Miracle-dz/UniFAR-FTUs huggingface dataset:
Intelligent Processing of Intrusion Detection Data

Intrusion detection technology, as an active and effective dynamic network defense technology, has rapidly become a hot research topic in the field of network security since it was proposed. However, current intrusion detection still faces some problems and challenges that affect its detection performance. Especially with the rapid development of the current network, the volume and dimension of network data are increasing day by day, and the network is full of a large number of unlabeled data, which brings great pressure on the data processing methods of IDS. In view of the tremendous pressure of intrusion detection brought by the current complex and high-dimensional network environment, this paper provides a feasible solution. Firstly, this paper briefly outlines the necessity of feature learning, the shortcomings of traditional feature learning methods and the new breakthroughs brought by deep belief network in feature learning, and focuses on the principle and working mechanism of deep belief network and Principal Component Analysis (PCA). Then, it constructs the intrusion detection model based on PCA-BP and DBN respectively. And through the experimental evaluation of the two detection models, a comparative experiment between deep belief network and principal component analysis is constructed. The experimental results show that deep belief network has unique advantages and good performance in feature learning. Therefore, deep belief network can be applied in the field of intrusion detection to extract effective features from the current high-dimensional and redundant network data, thereby improving the detection performance of IDS and its adaptability to the current complex and high-dimensional network environment.
Joint Spatial and Temporal Modeling for Hydrological Prediction

The accurate and timely estimation of river discharge plays an important role in hydrological modeling, especially for avoiding the consequences of flood events. The majority of existing work on hydrologic prediction focuses on modeling the inherent physical process for specific river basins, while the geographic-connections between rivers are largely ignored. Geographically connected rivers provide rich spatial information that can be used to predict discharge amounts. In this paper, we study a novel problem of exploiting both temporal patterns and spatial connections for hydrological prediction. We construct three relationship graphs for hydrological gauges in the study area: the hydraulic distance graph, the Euclidean distance graph and the correlation graph. We fuse these graphs into one hydrological network graph, and propose a novel framework ST-Hydro which exploits Graph Convolutional Networks (GCN) for learning the spatial feature representations, and Recurrent Neural Networks with carefully designed activation functions for capturing temporal features simultaneously for hydrological prediction. Experimental results on real world data set demonstrate that the proposed framework can predict the river discharge effectively and at an early stage.
Spherical Color-Shift Keying for Visible Light Communication Systems

In this paper, we propose the novel spherical colorshift keying (SCSK) schemes for visible light communication (VLC) systems, which can improve the system fidelity comparing to the conventional color-shift keying (CSK) techniques. Specifically, the constant-intensity constraint in conventional CSK schemes requires that the constellation symbols remain on a two-dimensional intensity plane. We propose to expand the envelope of CSK constellations to three-dimensional space subjected to a new spherical intensity constraint, such that the minimum Euclidean distance (MED) is enlarged. Furthermore, we convert the constellation design problem to the new spherical circle packing (SCP) problem, then solve it with the aid of the proposed spherical quasi-physical (SQP) model, where the socalled spherical heuristic quasi-physical algorithm (SHQPA) is employed to obtain the optimal SCSK constellations. Simulation results demonstrate that the new schemes can achieve beneficial gains upon their conventional counterparts.
Fusion of radiological images of Glioblastoma Multiforme using weighted average and maximum selection method

Medical image fusion is one of the blazing medical imaging research and it is a strategy for combining multiple images from distinct imaging modalities to build a unique image representation that will enhance and expand the scope of the image information. It offers a better approach to overcome the constraints of the modality of independent imaging. Misalignment of images from different modalities like CT, MRI, PET, SPECT can be handled using image registration to make the images appropriate for image fusion. The image fusion based on discrete wavelet transformation (DWT) is used in this paper to disintegrate the images and the low frequency sub-bands are merged through weighted average method and the high frequency sub-bands are fused through maximum selection method. As fusion is the primary preprocessing step that helps in achieving better detection and classification accuracy during further processing stages. It is important in medical analysis and diagnostics of GBM tumor detection. In this work, multimodal images for detecting GBM tumor are fused using weighted average and maximum selection method and is compared with existing image fusion techniques for performance analysis of fusion process.
Predicting the Protein Tertiary Structure by Hybrid Clonal Selection Algorithms on 3D Square Lattice

