Miracle-dz/UniFAR-FTUs huggingface dataset:
DDOS attacks detection based on attention-deep learning and local outlier factor

One of the most significant security concerns confronting network technology is the detection of distributed denial of service (DDOS). This paper introduces a semi-supervised data-driven approach to the detection of DDOS attacks. The proposed method employs normal events data without labeling to train the detection model. Specifically, this approach introduces an improved autoencoder (AE) model by incorporating a Gated Recurrent Unit (GRU) based on the attention mechanism (AM) at the encoder and decoder sides of the AE model. GRU enhances the AE's ability to learn temporal dependencies, and the AM enables the selection of relevant features. For DDOS attacks detection, the local outlier factor (LOF) anomaly detection algorithm is applied to extracted features from the improved AE model. The performance of the proposed approach has been verified using DDOS publically available datasets.
A Detailed Analysis on Disaster Tweet Analysis Using Deep Learning Techniques: DTWEET

The widespread use of PDAs makes it easier for people to repeatedly declare a crisis. Twitter has developed into a significant correspondence platform in times of emergency. Despite the fact that states and crisis management organisations collaborate through their unique public frameworks for responding to disasters, the effectiveness of the disaster response and the recovery cycle is ultimately determined by the views of those affected during and after the event. When a crisis strikes, people publish frequent updates on online entertainment platforms. The vast amounts of unstructured data generated by online entertainment platforms like Twitter can be filtered using text mining and AI calculations to identify such data through watchwords and idioms that refer to disasters. This study focuses on using Normal Language Processing (NLP) and order models to distinguish between real and fake disaster tweets. The dataset was obtained from the Kaggle website and includes tweets that are related to actual disasters as well as other tweets that make references to fictitious disasters.
Prediction of illuminance on the work plane using low dynamic unprocessed image data

Light level measurement is crucial in applications involving lighting control, circadian lighting, and light booths. The paper proposes a unique camera-based approach for measuring illuminance on a work plane. A machine learning-based model is suggested that takes the RAW pixel data of standard neutral colour patches placed on the work plane as inputs and predicts the illuminance level on the work plane. The training data is populated using a DSLR camera, calibrated illuminance meter, standard neutral patches, and controlled lighting in the darkroom. The RAW image files are processed using MATLAB, and the App Designer tool is used to develop the user interface. The model's performance is accessed under various illuminance levels and colour temperatures. The percentage error of prediction during testing by the model was less than 5. Since the proposed technique incorporates the camera as the light sensor, simultaneous illuminance measurement at multiple target points present in the camera vision can be realised.
Performance analysis of Incremental boosting based Transfer Learning in Deep CNN

Imbalanced datasets are one of the important research constraints available in image classification. Due to which the classifier performance is greatly reduced leading to overfitting and under fitting problems. However, this is greatly applicable for better well-balanced datasets. Adaboost classifier model is one such technique proven for its accuracy both in terms of margin theory and in terms of statistical point of view. Many novel approaches use boosting and bagging methods to improve the performance of classifier models. In this research, we are focusing on the effectiveness of boosting procedures in deep Convolution neural network (deep CNN) for classification and modification of ensemble approaches are done with transfer learning techniques. The Computational Complexity of the classifier affects the performance accuracy of the same. Based on the above idea, the input data for training the model is subsampled and reweighted for better efficiency and less complexity. Performance metrics used to analyze the performance of simple AdaBoost classifier, boosted GMM, boosted SVM, incremental boosting based transfer learning approaches using GMM and SVM with and without subsampling procedures are the accuracy, the training time, the predicting time of testing, the volume of the model, and the loss function. Along with the above-said metrics, three more essential parameters, namely the Jaccard index, Dice coefficient, and Mathews correlation coefficients, are used. Based on the experiments carried over the benign and malignant melanoma images from the ISIC database, the boosting based transfer learning approach in deep CNN gives an accuracy of 99.19% and confusion matrix created over the classifier has given the sensitivity of 98.46%.
Divergences in the determinants of investments in renewable energy sources: hydroelectric vs. other renewable sources

ABSTRACT In this paper, we analyze the drivers promoting the investments in renewable energy sources (RES) and the divergences on the basis of generation sources (hydroelectric and other renewable sources). To address these issues, a dynamic panel analysis of the renewable investments in a sample of 32 countries (Organisation for economic co-operation and development and Brasil, Russia, India, China and South Africa) with distinct economic and social structures, in the years between 2000 and 2008, is proposed. Results confirm that key factors promoting investments in RES vary according to generation sources considered. Investments in hydroelectric sources contribute to improve the environmental conditions, while the other sources are not significant. The policies are useful to support the investments in renewable energy. Results also show that share of nuclear and thermal electricity generation depress the investments in renewables.
TAROT: Spatio-Temporal Function Placement for Serverless Smart City Applications

