n=45 patients). Patient and tumor characteristics, patterns of failure, and survival outcomes were compared. Univariable and multivariable analyses were performed to identify prognostic factors. Results Patients with KLK5 2–3+ were younger (median: 52 vs. 60 years) and had frequent paraaortic lymph node involvement (40.0% vs. 18.3%) than those with KLK5 0–1+. With a median follow-up of 60.8 (interquartile range, 47.5–77.9) months, patients with KLK5 2–3+ had inferior 5-year locoregional recurrence-free survival and distant metastasis-free survival of 61.7% (vs. 77.5% in KLK5 0–1+ group) and 59.4% (vs. 72.8% in the KLK5 0–1+ group), respectively (all p<0.05). KLK5 2–3+ expression retained its significance after adjusting for other well-known prognostic factors of tumor size and stage in multivariable analysis. Conclusions KLK5 overexpression is associated with the aggressiveness of cervical cancer and may underlie the diminished response to conventional treatments. Therefore, KLK5 could be a reliable prognostic factor in cervical cancer.
Near-Carrier Phase Noise Suppression at Turnover Temperature in a Thin-Film Piezoelectric-on-Silicon Oscillator

In this paper, the near-carrier enhancement of phase-noise (PN) at turnover temperature (<inline-formula> <tex-math notation="LaTeX">${T} _{to}$ </tex-math></inline-formula>) in a quasi-thickness-Lamé (QTL) mode thin-film piezoelectric-on-silicon (TPoS) oscillator is reported for the first time. QTL-TPoS resonators fabricated on degenerately-doped n-type silicon offer a <inline-formula> <tex-math notation="LaTeX">${T} _{to}$ </tex-math></inline-formula> greater than 80°C and are suitable for implementation of highly-stable ovenized oscillators. In this work, a ~123MHz QTL-TPoS resonator is heated up to <inline-formula> <tex-math notation="LaTeX">${T} _{to}$ </tex-math></inline-formula> (~90°C) by injecting current through the silicon body of the resonator. It is experimentally observed that at turnover temperature, the phase-noise slope close to the carrier frequency (<inline-formula> <tex-math notation="LaTeX">$\Delta $ </tex-math></inline-formula> <italic>f</italic> < <italic>100Hz</italic>) decreases substantially in contrast to the expected trends. A ~10dB improvement in phase noise at 10Hz offset and a ~25dB improvement at 1Hz offset is recorded when the oscillator is operating at <inline-formula> <tex-math notation="LaTeX">${T} _{to}$ </tex-math></inline-formula> compared to room temperature. [2020-0206]
Retinal Vessel Segmentation using Deep Learning – A Study

The Retinal Vasculature conditions are important and reliable biomarkers for several cardiovascular and ophthalmologic diseases including Retinopathy of Prematurity, Diabetic Retinopathy, Glaucoma, Macular Degeneration, etc. The retinal-related diseases can be detected and diagnosed in the early stage before developing complexities and causing loss of sight by thoroughly understanding the vasculature condition. Analyzing these conditions is time consuming and tedious task. There have been many studies conducted to automatically extract retinal vessel information from fundus image and this is called retinal vessel segmentation. This paper provides a survey of the existing work that had been conducted to achieve this objective using Deep Learning methods. Deep Learning has become the state-of-the-art for solving computer vision problems. This paper briefly discusses U-Net based network architectures proposed for object segmentation and the usage of these networks in medical fields for vessel segmentation. It further compares the various architectures based on AUC and F1 score.
Mixture Modeling of Exponentiated Pareto Distribution in Bayesian Framework With Applications of Wind-Speed and Tensile Strength of Carbon Fiber

Mixture modelling has stunning applications to explain the composite problems in simple way. Bayesian demonstration of 3-Component mixture model of Exponentiated Pareto distribution in right-type-I censoring scheme is presented in this article. The posterior densities of the parameter(s) are attained supposing the non-informative (uniform, Jeffreys) priors. The symmetric and asymmetric Loss Functions (Squared Error, Precautionary, Quadratic and DeGroot Loss Function) are assumed to get the Bayes estimator(s) and posterior risk(s). The presentation of the Bayes estimator(s) over posterior risk(s) in the studied loss functions is examined over simulation practice. Two real-data sets, wind speed and tensile strength of carbon fiber, are also analyzed for mixture to complete the performance of Bayes estimator(s). To enhance the study, the limiting forms are also derived for Bayes estimator(s) and posterior risk(s). The results reveal that for the component parameter(s), the Bayes Estimator(s) have their risks accordingly: DeGroot Loss Function < Precautionary Loss Function < Squared Error Loss Function < Quadratic Loss Function, and whereas for the proportion parameter(s) these are classified as: Squared Error Loss Function < Precautionary Loss Function < DeGroot Loss Function < Quadratic Loss Function. Therefore, in this study, DeGroot Loss Function performs efficient and the most preferable non-informative prior is the Jefferys prior for estimation of 3-Component mixture of Pareto distribution.
Possibility to use divergent tasks for baseline alpha rhythm modulation in older adults