The article considers the task of predicting the tertiary structure of a protein based on its primary sequence. The task of protein folding is solved by searching for conformation with minimal energy, if you look in terms of optimization problem. The work describes the use of a hybrid artificial immune system. In our work we try to combine clonal selection algorithms with differential and trigonometric differential evolution algorithms. We use special coding and decoding methods for individual individuals. The affinity function reduces the number of solutions with self-intersections (incorrect conformations) in the developed hybrid algorithms. We chose the hydrophobic-polar Dill model on a three-dimensional square lattice to test our algorithms. The study shows the advantages of the developed algorithms in comparison with other existing methods using experimental studies of test sequences.
DeepOcean: A General Deep Learning Framework for Spatio-Temporal Ocean Sensing Data Prediction

The emerging Internet of Underwater Things (IoUT) and deep learning technologies are combined to provide a novel, intelligent, and efficient data processing and analyzing schema, which facilitates the sensing and computing abilities for the smart ocean. The underwater acoustic (UWA) communication network is an essential part of IoUT. The thermocline, in which temperature and density change drastically, affects the connectivity and communication performance between IoUT nodes, as well as the network topologies. In this paper, we propose DeepOcean, a deep learning framework for spatio-temporal ocean sensing data prediction, which consists of a generative module and a prediction module. We implement the generative module with a multi-layer perceptron (MLP) to capture the spatial dependencies and construct high-resolution data based on sparse observations. The prediction module is implemented with our proposed Multivariate Convolutional LSTM (MVC-LSTM) neural network, which captures both the spatio-temporal dependencies and the interactions of different oceanographic features for prediction. We evaluate the effectiveness of DeepOcean with Argo data, where the proposed framework outperforms fifteen state-of-art baselines in terms of accuracy.
Transforming growth factor-beta 1 increases glucocorticoid binding and signaling in macrophages through a Smad- and activated protein-1-mediated process.

BACKGROUND Renal inflammation is regulated by a network of local and systemic mediators. Of them, transforming growth factor-beta1 (TGF-beta 1) and glucocorticoids play an important role in deactivating monocytes/macrophages. We examined the hypothesis that TGF-beta 1 effects may be partially achieved through modulation of the sensitivity of these cells to glucocorticoids. METHODS Human promonocytic U 937 cells differentiated to a mature macrophage-like phenotype were exposed to recombinant TGF-beta 1 before specific binding of [3H] dexamethasone was measured. The expression of glucocorticoid receptor (GR) was examined by RNase protection assay and Western blot analysis. The role of Smad 2/3 and activator protein 1 (AP-1) in the response to TGF-beta 1 was determined by introducing transdominant negative mutants and decoy oligodeoxynucleotides, respectively. RESULTS U 937 cell exposure to TGF-beta 1 caused a dose- and time-dependent increase in [3H] dexamethasone binding to these cells, with a < or =twofold increase in the number of binding sites per cell, without modification of the affinity. The changes in glucocorticoid binding were associated with identical changes in GR protein and mRNA levels, that were explained by an increase in GR gene transcription rather than by posttranscriptional mechanisms. Functional inactivation of Smad 2/3 and AP-1 limited the response to TGF-beta 1, indicating a role for these transcription factors. Finally, increases in glucocorticoid binding to GR were responsible for increases in the ability of GR to transactivate minimal promoters containing glucocorticoid-responsive elements (GRE) [MMTV-Luc and (GRE)2 TK-Luc]. CONCLUSION TGF-beta 1 increases glucocorticoid binding and signaling in inflammatory cells through a Smad 2/3- and AP-1-mediated process. This may represent a new target for intervention to increase glucocorticoid responsiveness.
Reward-Punishment based User Utility Maximization Model for Optimal Real-time Pricing in Electricity Energy Supply

Due to uncertainty factors such as renewable energy, imbalance between supply and demand has a great impact on the stable operation of the smart grid. The demand response (DR) program can effectively solve the imbalance issue by guiding users to adopt reasonable electricity consumption manner. In addition, users should be able to benefit from participating in DR by changing the habit of using energy. In the present study, a dynamic reward-punishment based user utility maximization model is proposed for optimal real-time pricing (RTP) in electricity energy supply to reach the supply-demand balance and provide the optimal reward and punishment for users. Based on the dynamic reward and punishment mechanism, the proposed model can obtain the optimal electricity consumption by maximizing user utility and encourage users to adopt a reasonable electricity consumption manner. From the perspective of algorithm implementation, a distributed algorithm is proposed to achieve the purpose of the supply-demand balance with the optimal electricity price and optimal electricity consumption. The simulation results show that the proposed utility model and distributed algorithm are reasonable and effective in dealing with the imbalance problem between supply and demand.
An Adaptive Content Skipping Methodology based on User Behavioral Modeling

