Miracle-dz/UniFAR-FTUs huggingface dataset:
Cyberpulse: A Machine Learning Based Link Flooding Attack Mitigation System for Software Defined Networks

Software-defined networking (SDN) offers a novel paradigm for effective network management by decoupling the control plane from the data plane thereby allowing a high level of manageability and programmability. However, the notion of a centralized controller becomes a bottleneck by opening up a host of vulnerabilities to various types of attacks. One of the most harmful, stealthy, and easy to launch attacks against networked systems is the link flooding attack (LFA). In this paper, we demonstrate the vulnerability of the SDN control layer to LFA and how the attack strategy differs when targeting traditional networks which primarily involves attacking the links directly. In LFA, the attacker employs bots to surreptitiously send low rate legitimate traffic on the control channel which ultimately results in disconnecting control plane from the data plane. Mitigating LFA on the control channel remains a challenge in the network security paradigm with the use of network traffic filtering only. To address this challenge, we propose CyberPulse, a novel effective countermeasure, underpinning a machine learning-based classifier to alleviate LFA in SDN. CyberPulse performs network surveillance by classifying network traffic using deep learning techniques and is implemented as an extension module in the Floodlight controller. CyberPulse was evaluated for its accuracy, false positive rate, and effectiveness as compared to competing approaches on realistic networks generated using Mininet. The results show that CyberPulse can classify malicious flows with high accuracy and mitigate them effectively.
Predicting the Early Stages of the Alzheimer's Disease via Combined Brain Multi-projections and Small Datasets

Alzheimer is a neurodegenerative disease that usually affects the elderly. It compromises a patient’s memory, his/her cognition, and perception of the environment. Alzheimer’s Disease detection in its initial stage, known as Mild Cognitive Impairment, attracts special efforts from experts due to the possibility of using drugs to delay the progression of the disease. This paper aims to provide a method for the detection of this impairment condition via the classification of brain images using Transfer Learning Deep Features and Support Vector Machine. The small number of images used in this work justifies the application of Transfer Learning, which employs weights from VGG19 initial layers used for ImageNet classification as deep features extractor, and then applies Support Vector Machines. Majority Voting, False-Positive Priori, and Super Learner were applied to combine previous classifiers predictions. The final step was a detection to assign a label to the previous voting outcomes, determining the presence or absence of an Alzheimers pre-condition. The OASIS-1 database was used with a total of 196 images (axial, coronal, and sagittal). Our method showed a promising performance in terms of accuracy, recall and specificity.
Spoken Emotion Recognition by Combining Deep Belief Networks and Multi-layer Perceptron

The recently-emerged deep learning theory has attracted extensive attentions in machine learning, signal processing, artificial intelligence and pattern recognition. Deep belief networks (DBN) is the well-known method of deep learning due to its strong ability of unsupervised feature learning. In this paper, a new method of spoken emotion recognition by combining DBN and multi-layer Perceptron (MLP), is proposed. DBN is used to perform unsupervised feature learning on the extracted emotional acoustic features. Then the learning results of the topside hidden layer of DBN is employed to initialize the MLP model for emotion classification. Experimental results on the popular Berlin database of German emotional speech, demonstrate the promising performance of the proposed method on spoken emotion recognition tasks, outperforming the other used methods.
Analysis of an H∞ Robust Control for a Three-Phase Voltage Source Inverter

Recently, power quality improvement has gained a lot of attention due to the rapidly increasing use of power electronics equipment. Several control strategies for DC/AC Voltage Source Inverters (VSI) have been developed to obtain good quality output with low harmonic distortion. This paper proposes a robust control scheme to improve the power quality of a three-phase DC/AC VSI. The control scheme includes an outer voltage loop and an inner current loop, with both controllers designed by the standard H∞ robust control technique. The system with the proposed controller has a low total harmonic distortion (THD) and improved power quality of output voltage in the presence of linear and non-linear loads. The simulation is carried out in MATLAB/Simulink environment, and the results of the proposed control scheme are compared with the performance of dead-beat (DB) predictive control and conventional proportional integral (PI) control. It is observed from the results that the proposed control scheme outperforms other control schemes in terms of the THD level, having a better steady-state and transient performance.
Gain estimation of linear dynamical systems using Thompson Sampling