Findings reliably revealed that alpha power attenuates in aging whereas distractibility tends to increase. Given the causal link between alpha power and inhibition capacity in aged people, enhancement of baseline alpha power may have positive effect on cognition. Based on robust evidence of alpha power enhancement during divergent thinking, we suggested that divergent tasks might be candidate cognitive activity for modulation of resting state alpha rhythm. We investigated the changes in baseline upper alpha rhythm that followed visual and verbal divergent thinking training session in 30 older adults (64.2±6 years). 52-channel EEG was registered prior to, during and after session. Upper alpha power was calculated via Fourier transform; current source density (CSD) and statistical nonparametric mapping were performed using LORETA. Analysis of both power and CSD revealed alpha increases from pre-training to post-training interval, with maximum difference in Superior Parietal lobule (region of interest, ROI). CSD estimates during verbal task did not differ from pre-training alpha activity. CSD during visual task performance was higher than at pre-training interval and than during verbal task performance. The spatial pattern of alpha CSD increase during visual task performance matched spatial pattern of CSD increase in post-training activity. Moreover, measures of ROI in visual task performance positively correlated with measures of ROI in post-training interval. The findings are in favor for notion, that divergent thinking session may be viewed as a kind of cognitive activity resulted in alpha power increase in older adults. Visual divergent task performance made crucial impact in this post-training alpha rhythm modulation.
Collaborative Filtering Based on a New Matrix Factorization Algorithm

Online platforms such as Facebook, Netflix, and Amazon, provide users with information on products or services that matches their interests or backgrounds. As one of the most popular services over online platforms, recommendation system has attracted increasing research efforts recently. Among all the recommendation tasks, an important one is item recommendation over high spectral data. Existing recommendation techniques are not effective for handling users with diverse interests. In this paper, the sparsity problem of traditional recommendation algorithms, a new recommendation algorithm based on spectral clustering is proposed. The spectral clustering process can improve the efficiency of spectral clustering algorithm. Spectral clustering can be performed off-line, which will accelerate the speed of online recommendation. The experimental results on Netflix show that the new algorithm improves recommendation quality in MAE and coverage.
Detection of Icing and Calculation of Sag of Transmission Line Through Computer Vision

Overhead transmission lines in freezing season can be covered with ice in most of the countries. The icing of the transmission lines is usual and the electrical line comes vertically downwards which may cause huge economic loss, short circuit and power failure. Transmission lines are normally allowed to give a conductor suspension so that there is no breakage or drop off in all the weather conditions. Sag is an important parameter and has to be maintained within its limit. So there is a need for continuous monitoring of the transmission lines. There are many sensor technologies where sensors are installed directly on the wire are used to calculate the sag, tension, icing. But the employment of different sensors makes it more expensive and during the installation of sensors switching off the transmission line is needed. Hence, the concept of a vision system is used which requires only one camera placed in front of the transmission line for monitoring. This paper focuses on the detection of icing in a transmission line. Also, sag and tension of the overhead transmission line are calculated using image processing. Detection of icing and calculation of transmission line parameters is accomplished through the camera. The captured image from the camera can be processed via raspberry PI. The information about the transmission lines is sent to the monitoring station for taking safety precaution measures.
DPA: Demand-Based Partition and Data Allocation for Hybrid On-Chip Memory

The performance and energy consumption of the memory system has become an essential bottleneck in modern processors. Scratchpad memory (SPM) is widely used in the embedded system for meeting the constrict of energy consumption. The hybrid memory architecture has been proposed to improve the energy efficiency of the overall system. Previous works focus on optimizing data allocations in the fixed-size cache and SPM. However, applications have different preferences for cache and SPM. Fixed memory structures may cause a waste of storage resources. In this paper, a reconfigurable mechanism is proposed to dynamically adjust the partition of the local memory and allocate data with high-locality into the SPM. Our experimental results show that our method improves the energy-delay product by 12.0% on average compared with the previous works.
Beam Steering Using Active Artificial Magnetic Conductors: A 10-Degree Step Controlled Steering

