Miracle-dz/UniFAR-FTUs huggingface dataset:
DDoS Attacks Detection and Mitigation in SDN Using Machine Learning

Software Defined Networking (SDN) is very popular due to the benefits it provides such as scalability, flexibility, monitoring, and ease of innovation. However, it needs to be properly protected from security threats. One major attack that plagues the SDN network is the distributed denial-of-service (DDoS) attack. There are several approaches to prevent the DDoS attack in an SDN network. We have evaluated a few machine learning techniques, i.e., J48, Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbors (K-NN), to detect and block the DDoS attack in an SDN network. The evaluation process involved training and selecting the best model for the proposed network and applying it in a mitigation and prevention script to detect and mitigate attacks. The results showed that J48 performs better than the other evaluated algorithms, especially in terms of training and testing time.
Evaluation of Big Data Governance - Combining a Multi-Criteria Approach and Systems Theory

The main objective of Big data is to meet business competitiveness and facilitate decision-making. To achieve this goal, the implementation of a governance plan is necessary. In this article, we propose a big data governance evaluation approach based on the fact that big data governance is seen as an artifact having the characteristics of a system. We develop a multi-criteria hierarchy organized according to the main dimensions of systems theory. The approach is illustrated on a practical case.
Sigmoid based Adaptive Noise Estimation Method for Speech Intelligibility Improvement

The requirement of noise spectrum is necessary to make a reliable speech enhancement system. The key factor is both frequency and time dependent smoothing constant, which is responsible to update the noise spectrum. Generally, in the SPP (Speech Presence Probability) method the smoothing constant is computed by using recursive averaging approach due to this required smoothed version of noise spectrum is not achieved which leads to degrade the speech quality. Hence, in this paper sigmoid function is used as a smoothing parameter, which depends upon a posteriori SPP (speech presence probability). The characteristics of this function are more flexible due to which the decisions will be too soft to update the noise power spectrum. Moreover, independent adjustment of slope and mean of the sigmoid function provides appropriate trade-off between the underestimation and overestimation of the noise. On the behalf of intelligibility and quality of the enhanced signal the accomplishment of the propound method is evaluated by using objective measures. It is clearly shown that in simulation results the proposed method is much reliable than the reference approaches.
Off-Road Drivable Area Extraction Using 3D LiDAR Data

We propose a method for off-road drivable area extraction using 3D LiDAR data with the goal of autonomous driving application. A specific deep learning framework is designed to deal with the ambiguous area, which is one of the main challenges in the off-road environment. To reduce the considerable demand for human-annotated data for network training, we utilize the information from vast quantities of vehicle paths and auto-generated obstacle labels. Using these auto-generated annotations, the proposed network can be trained using weakly supervised or semi-supervised methods, which can achieve better performance with fewer human annotations. The experiments on our dataset illustrate the reasonability of our framework and the validity of our weakly and semi-supervised methods.
Communication Strategies for Automated Merging in Dense Traffic

The aim to integrate automated vehicles in manual traffic motivates the investigation of the communication of road users. Especially in situations of high traffic density, an automated lane change without cooperation of interacting traffic participants cannot be executed. Therefore, an automated vehicle needs a distinct interpretable strategy to inform interacting road users of its intention. In this presented driving study, different variations to announce a lane change to interacting traffic are performed on a test track and are evaluated (with 40 participants) in a within-subjects design. To gain standardized situations, all lane change maneuvers are executed automatically. During the study, different factors to announce a lane change, like the time to set the indicator, a weak or strong deceleration to the target gap or a lateral offset in advance of the lane change are investigated. Moreover, the influence of the lane change direction as well as the velocity of the target gap are analysed. The study illustrates that not only an indicator is crucial to announce a lane change. Also a strong deceleration to the target gap influences the processing of the information and cooperation of interacting traffic participants. Other factors, such as a lateral offset in advance of a lane change is evaluated as less important. In addition, the study implies higher cooperation and an influence of the lane change direction at slow velocities.
Paths of Innovation Affected by Customer Category: Case of Early and Majority Market Customers in Semiconductor Technology