Emerging smart city applications (i.e., traffic management, smart tourism) have to (i) process data coming from different IoT devices and (ii) deliver results of data processing to various user devices (e.g., smart vehicles or smartphone) while considering applications’ latency constraints. Serverless edge computing has proven to be very effective for latency-aware processing of IoT data, since it allows application developers to define data processing logic in terms of functions which react to data events. However, data processing functions should be dynamically placed and migrated while considering IoT data sources’ location and user devices’ mobility to minimize end-to-end latency. Unfortunately, current serverless computing solutions do not support mobility-aware placement of functions. In this paper, we propose dynamic function placement based on user devices’ mobility to address latency requirements of smart city applications. We consider serverless smart city applications, since this computational model allows to model application as a function execution in response to specific events, which makes it suitable for event-driven applications typical of smart city and IoT. First, we identify the parameters affecting end-to-end latency of serverless smart cities’ applications. Then, based on our findings, we design TAROT, a latency-aware function placement method based on data-driven mobility predictions. Results show improvements up to 46% for average end-to-end latency in comparison to state-of-the-art solutions.
Moisture Patterns in Deepening Maritime Extratropical Cyclones. Part I: Correlation between Precipitation and Intensification

Abstract Coincident satellite passive microwave (SSM/I) observations and 48-h numerical simulations of 23 intensifying extratropical cyclones located over the North Atlantic or North Pacific Oceans during a single cold season are examined in order to identify systematic differences in the moist processes of storms exhibiting rapid and ordinary intensification rates. Analysis of the observations and simulations focus on the 24-h period of most rapid intensification for each case as determined from European Centre for Medium-Range Weather Forecasts 12-h mean sea level pressure analyses. SSM/I observations of area-averaged precipitation and an index that responds to cold-cloud (convective) precipitation to the northeast of surface cyclone centers were previously shown to correlate well (∼0.80) with the latitude-normalized deepening rate (NDR) of the study sample. This large correlation is replicated by the numerical model, although the area-averaged precipitation region yielding the maximum correlation coeff.
Provenance-enhanced Root Cause Analysis for Jupyter Notebooks

With Jupyter notebooks becoming more commonly used within scientific research, more Jupyter notebook-based use cases have evolved to be distributed. This trend makes it more challenging to analyze anomalies and debug notebooks. Provenance data is an ideal option that can create more context around anomalies and make it easier to find the root cause of the anomaly. However, provenance rarely gets investigated in the context of distributed Jupyter notebooks. In this paper, we propose a framework that integrates two data types, provenance and detected performance anomalies based on performance data. We use the combined information to visually show the enduser the provenance at the time of the anomaly and the root cause of the anomaly. We build and evaluate the framework with a notebook extended with anomaly-generating functions. The generated anomalies were automatically detected, and the combined information of provenance and anomaly creates a valuable subset of the provenance data around the time an anomaly occurred. Our experiments create a clear and confined context for the anomaly and enable the framework to find the root cause of performance anomalies in Jupyter notebooks.
Detection of some anaemia types in human blood smears using neural networks

The identification process based on measuring the level of haemoglobin and the classification of red blood cells using microscopic examination of blood smears is the principal way to diagnose anaemia. This paper presents a proposed algorithm for detecting some anaemia types like sickle and elliptocytosis and trying to count them with healthy ones in human red blood smears based on the circular Hough transform and some morphological tools. Some cells with unknown shapes (not platelets or white cells) also have been detected. The extracted data from the detection process has been analyzed by neural network. The experimental results have demonstrated high accuracy, and the proposed algorithm has achieved the highest detection of around 98.9% out of all the cells in 27 microscopic images. Effectiveness rates up to 100%, 98%, and 99.3% have been achieved by using neural networks for sickle, elliptocytosis and cells with unknown shapes, respectively.
Accurate Positions for MCG Galaxies

We have measured accurate celestial coordinates for 4741 extragalactic objects, primarily drawn from a list of MCG galaxies with no recently published accurate positions. The standard deviations in the new positions depend slightly on the measurement method but are on the order of 1. ″0 to 1. ″2. Standard deviations in the original MCG positions are confirmed to be at the 1. ′5–2. ′0 level. These new positions were integrated into NED in 1997 December.
Enhancement of omnidirectional total-reflection wavelength range by using one-dimensional ternary photonic bandgap material