An Active Artificial Magnetic Conductor (AAMC) is presented to steer the radiation pattern of a printed dipole working at 2 GHz. The elements that generates the phase shift are a set of Varactor Diodes, which are characterized using its spice model in order to obtain a phase shift - capacitance mapping. Overall beam steering of +/- 40° with a step size of 10° is achieved. A circuit model that describes any multilayer substrate AAMC unit cell, which uses fist form of Foster’s theorem along with transmission line theory, is proposed. Our work is suitable to be used as low profile antenna; for example, street furniture antennas, which are located on the facades of houses or buildings, so that they can be visually mixed up with signs or advertisements. Simulations have been validated using a prototype consisting of an array of <inline-formula> <tex-math notation="LaTeX">$22\times 14$ </tex-math></inline-formula> AAMC elements; the overall structure measures <inline-formula> <tex-math notation="LaTeX">$1.9 \lambda _{0}\times 1.21 \lambda _{0} $ </tex-math></inline-formula>. This reflector will generate a phase gradient in its columns, which will modify the reflection angle of an incident electromagnetic wave in the H-Plane. Beam switching control has been achieved using suitably amplified LPF PWM signals generated by two Arduino modules. A printed dipole with a Fractional Bandwidth of 17% is designed and manufactured to illuminate the structure at a distance <inline-formula> <tex-math notation="LaTeX">$\lambda _{0}/4$ </tex-math></inline-formula> above the surface. Far-field radiation patterns and reflection coefficients have been measured in an anechoic chamber using a spherical system. These compare favorably with simulations performed using the Time Domain solver in CST Microwave Studio.
Session-level Adversary Intent-Driven Cyberattack Simulator

Recognizing the need for proactive analysis of cyber adversary behavior, this paper presents a new event-driven simulation model and implementation to reveal the efforts needed by attackers who have various entry points into a network. Unlike previous models which focus on the impact of attackers' actions on the defender's infrastructure, this work focuses on the attackers' strategies and actions. By operating on a request-response session level, our model provides an abstraction of how the network infrastructure reacts to access credentials the adversary might have obtained through a variety of strategies. We present the current capabilities of the simulator by showing three variants of Bronze Butler APT on a network with different user access levels.
Decision Tree approach to Identify Vision Disorder for lazy Eye

Lazy Eye is a disorder of vision and is related to a neurological disorder, in which the brain is not able to receive correct input from one eye. This disease called Amblyopia. Two to five percent people are affected by this disorder. Cibis, Wang, and Van Eenwyk have developed an automatic system for sight screening, aimed for early finding the problem while the person is a child. These systems not able to achieve an accuracy of more than 78%. From AVVDA system two more features are used but achieved a low accuracy. We are using traditional machine learning technique, decision tree approach, to get more accuracy. We have used random forest WEKA architecture for the patient vision data set and for selecting the appropriate features we have used InfoGain Class. With the decision tree approach, our work showed good results, where the accuracy achieved is more than 90%.
Short Term Forecasting of Sea Level by Using LSTM with Limited Historical Data

Sea level forecasting is useful for many coastal applications, such as navigation, coastal operation, etc. One of many challenges in sea level forecasting is the availability of its historical data. Limited amount of historical data may lead to inaccurate prediction. Especially when using tidal harmonic analysis, low frequency components cannot be captured in short term historical data. In this paper, we present an application of a deep learning approach for sea level forecasting, especially with limited amount of historical sea level data. We use the so-called Long Short Term Memory (LSTM) method to forecast the sea level. We use two months of historical data, to forecast 7 days up to two months ahead. As a study case, we use the sea level data that is recorded from the tidal buoy station in Tanjung Benoa, Bali, Indonesia. To investigate its accuracy, we compare results of forecasting by using the Long Short Term Memory (LSTM) with the traditional method tidal harmonic analysis that uses Least Square Estimation (LSE) method. Moreover, we also compare the Long Short Term Memory (LSTM) method with feedback and no-feedback. The results of forecasting by using Long Short Term Memory (LSTM) with feedback results show in accurate prediction, with R value of 0.99.
The structure of the cytokine gene network in uterine fibroids