Because customers provide important external knowledge or resources, understanding customer behavior is important to identify the external sources and processes of innovation. While multiple factors affect customers’ innovative behavior, this study explored whether different customer categories will have different effects on innovative behavior. A case study of semiconductor markets suggests that early market customers use switching path only while the majority market customers used multiple innovation paths (switching, new entrance, or leapfrogging) to achieve market goals. This study has theoretical implications for the analysis of the effect of customer category on innovative behavior, and provides practical insights for customers and suppliers to develop innovative strategy.
Moving Obstacle Avoidance and Topology Recovery for Multi-agent Systems

This paper proposes a novel moving obstacle avoidance algorithm for multi-agent systems. The method has robustness in maintaining formation shape. Even if link failure occurs among agents when avoiding obstacles, the communication topology of the system can be recovered based on the conditions we obtain. The main idea includes two parts, i) a flexible function of relative velocities and positions between agents and obstacles is designed to avoid moving/stationary obstacles, and ii) based on initial adjacent matrix and graph connection characteristic, a topology recover mechanism is proposed to guarantee formation shape and no extra links are involved. The proposed algorithm has the following advantages: i) It is able to recover formation shape, even if some links among agents are broken while avoiding moving obstacles; ii) In the process of rebuilding communication links, the proposed algorithm can protect communication topology from being altered maliciously. Furthermore, we obtain conditions of the topology recovery for both directed and undirected graph. Some simulations are conducted to demonstrate the efficiency of the proposed algorithm.
Data-Driven Uncertain Modeling and Optimization Approach for Heterogeneous Network Systems

In the age of IoT, the heterogeneous network fusion becomes a tremendous issue, a dilemma in heterogeneous network is how to integrate the resource and allocate the multifarious services which is remarkable but seriously difficult to system-level model and quantify. Aiming at the representation of uncertainly systems design element distribution, combined with modeling and optimization, here we proposed a novel mixture stochastic process and multi combination upper confidence bound strategy for data-driven Bayesian Optimization. This method can be generally applied to the uncertain modeling and design problem in heterogeneous networks' scenarios. We applied the method to the multi-services scenario of space information network systems. Compared with other combinations of surrogate models with origin acquisition strategies in the experiments, our method brought up a better representation and optimization results.
Power Components Calculation and Their Application in Presence of High Harmonics

The article presents the issues of power components calculation in the presence of high harmonics. Within the study, definitions by various scientists and existing standards are applied. The most appropriate method for calculating the power components in AC circuits in presence of high harmonics is selected. The selection was conducted from the viewpoint of reactive power compensation and the possibility of identifying the source of high harmonic distortions. Method for detection the sources of high harmonics is based on metering distortion power. This method can easily be implemented in existing measurement systems.
Bandwidth Planning of Web Services in Changing Contexts Based on Network Simulation

Minimizing network usage is often a key driver of cost control for web services in the cloud. Most of the literature focuses on resources such as CPU, memory, and disk but the bandwidth is somehow neglected. In fact, it's very challenging to predict the network throughput of modern web services due to the factors of miscellaneous service responses and complex network transportation. Previously we proposed a what-if analysis approach named Log2Sim to predict bandwidth consumption and response time of web services based on network simulation, however, how to use it to address bandwidth problems in different changing contexts is not well studied. In this paper, we work this out with some extensions of Log2Sim, and a general process of bandwidth planning in different changing contexts is given. We also use a classic web benchmark and design various controlled experiments to evaluate this extended approach from different perspectives in multiple scenarios.
Spectral Efficiency of Multi-pair Two-Way Massive MIMO Relay With Correlated Hardware Distortion

Most of the existing works on massive multi-input multi-output (MIMO) relaying consider the hardware distortion to be uncorrelated across antennas. We consider multi-pair two-way half-duplex massive MIMO relaying with correlated hardware distortion. We study the effect of distortion correlation on the spectral efficiency (SE) in two scenarios when i) relay is equipped with low-quality hardware; and ii) both relay and users are equipped with low-quality hardware. We numerically show that the distortion correlation considerably affects the SE in the former case, especially for small number of users and/or large number of relay antennas. We also numerically show that when the user has low-quality hardware, the hardware distortion correlation can be neglected for moderate number of users and/or number of relay antennas.
Comparison of Machine Learning Based Methods for Residential Load Forecasting