Here we demonstrate by theoretical analysis a novel way to enhance the omnidirectional total-reflection wavelength range in one-dimensional photonic bandgap material by using a ternary periodic structure (i.e., three material layers constituting a period of the lattice). The omnidirectional total reflection range using a binary (BaF2/PbS) periodic structure was enhanced by 108 nm when the structure was modified by sandwiching a thin layer of ZrO2 between every two layers, constituting a period of the lattice. A shift of the omnidirectional range toward higher wavelengths was also observed. When the sandwiched layer was CeF3, the enhancement in the range was 120 nm. As the sandwiched layer is very thin, there will not be any significant increase in the size of the reflector, contrary to the case of heterostructured photonic crystals (PCs) where two or more PCs are clubbed together to achieve enhancement.
Privacy Preserving Recommendations for Social Networks

Social recommendation is an advanced service of social networking platforms that is provided to their users. Social recommendation uses profiles and connections to generate personalized suggestions of contents, advertisements, people, pages, or interest groups. Since individual sensitive information is possibly involved in elaborating a recommendation, it may be inferred by an adversary in some situations. In this work, we design a differentially private setting to prevent social recommendations from disclosing sensitive information. Our recommendation system targets users of online social networks by leveraging their attributes and relationships. Unlike other approaches, we rely on both profile similarity and homophily properties. Therefore, our system estimates the frequency of friends who share some attribute values and applies non-negative matrix factorization to derive recommendations such as hobbies, movies, etc. We demonstrate the effectiveness of the proposed approach through experiments on real-world datasets and evaluation according to utility measures.
Plücker formulae for singular space curves

Abstract In [4] I extended the Plücker formulae to curves in Pn for any n, under fairly strong genericity hypotheses. In this paper we seek to weaken these hypotheses using the theory of excess intersections. The method applies to just one of the formulae (so the results are complete only for n=3), and for this we obtain a number which is a sum of local contributions, given by an explicit rule. We evaluate these contributions in the cases when they are determined by the terms of least order in the respective Taylor expansions, thus obtaining explicit formulae in numerous cases.
Development of a silicon microprobe for NO detection

An array of electrochemical sensors -- amperometric detection -- was developed using silicon planar technology. Our main purpose is to detect the activity of free radicals such as nitric oxide (NO) at cellular dimensions. In this paper we focused on the process sequence used to produce silicon microprobes, based on plasma etching. Typical dimensions of the structures are: a length of 10 mm, a width of 1 mm, a tip width of 60 micrometers, and a thickness of 30 micrometers. Four different probe designs were adopted in order to test mechanical integrity. The defined process using plasma etching revealed to be feasible, although the lateral walls of the obtained probes resulted very rough. Preliminary mechanical tests were performed using probes with a thickness of 300 micrometers. Probes with wider shapes seem to have a better combination of higher fracture force and possibility to place more electrodes close to the tip.
A Compendium Probabilistic Prospective for Predicting Coffee Crop Yield Based on Agronomical Factors

Coffee has a significant part in the economies of many African, American, and Asian nations since it is a perennial crop and a product used in daily life. For agricultural system modelers, it is still difficult to anticipate coffee output with any degree of accuracy given the environmental, meteorological, and soil fertility factors that influence it. Out of the 103 different varieties of class coffee bean variation that are commercially traded worldwide, the two most significant types of coffee assortment filled in India are Arabica and Robust. In order to investigate and construct a predictive model for the development of coffee planters to make accurate judgments in time during bad conditions in advance, we are taking the most important plantation crop in India, namely, coffee. In order to quantify the effect of agronomic parameters to obtain a decent coffee yield, we thus present a framework for coffee yield prediction utilizing machine learning probabilistic techniques. Here, we take into account the historical dataset from the Central Coffee Research Institute (CCRI), Karnataka, for the year (2008-2019). We are taking into account agronomic elements like Age, Soil Nutrients: Organic Carbon (OC), Phosphorus (P), Potassium (K), Alkaline (pH), and Respective Yield Obtained in Chikkamagaluru Region, Karnataka State, India, for the forecast of coffee yield. Multiple Linear Regression, Lasso Regression, and Elastic Net Regression are three different predictive regression algorithms that are used for prediction; the results of each are compared, examined, and tabulated. The best accurate coffee yield estimate during the autonomous testing phase was produced using an elastic net (ENET) regression model ($\mathrm{R}2=0.26$ kg ha 1, $\text{RMSE}=136.95$ kg ha 1, and $\text{MAE}=111.41$ kg ha 1). This contrasted with the less accurate Lasso regression model ($\mathrm{R}^{2}=0.25$ kg ha - 1, $\text{RMSE}=137.64$ kg ha−1 and $\text{MAE}=111.96$ kg ha - 1) and MLR ($\mathrm{R}^{2}=0.25$ kg ha−1, $\text{RMSE}=137.71$ kg ha−1 and $\text{MAE}= 112.02$ kg ha - 1). In this work, the ENET model replaces the Lasso and MLR models as an enhanced class of artificial intelligence models for smallholder farm coffee yield prediction. This is a distinctive addition to the area of agronomy, particularly in terms of the right selection of the best soil characteristics, coffee variety, and age that may be employed in the forecasting of the ideal coffee production.
Influencers Analysis from Social Media Data