Adaptive Educational Systems (AES) have demonstrated the potential of improving learning efficacy by individualizing course delivery to particular user needs. Whereas the algorithms driving today’s AES are primarily based on user responses to quiz questions, these systems are now capable of capturing fine-granular behavioral data on users, such as keystroke measurements on lecture videos and social interactions on discussion forums. In this paper, we develop a methodology that leverages behavioral data for the task of content skipping in an AES, i.e., detecting content segments that are unnecessary for a user and passing over them automatically. Our methodology contains three modules: (1) a Behavioral Data Processor, which converts user behaviors and course content into algorithm features including course topics, (2) a User State Tracer, which maintains an estimate of user knowledge state and interest on a per-topic basis, and (3) a Content Skipping Trigger, which determines the segments to be removed from the course for this user. In evaluating our approach on two real-world datasets collected from courses hosted on our existing platform, we find 80-90% accuracy in terms of identify segments that users would themselves eventually skip. In doing so, we also perform some exploratory analysis to show how the prediction results can help instructors to improve the course design quality.
Very Novel Design and Mutual Coupling Analysis of a Wideband, Tightly Arranged DP massive MIMO (32T and 32R) Antenna Array for 5G Base Station Application

A Wideband, Tightly Arranged DP Massive MIMO (32T and 32R) Aerial Array for 5 G Base Station Application is provided with Very Novel Architecture and Mutual Coupling Analysis. The dual polarization is achieved by placing two chamfered aerial bow-tie dipole elements in orthogonal orientation and being fed with two coaxial cables. The design consists of chamfering the two arms of bow-tie aerials that enhance port isolation to port. The pattern of unidirectional radiation is obtained by placing a metal ground plane at a distance of λ/4. The radiating components are designed for operation at 3.5 GHz. Simulated tests of the expected dual-polarized chamfered aerial bowtie show a sub-6GHz bandwidth (2.84.0 GHz) with VSWR < 1.5. Isolation between the two ports is-27dB and reciprocal coupling is around-25 dB and for both the polarization a gain of 9.1dBi is observed. The aerial overall height is 0. 25λ0 and the design is simple and easy to manufacture. This Aerial is thus the most suitable for future 5G BTS implementations.
A Dynamic Illustration Approach For Arabic Text

In this paper, we present an approach to dynamically transform simple Modern Standard Arabic children’s stories scripts to the best representative images that can illustrate efficiently the meaning of words and word senses. We connect formally multiple datasets involved in our framework. We then apply several techniques to find the images and associate them with related word senses. First, we apply natural language processing techniques to analyze the text in stories and we build a semantic representation of main characters and events in each paragraph. Second, we apply various query formulation techniques as a brief scenario to enhance image web search which showed better accuracy as per preliminary results. Third, most significant queries are chosen to retrieve a list of possible candidate images from our multimedia database and search engines (i.e., Google and Bing). Instructors can then select and validate the ranked contextual images to compose the final visualization for each paragraph.
ITUS: A Secure RISC-V System-on-Chip

The rising tide of attacks, in the recent years, against microprocessors and the system-on-chip (SoC) space as a whole, has led to a growing number of studies into security of SoCs. Security fortification is often incorporated as a follow-up feature to existing systems and many vulnerabilities cannot be patched without significantly degrading performance. A holistic approach to address the security challenge needs to include security-first design principles, security-aware test and verification methodologies, and well-quantified performance trade-off analysis. In this paper, we report the design principles of ITUS 1, a secure SoC based on RISC-V architecture. In parallel, a systematic overview of various design and automation efforts towards achieving SoC security is presented.1after Itus, a relevant name from Greek mythology
Situation Management of a Self – Organized Information Transmission Network in Fast Deployment Security Systems

The method of situational management of self-organizing infocommunication network, based on the construction of an adaptive management strategy, capable of providing certain noise immunity of information transmission in the conditions of destructive influences is considered. Examples of the use of situational control to ensure the formation of an information-reliable route of information transmission in fast deployment security systems are given.
An Integral Sliding Mode Controller for a Vertical Take-Off and Landing System

This work proposes the design of an Integral Sliding Mode Controller (ISMC) to stabilize a Vertical Take-Off and Landing (VTOL) System. The sliding surface is employed to drive the system towards equilibrium and thus, stability. The parameters of the sliding mode controller have been estimated by minimzing an objective function using an evolutionary algorithm, namely, Squirrel Search Algorithm (SSA). Simulations have been carried out for reference signal tracking and disturbance rejection. The performance of the proposed ISMC has been compared with that of proportional-integral-derivative (PID) and fractional order PID controllers. The obtained results are promising and confirm that the ISMC exhibits superior performance when compared to the other controllers.
Non-invasive Cuff-less Measurement of Blood Pressure Based on Machine Learning