Load forecasting is a vital factor in electrical power systems due to technical and economic implications. Its forecasting is, however, a very challenging task due to many variable factors like occupant behavior, household size, loads present in the household and many other factors. In this article, three different machine learning algorithms have been applied for load forecasting on a load dataset of an Estonian household which was measured for a whole month. The simulation results of the Support Vector Machine (SVM) based forecasting model gives best results when compared with the real data. The validation results indicate that it has the lowest Root Mean Square Error (RMSE) value of 187.73 which is 28% and twice less than tree based and linear regression, respectively.
Improving Hearing Healthcare with Big Data Analytics of Real-Time Hearing Aid Data

Modern hearing aids are not simple passive sound enhancers, but rather complex devices that can log (via smartphones) multivariate real-time data from the acoustic environment of a user. In the evotion project (http://h2020evotion.eu) such hearing aids are integrated with a Big Data analytics platform to bring about ecologically valid evidence to support the hearing healthcare sector. Here, we present the background of the Big Data analytics platform and demonstrate that modeling of longitudinally sampled data from hearing aids can support clinical investigations with hypotheses about hearing aid usage prognosis, and support public health decision-making within the hearing healthcare sector by simulation techniques. We found, that distinct characteristics of the acoustic environment significantly modulate how hearing impaired individuals use their hearing aids. Higher sound levels and an increased sound diversity but degraded signal quality all predicts more minutes of use per hour. By simulation, we show that a projected increase in the overall sound levels by 10dB followed by a 4dB increase in noise exposure will increase the need for hearing aid use by an additional 1 hour/day across a population of hearing impaired hearing aid users.
Exploring Technology and Engineering Management Research Landscape

This study set out to explore the basic features of the field by using scientific articles in the field of Technology and Engineering Management (TEM). In this context, publication data indexed in Web of Science (WoS) were used. The obtained data were recorded in the relational database and prepared for analysis. In the analysis of the data, the R programming language, the CiteSpace and the Ggplot2 library were used. Social Network Analysis (SNA) and Bibliometric were used as methods. Accordingly, the longest-running journal in the field is IEEE-TEM. We identified 6 basic clusters in TEM studies; Open innovation, management practices, bibliometric analysis, corporate opinion, academic success creation and health technology literature. Author collaboration in the TEM field shows an increasing trend. For 2018, the average number of authors per publication was calculated as 2.98. In the area of TEM, literature obsolescence (aging) ranges from 12 to 18 years. In terms of the topics discussed in the field, a change has been observed since 2003. In other words, number of researches on the topics in SMEs, regional information system, efficiency measurement model and strategic information system clusters has been decreasing since 2003.
Simultaneous description of logical design and implementation of automated driving systems

As represented by automated driving systems, the complexity of in-vehicle systems is rapidly increasing. The process related to the design, implementation, and validation of the system has become complicated. To reduce this complexity, a function description method suitable for the development of an automated driving system and an interpreter for directly executing this function description in software are proposed. To describe the function logically and strictly, the description sentence is based on an ontology model for traffic context. The description is also based on one-time predicate logic to enable verification by a theorem prover. By using this method, the behaviors of the automated driving system are directory linked with the description of the function definition.
Fusion Method of Convolutional Neural Network and Support Vector Machine for High Accuracy Anomaly Detection

In this paper, binary classification methods using support vector machines (SVM) obtained from one-class learning and two-class learning are introduced. Firstly, an application of deep CNN (DCNN) for visual inspection is developed and is trained using a large number of images to inspect undesirable defects seen in resin molded articles. Then, the trained DCNN named sssNet and well-known Alexnet are respectively incorporated with two kinds of one-class learning based SVMs to classify sample images with high recognition rate into accept as OK category or reject as NG category, in which compressed feature vectors obtained from the DCNNs are used as the inputs for the SVMs. The performances of two SVMs obtained from one-class learning are compared and evaluated through training and classification experiments. Then, another SVM obtained from two-class learning is introduced. Finally, a template matching technique is further applied to extract important target areas from original training and test images. This will be able to enhance the reliability and accuracy for binary classification.
Cost-Efficient Dependent Task Offloading for Multiusers