The structure of 166416 gene chains of various dimensions consisting of polylocus combinations of 12 polymorphic positions which forming cytokine gene network which associated with the development of uterine fibroids was studied. The structural and functional organization of the cytokine gene network, which associated with uterine fibroids resistance, was also studied for comparison. Computer modeling of network interactions of cytokine genotypes of various nature involved in the regulation of inflammation, angiogenesis, destruction and remodeling of connective tissue involved in the pathogenesis of polygenic diseases, including uterine fibroids, was performed using the Cytoscape bioinformatics platform. Analysis of the topology of this gene network allowed us to identify the main genes and the main intergenic interactions that make the greatest contribution to the development of uterine fibroids. The main genes for the development of uterine fibroids can be 3 polymorphisms: IL1B-31TC, VEGF+936CT and IL10-592CA. These genes form the main nodes in the gene network because they have the most interactions with other genes. The main intergenic interactions are the following paired combinations of polymorphisms: [IL1B-31TC : IL10-592CA] (13,5%), [TNF-238GG : IL1B-31TC] (12,0%), [IL1B-31TC : IL10-1082AG] (10,5%), [TNF-863CC : IL1B-31TC] (9,7%), [TNF-238GG : VEGF+936CT] (9,0%), since they account for 55% of all major interactions in the gene network associated with uterine fibroids development. These composite gene patterns can determine the predisposition to the uterine fibroids development, affecting the intensity of immune inflammation processes and the activity of neoangiogenesis in the myometrium. On the other hand, a characteristic feature of the structural and functional organization of the cytokine gene network, which associated with uterine fibroids resistance, is its ability to self-regulate the levels of promoter activity of angiogenesis and inflammation regulator genes due to regulatory circuits with a common hub in the form of the homozygous VEGF+936CC genotype. The most frequent intergenic interactions are associated with this vertex: [TNF-238 GG : VEGF+936 CC] (13.2%), [TNF-863 CC : VEGF+936 CC] (13.2%), [IL4-590 CC :VEGF+936 CC] (7.9%), and [VEGF+936 CC : MMP9-1562 CC] (7.9%). This gene network ensures the balanced flow of angiogenesis processes, the functioning of the extracellular matrix and the processes of inflammation in the myometrium, which prevents the development of uterine fibroids.
Undersampling Reference Receiver For LNA Distortions Compensation Direct RF Digitization Receiver

The paper analyzes the effect of the nonlinear distortion of Low Noise Amplifier (LNA) in multichannel direct RF digitization receivers (DRF-RX). A novel distortion compensation approach has been proposed, where the compensation is performed entirely in the digital domain at baseband after the digital down-converter (DDC). Distortion information is extracted from the signals of the main receiver and a linear reference receiver. The reproduced information is used for distortion compensation using a nonlinear model and an adaptive Least Mean Square (LMS) algorithm. As the major advantage, processing at baseband with a low sampling rate significantly reduces the cost, the design complexity and the energy consumption of the receiver. The proposed undersampling reference receiver structure exhibits additional benefit in terms of hardware usage and energy consumption by using a low-cost and large dynamic range ADC. To verify the scheme's efficiency, an Ultra High Frequency (UHF) DRX with four QPSK channels is simulated by Matlab Simulink. The simulation results show that the method improves the Spurious-free dynamic range (SFDR) with an increase of about 20 dB, compared to the results of RX without distortion compensation.
Heuristic Contention-Free Scheduling Algorithm for Multi-core Processor using LET Model

Embedded systems, e.g., self-driving systems and advanced driver-assistance systems (ADAS), require computing platforms with high computing power and low power consumption. Multi-/many-core platforms satisfy these requirements effectively. However, for hard real-time applications, multiple demands on shared resources can impede real-time performance, and memory is one resource that can impair the desired performance significantly. Therefore, it is important that memory access timing be deterministic to facilitate predictability. To realize this, the Logical Execution Time (LET) paradigm is currently attracting attention. This paper proposes a theoretical scheduling method for a model applying the LET paradigm to directed acyclic graph (DAG) nodes for a multi-/many-core platform. The proposed method considers communication timing between nodes and generates a schedule that does not cause communication contentions. In addition, the proposed method attempts to distribute tasks and reduce LET intervals to address increased execution times due to the implementation of the LET paradigm. In the evaluation, we observed that the proposed method improved the schedule length by up to 40%.