This article attempts to consider a new approach to continuous measurement of blood pressure (BP), based on the pulse transit time (PTT) between two points of a blood vessel and the shape of pulse wave (PW). The measuring of PW/PTT based on the signal processing and analysis of the patient’s electrocardiogram (ECG) and photoplethysmogram (PPG). The PTT-based blood pressure estimation algorithms, used by most authors, suggests their individual calibration for each patient. More flexible is a different approach - the use of machine learning. It is especially noted that the use of machine learning had reduced the error of blood pressure measurement.
Systematic Review on Security and Privacy Requirements in Edge Computing: State of the Art and Future Research Opportunities

Edge computing is a promising paradigm that enhances the capabilities of cloud computing. In order to continue patronizing the computing services, it is essential to conserve a good atmosphere free from all kinds of security and privacy breaches. The security and privacy issues associated with the edge computing environment have narrowed the overall acceptance of the technology as a reliable paradigm. Many researchers have reviewed security and privacy issues in edge computing, but not all have fully investigated the security and privacy requirements. Security and privacy requirements are the objectives that indicate the capabilities as well as functions a system performs in eliminating certain security and privacy vulnerabilities. The paper aims to substantially review the security and privacy requirements of the edge computing and the various technological methods employed by the techniques used in curbing the threats, with the aim of helping future researchers in identifying research opportunities. This paper investigate the current studies and highlights the following: (1) the classification of security and privacy requirements in edge computing, (2) the state of the art techniques deployed in curbing the security and privacy threats, (3) the trends of technological methods employed by the techniques, (4) the metrics used for evaluating the performance of the techniques, (5) the taxonomy of attacks affecting the edge network, and the corresponding technological trend employed in mitigating the attacks, and, (6) research opportunities for future researchers in the area of edge computing security and privacy.
Color instance segmentation and classification of cervix images

Instance segmentation is the task of assigning a label to each pixel in the image, treating objects of same class separately. On the other hand, classification assigns a label to the whole image. In this paper, we are comparing both methods on a data-set of different cervix types. It was required to detect which cervix type out of 3 categories the image holds. For classification using Convolutional neural network, the region of interest is segmented before starting to train the network. However, in instance segmentation, the input is the full image.Instance segmentation happened to outperform the classification pipeline on this data-set with accuracy of 62% vs 55% for the latter approach
Comprehensive Survey of Machine Learning Approaches in Cognitive Radio-Based Vehicular Ad Hoc Networks

Nowadays, machine learning (ML), which is one of the most rapidly growing technical tools, is extensively used to solve critical challenges in various domains. Vehicular ad hoc network (VANET) is expected to be the key role player in reducing road casualties and traffic congestion. To ensure this role, a gigantic amount of data should be exchanged. However, current allocated wireless access for VANET is inadequate to handle such massive data amounts. Therefore, VANET faces a spectrum scarcity issue. Cognitive radio (CR) is a promising solution to overcome such an issue. CR-based VANET or CR-VANET must achieve several performance enhancement measures, including ultra-reliable and low-latency communication. ML methods can be integrated with CR-VANET to make CR-VANET highly intelligent, achieve rapid adaptability to the dynamicity of the environment, and improve the quality of service in an energy-efficient manner. This paper presents an overview of ML, CR, VANET, and CR-VANET, including their architectures, functions, challenges, and open issues. The applications and roles of ML methods in CR-VANET scenarios are reviewed. Insights into the use of ML for autonomous or driver-less vehicles are also presented. Current advancements in the amalgamation of these prominent technologies and future research directions are discussed.
Novel Single Phase E-Shaped Double-Stator Flux Switching Permanent Magnet Generator

A novel single-phase E-shaped Double-Stator Flux Switching Permanent Magnet Generator (E-DS-FSPMG) is presented. The permanent magnets (PMs) and the armature coils are located on the E-shaped stators. The rotor is modular. The topology and the operating principles are discussed in detail. The main geometrical parameters of the proposed generator are optimized based on the sensitivity analysis, in terms of minimum cogging torque, maximum flux linkage first component, and minimum flux linkage Total Harmonics Distortion (THD). The no-load electromagnetic characteristics of the optimized generator, including flux density, flux linkage, Electro-Motive Force (EMF), and cogging torque at a given speed, are presented. Furthermore, the full-load performance of the proposed generator is investigated on the basis of the Two-Dimensional Finite Element Method (2D-FEM). Also, the Unbalanced Magnetic Force (UMF) of the optimized proposed single-phase E-DS-FSPMG is analyzed and compared with a single-phase single-stator E-shaped FSPM generator (E-SS-FSPMG). According to the simulation results, the UMF amplitude of the proposed generator is about 50% less than that of the considered single-phase E-SS-FSPMG.