We present the gain estimation problem for linear dynamical systems as a multi-armed bandit. This is particularly a very important engineering problem in control design, where performance guarantees are casted in terms of the largest gain of the frequency response of the system. The dynamical system is unknown and only noisy input-output data is available. In a more general setup, the noise perturbing the data is non-white and the variance at each frequency band is unknown, resulting in a two-dimensional Gaussian bandit model with unknown mean and scaled-identity covariance matrix. This model corresponds to a two-parameter exponential family. Within a bandit framework, the set of means is given by the frequency response of the system and, unlike traditional bandit problems, the goal here is to maximize the probability of choosing the arm drawing samples with the highest norm of its mean. A problem-dependent lower bound for the expected cumulative regret is derived and a matching upper bound is obtained for a Thompson-Sampling algorithm under a uniform prior over the variances and the two-dimensional means.
Design of tunable radiofrequency blocks in FD-SOI technology for IoT applications

Communicating objects are inviting themselves into daily life leading to digitization of the physical world. This explosion of multimedia wireless applications for consumer electronics makes the power consumption a key metric in the design of multi-mode wireless portable devices. Conventional transceivers have fixed performances and are designed to meet high performances in all wireless link conditions. However, most of the time, the channel of communication is not at worst case and these transceivers are therefore over specified. Being aware of the channel link conditions would allow such devices to adapt themselves and to reduce significantly their power consumption. Therefore, the challenge is to propose a QoS (Quality of Service) in terms of communication range, response time as instance, equivalent to industrial modules with a reduced overall power consumption.To address this purpose, this thesis proposes a design strategy for the implementation of adaptive radio-frequency receiver (Rx) modules. Hence the Rx front end achieves the correct QoS for various scenarii of communications with a minimum of power consumption.As a proof of concept, the adaptive approach is demonstrated with the design of a tunable LNA (Low Noise Amplifier). As the first element of the receiver chain, the LNA limits the receiver in terms of sensitivity and is therefore a good candidate to perform reconfiguration. The body biasing of the FD-SOI (Fully Depleted Silicon-On-Insulator) technology is first exploited to reduce the power consumption of a circuit and then as an opportunity to perform circuit tunability.
Variational Bayesian Inference in Large Vector Autoregressions with Hierarchical Shrinkage

Many recent papers in macroeconomics have used large Vector Autoregressions (VARs) involving a hundred or more dependent variables. With so many parameters to estimate, Bayesian prior shrinkage is vital in achieving reasonable results. Computational concerns currently limit the range of priors used and render difficult the addition of empirically important features such as stochastic volatility to the large VAR. In this paper, we develop variational Bayes methods for large VARs which overcome the computational hurdle and allow for Bayesian inference in large VARs with a range of hierarchical shrinkage priors and with time-varying volatilities. We demonstrate the computational feasibility and good forecast performance of our methods in an empirical application involving a large quarterly US macroeconomic data set.
Extraction of Disturbance Signals Based on the Inertial Sensor with a Blind Source Separation Method

The inertial sensor is a key component in the photoelectric tracking system mounted on the moving platform, and its disturbance information extraction is a prerequisite for decoupling the system disturbance. In this paper, to get the specific signal of fs Hz, firstly, source signal of 2xfs Hz from the inertial sensor is needed. Then, single channel signal with frequency 2xfs is constructed into 2 aliased signals with the frequency of fs by down-sampling. Finally, a BSS (Blind Source Separation, BSS) algorithm is used to extract the disturbance signal in the aliased signals. Simulation results of ship borne data show that the algorithm can accurately extract the high-frequency disturbance signal of about 33 Hz in the aliased signals. The experimental results of the platform prove that the spectrum of the disturbance signal (10 Hz) and the spectrum of the target signal (0.1 Hz) do not overlap each other, and the correlation coefficient between the perturbation signal and the perturbation source signal is as high as 0.9976, which means the extraction of disturbance signal in the inertial sensor signals is realized.
Mining rich graphs: a graph transformation approach

There have been a large number of studies mining graph patterns, however, most of them only process graphs with nodes and edges having a single label. Often there is a need to represent a rich dataset as graphs with nodes and edges having multiple labels each label representing a feature in the dataset. For example, in a social network, multiple features could be associated to individuals (e.g., age, address, etc.) and their relationships (e.g., friend, foe, send message, etc.). To analyze these rich datasets, there is a need to extend existing graph mining algorithms to also mine rich graphs with nodes and edges having multiple labels. In this paper, we propose a novel algorithm and framework to transform richly labeled graphs (i.e., graphs with nodes and edges having multiple labels) to an equivalent set of simple labeled graphs (i.e., graphs with nodes and edges having single labels). The resultant simple graphs could be fed to most of the available graph mining algorithms to produce simple graph patterns. A reverse translation process is then employed to recover rich graph patterns from the simple graph patterns. We demonstrate that our proposed algorithm is scalable on various synthetic and real datasets. We experiment with three notable graph mining algorithms which are gSpan, CloseGraph, and Top-k LEAP algorithms. We show that our algorithm and framework could complement existing simple graph mining algorithms to allow them to mine rich graphs.
Arabic English Cross-Lingual Plagiarism Detection Based on Keyphrases Extraction, Monolingual and Machine Learning Approach