The extensive use of mobile intelligent devices, such as smart phones and tablets, induces new opportunity and challenge for computation offloading. Task offloading is an important issue in a system consisting of multiple types of devices, such as mobile intelligent devices, local edge hosts and a remote cloud server. In this paper, we study the offloading assignment of multiple applications, each one comprising several dependent tasks, in such a system. To evaluate the total cost in the offloading process, a new metric is introduced to take into account features of different devices. The remote server and local hosts are more concerned about their processors utilization, while mobile devices pay more attention to their energy. Therefore, this metric uses relative energy consumption to denote the cost of mobile devices, and evaluates the cost of the remote server and local hosts by the processor cycle number of task execution. We formulate the offloading problem to minimize the system cost of all applications within each application’s completed time deadline. Since this problem is NP-hard, the heuristic algorithm is proposed to offload these dependent tasks. At first, our algorithm arranges all tasks from different applications in a priority queue considering both completed time deadline and task-dependency requirements. Then, based on the priority queue, all tasks are initially assigned to devices to protect mobile devices with low energy and make them survive in the assignment process as long as possible. At last, to obtain a better schedule realizing lower system cost, based on the relative remaining energy of mobile devices, we reassign tasks from high-cost devices to low-cost devices to minimize the system cost. Simulation results show that our proposed algorithm increases the successfully completed probability of whole applications and reduces the system cost effectively under time and energy constraints.
A Method of Evaluating Rehabilitation Stage by sEMG Signals for the Upper Limb Rehabilitation Robot

Stroke can easily lead to nerve injury, which will bring inconvenience in life. Research shows that this kind of injury can be improved by using rehabilitation robots for rehabilitation training. In this paper, a method of evaluating and classifying rehabilitation stages by sEMG signals are proposed, and the feasibility of this method is verified. Firstly, a method to distinguish the impaired side from the healthy side using sEMG signals are proposed, and its accuracy is verified by simulation experiments. Through this experiment, some better feature are chosen to evaluate the muscle strength. sEMG signals of the upper-limb muscles of the patients are denoised and analyzed in time domain and frequency domain. sEMG signals are then obtained into the training set and the test set, using the training set to make the weighed evaluation for the unknown rehabilitation stage and calculate the final classification results. The experiments are carried out and the result verified the feasibility of using sEMG signals to distinguish the rehabilitation stage.
Transmit Data Rate Control Based Decentralized Congestion Control Mechanism for VANETs

Vehicular Ad Hoc Networking (VANET)technology is becoming prominent in this era. It is defined to improve on-road conditions and provide safety and different entertainment applications to end users in orderly and efficient manner. The idea of making everything connected in Internet of things (IoT)has evolved as a promising networking system and VANETs are one of the components of it. In this paper, we present a multistate active transmit data rate control based decentralized congestion control mechanism. The real world scenario generated from SUMO is considered for simulation. Simulation results show that, data rate control mechanism performs better over transmit power control and without adaptation mechanisms.
Provision of Ancillary Services of a Nanogrid in Grid Connected Mode

In the future more and more conventional power plants, which provide ancillary services such as provision of reactive power for voltage control and primary control power (active power) for frequency control to the transmission system and thus secure the energy supply, are going to be replaced by renewable energy sources. Due to this fact new concepts for providing these services by renewable power sources will be necessary in the future to maintain stability of the network operation. This refers to the delivery of active and reactive power by distributed generation (DG) and distributed storage (DS). Beyond that DG and DS can be found nowadays in households or also called nanogrids. This research work presents a concept of a nanogrid that can provide ancillary services to the distribution grid in the low voltage level and transfer reactive power as well as primary control power to higher voltage levels by upscaling, which means the connection of many nanogrids. The implementation of the concept is done in a real system and also in a simulation environment that uses simplified mathematical models.
An Approach for a Requirement Analysis for an Autonomous Family Vehicle

Various manufacturers have presented concepts for autonomous vehicles in recent years. However, none of these concepts were designed specifically for the needs of a multi-generation family. A vehicle that meets these requirements can be used independently even by those who are dependent on an accompanying family member when using a conventional car. Furthermore, an autonomous family vehicle, as discussed in this paper, is intended for private use. The basis of the requirements of such a vehicle are the physical and mental abilities of its users. This paper pursues the approach of deriving initial functional requirements by considering examples of accompanied rides in a conventional car. Additionally, an outlook is given on the derivation of non-functional requirements necessary for the safe operation of the autonomous family vehicle.