Influencer promotion through interpersonal organizations is turning into a significant option in contrast to conventional methods of publicizing. Different arrangements have been suggested that frequently exploit chart-based ways to deal with finding influencers in interpersonal organizations. Influencers are people who have good recognition and reputation in a particular expertise area. In social media, Influencers are someone who influences others with different posts and a huge number of followers. To enhance product sales and personality, market researchers find influencers in their area. Most Influencers are found on Social media platforms, namely, Instagram, Facebook, YouTube, and Twitter by analyzing their likes, comments, and followers. In this paper, Instagram and YouTube data sets are analyzed. The Influencers are identified on engagement rate and categorized into mega and mini tiers. These influencers are used to brand promotion, enhance the brand reach and thereby enhance the business.
A Method of Constructing Malware Classification Dataset Using Clustering

Machine learning, which automatically learns models from data, is receiving a lot of attention as a solution to cope with the increasing number of malicious codes every year. However, since most malicious codes are variants developed by recycling existing malicious codes, there is a problem that the model is easily overfitted to the training set compared to other domains. Previous studies have tried to remove the variants using labels provided by vaccines, but it can lead to indiscriminate removal of malicious codes since the vaccine label is inaccurate. Therefore, we propose a method of constructing a dataset by performing clustering and randomly selecting one from a cluster. To demonstrate that the proposed method of constructing training set can prevent overfitting and improve the generalization performance, we experimented with three training sets: a set that variants are not removed, a set that duplicated families are removed using labels, and a set that duplicated families are removed by the proposed method. To measure generalization performance, we experimented with six test sets constructed by the similarity to the training sets. It was confirmed that models learned from the training set constructed by the proposed method performed better on four test sets than the other models.
Fire Detection and Analysis in Video Surveillance Application using CNN

Fire detection based on computer vision is one amongst the key technologies of the trendy surveillance systems. Convolutional Neural Networks (CNN) has yielded cutting edge execution in picture characterization, and diverse computer vision undertakings. The fire detection systems increases the accuracy, thereby reduces the fire disasters and minimizes the overall threats to the society. However, how to reliably and accurately detect the fire is still a tricky problem. This article proposes a computationally economical CNN design for fireplace detection, localization and spread rate estimation of the fireplace. Experimental results shows that the proposed method has improved accuracy and effectiveness of the localization and spread rate estimation.
MANDA: On Adversarial Example Detection for Network Intrusion Detection System

With the rapid advancement in machine learning (ML), ML-based Intrusion Detection Systems (IDSs) are widely deployed to protect networks from various attacks. One of the biggest challenges is that ML-based IDSs suffer from adversarial example (AE) attacks. By applying small perturbations (e.g., slightly increasing packet inter-arrival time) to the intrusion traffic, an AE attack can flip the prediction of a well-trained IDS. We address this challenge by proposing MANDA, a MANifold and Decision boundary-based AE detection system. Through analyzing AE attacks, we notice that 1) an AE tends to be close to its original manifold (i.e., the cluster of samples in its original class) regardless of which class it is misclassified into; and 2) AEs tend to be close to the decision boundary to minimize the perturbation scale. Based on the two observations, we design MANDA for accurate AE detection by exploiting inconsistency between manifold evaluation and IDS model inference and evaluating model uncertainty on small perturbations. We evaluate MANDA on both binary IDS and multi-class IDS on two datasets (NSL-KDD and CICIDS) under three state-of-the-art AE attacks. Our experimental results show that MANDA achieves high true-positive rate (98.41%) with a 5% false-positive rate.
Performance Analysis on Skew Optimized Clock Tree Synthesis

In this survey paper, various methodologies adopted in skew minimization of Clock tree are addressed and the results of these methodologies are compared. Due to fast technology growth and complicated design circumstances, Clock skew reduction has become a tedious task for designers. Effective clock skew optimization improves the design performance. Minimizing clock skew among various corners becomes more difficult in current SoCs.
Forward-Central Jet Correlations

The azimuthal correlation between forward and central jets has been measured in proton–proton collisions at the LHC, at the centre-of-mass energy of 7 TeV. The forward jet is reconstructed in the hadronic forward calorimeter, within the pseudorapidity 3.2 35 GeV. The measurement of the azimuthal angle between the jets is performed for different separations in pseudorapidity, with the largest separation being ∆η = 7.5 units. The analysis is carried out for inclusive dijet events and for two subsamples, one where an additional jet is required between the forward and the central jet, and one where the additional jet is vetoed. Comparisons between data and several different Monte Carlo models and tunes show a large sensitivity to the modeling of QCD radiation.