Due to rapid growth of research articles in various languages, cross-lingual plagiarism detection problem has received increasing interest in recent years. Cross-lingual plagiarism detection is more challenging task than monolingual plagiarism detection. This paper addresses the problem of cross-lingual plagiarism detection (CLPD) by proposing a method that combines keyphrases extraction, monolingual detection methods and machine learning approach. The research methodology used in this study has facilitated to accomplish the objectives in terms of designing, developing, and implementing an efficient Arabic – English cross lingual plagiarism detection. This paper empirically evaluates five different monolingual plagiarism detection methods namely i)N-Grams Similarity, ii)Longest Common Subsequence, iii)Dice Coefficient, iv)Fingerprint based Jaccard Similarity and v) Fingerprint based Containment Similarity. In addition, three machine learning approaches namely i) naïve Bayes, ii) Support Vector Machine, and iii) linear logistic regression classifiers are used for Arabic-English Cross-language plagiarism detection. Several experiments are conducted to evaluate the performance of the key phrases extraction methods. In addition, Several experiments to investigate the performance of machine learning techniques to find the best method for Arabic-English Cross-language plagiarism detection. According to the experiments of Arabic-English Cross-language plagiarism detection, the highest result was obtained using SVM classifier with 92% f-measure. In addition, the highest results were obtained by all classifiers are achieved, when most of the monolingual plagiarism detection methods are used.
Overlapping Community Detection with Graph Neural Networks

Community detection is a fundamental problem in machine learning. While deep learning has shown great promise in many graphrelated tasks, developing neural models for community detection has received surprisingly little attention. The few existing approaches focus on detecting disjoint communities, even though communities in real graphs are well known to be overlapping. We address this shortcoming and propose a graph neural network (GNN) based model for overlapping community detection. Despite its simplicity, our model outperforms the existing baselines by a large margin in the task of community recovery. We establish through an extensive experimental evaluation that the proposed model is effective, scalable and robust to hyperparameter settings. We also perform an ablation study that confirms that GNN is the key ingredient to the power of the proposed model.
Decremental dominators and low-high orders in directed acyclic graphs

Konstantinos Giannis M.Sc. in Computer Science, Department of Computer Science and Engineering, University of Ioannina, Greece, September 2018 Tilte of Dissertation: Decremental Dominators and Low-High Orders in Directed Acyclic Graphs Thesis Supervisor: Loukas Georgiadis Graphs are mathematical objects that model many diverse natural or man-made systems. A graph G = (V,E) consists of a set of vertices V together with a set E of edges. Graphs play an important role in computer science because they can be used to represent essentially any pairwise relationship between objects. For example, graphs can model transportation networks, communication networks, social networks, electronic circuits etc. Graphs are useful not only in computer science but in many academic areas such as chemistry, physics, mathematics and biology. Designing e cient graph algorithms can be a very challenging task. In this thesis, we consider practical algorithms for maintaining the dominator tree and a low-high order of a directed acyclic graph (DAG) under edge deletions. Let G=(V, E, s) be a directed graph with a distinguished start vertex s. The dominator tree D of G is a tree rooted at s, such that a vertex v is an ancestor of a vertex w if and only if every path from s to w include v. The dominator tree is a central tool in program optimization and code generation, and has many applications in other diverse areas including constraint programming, circuit testing, biology, and in algorithms for graph connectivity problems. A low high order of G is a preorder of D that certi es the correctness of D and has further applications in connectivity and path-determination problems. First, we provide a carefully engineered version of a recent algorithm [ICALP 2017] for maintaining the dominator tree of a directed acyclic graph through a sequence of edge deletions. Then, we show how how to extend this algorithm so that it also maintains a low-high order of the given DAG. Our algorithms, for both tasks, run in O(mn) total time and O(m+n) space, where n is the number of vertices and m is the number of edges before any deletions. These results trivially extend to the case of reducible graphs. We study the e ciency of our algorithms in practice by conducting an extensive experimental study, using real-world graphs, taken from a variety of application areas, and
Klasterisasi Data Supplier Menggunakan Algoritma Fuzzy C-Means (Studi Kasus: PT. Jogja Rekayasa Engineering)

INTISASIPenelitian dengan studi kasus pada PT Jogja Rekayasa Engineering ini adalah sebuah pengembangan dari aplikasi pencatatan keuangan, yang termasuk di dalamnya, kontrak, rencana anggaran proyek, rencana anggaran belanja, beserta data vendor, juga pekerjaan dan barang yang di supply. menjadi suatu tolok ukur pengambilan keputusan pemilihan vendor supplier yang tepat bagi perusahaan PT. Jogja Rekayasa Engineering yang sebelumnya sudah di implementasikan dengan aplikasi berbasis web. Proses penelitan memanfaatkan metode Fuzzy C-Means untuk klasterisasi pada data yang di ambil dari database aplikasi, penelitian ini di harapkan agar perusahaan dapat memilih serta menentukan supplier mana yang terbaik untuk dapat menjalin kerja sama, dengan memanfaatkan beberapa variabel pengukuran yang tersedia dari banyaknya data yang sudah di klaster. Penelitian sendiri akan berupa simulasi data yang besar untuk proses clustering itu sendiri. Ketika proses cluster dengan algoritma Fuzzy C-Means sudah berjalan seperti yang di harapkan, kemudian akan ditanamkan pada fitur aplikasi perusahaan untuk menentukan keputusan pemilihan vendor, yang akan di kerjakan oleh tim web developer mereka. Kata Kunci — SPK, Clustering, FCM, Fuzzy, Supplier, Vendor. ABSTRACTThis research with case study at PT Jogja Engineering Engineering is a development of financial recording application, including contract, project budget plan, budget plan, beside the vendor data, also work and goods in supply. become a benchmark decision making vendor selection appropriate supplier for company PT. Jogja Engineering Engineering which previously been implemented with web-based applications. The research process utilizes the Fuzzy C-Means method for clustering the data taken from the application database, this research is expected to enable the company to choose and determine which supplier is best to be able to work together, by utilizing several measurement variables available from the number of data already in the cluster. The research itself will be a large data simulation for the cluster-ing process itself. When the cluster process with the Fuzzy C-Means algorithm is already running as expected, then it will be embedded in the enterprise application features to determine the vendor selection decision, which will be done by their web developer team.Keyword – DSS, Clustering, FCM, Fuzzy, Supplier, Vendor.
Normalizing Emotion-Driven Acronyms towards Decoding Spontaneous Short Text Messages

Reflecting the rapid growth in the use of Social Networking Services (SNSs), it has of late become popular for users to share their feelings, impression, and opinions with each other, about what they saw or experienced, rapidly by means of short text messages (SMS). This trend has let a large number of users consciously or unconsciously use emotion-bearing words and also acronyms to reduce the number of characters to type. We have noticed this new emerging category of language unit, namely “Emotion-Driven Acronyms (EDAs)”. Because by definition, each acronym consists of less characters than its original full form, the acronyms for different full forms often coincidently identical. Consequently, the misuse of EDAs substantially decreases the readability of messages. Our long-term research goal is to normalize text in a corrupt language into the canonical one. In this paper, as the first step towards the exploration of EDAs, we focus only on the normalization for EDAs and propose a method to disambiguate the occurrence of an EDA that corresponds to different full forms depending on the context, such as “smh (so much hate / shaking my head)”. We also demonstrate what kind of features are effective in our task experimentally and discuss the nature of EDAs from different perspectives.
Facial expression recognition using feature level fusion

Abstract In this paper, a model for facial expression recognition (FER) fusing local and global features is proposed. Local features are extracted by dividing the face into multiple regions and carefully selecting regions of interest which assists in reducing redundancy. Global features are extracted from the entire subject face and features of interest are extracted for expression recognition. The major drawback with previously available techniques in literature is that they do not take finer details into account along with global geometric features. In our work, using concatenation fusion, FER is performed in an effective way; one part of model recognizes facial expressions on a complete scale while the other performs the same task on a finer scale, thereby improving the accuracy. Accuracy with the proposed fused model is increased to 93.52% in comparison to the accuracy of 74.11% with local binary patterns and 90.64% with global features when used as standalone technique.