Miracle-dz/UniFAR-FTUs huggingface dataset:
Detecting Saturation Attacks in Software-Defined Networks

Software-Defined Networking (SDN) is a new net-working paradigm that has revolutionized network architectures. The separation of data and control planes improves the efficiency of packet delivery. However, there exist various security attacks against SDN systems. For example, a saturation attack may disturb the normal delivery of packets and even make the SDN system out of service by flooding the data plane, the control plane, or both. This paper presents an anomaly detection method, called SA-Detector, for dealing with a family of saturation attacks. SA-Detector builds upon the study of self-similarity of OpenFlow traffic, which has shown that the normal and abnormal traffic patterns between the controller and the OpenFlow switches have different characteristics. We have evaluated the performance of SA-Detector with different time scales, network scales, Internet applications, and attack implementations. The experimental results show that the average accuracy is 96.54% and the average precision is 92.06%. This indicates that SA-Detector is effective for detecting saturation attacks.
Calibrating Away Inaccuracies in Ultra Wideband Range Measurements: A Maximum Likelihood Approach

A calibration framework for an ultra-wideband localization system employing inertial measurement units is presented. No external motion capture system or other sensors are required for the calibration procedure. Given a covariance function for the error in the range measurements, a range measurement model based on a Gaussian process is obtained by maximizing the joint likelihood of angular rate, acceleration, and range measurements. The framework is experimentally evaluated, and it is shown how the resulting measurement model, integrated in a standard Kalman filter, can be used for real-time localization on a platform with limited computational resources. The calibration significantly improves the localization accuracy for randomly generated trajectories and different localization system setups.
Unbalance Data Processing Strategy for Protein Interaction Sites Prediction

Protein-protein interactions play essential roles in various biological progresses. Identifying protein interaction sites can facilitate researchers to understand life activities and therefore will be helpful for drug design. However, the unbalance between the negative and positive samples comes from the current definitions of interaction sites places restriction on the prediction of protein interaction sites by computational approaches. In this work, we proposed three imbalance data processing strategies to improve the performance of protein interaction sites prediction. We first proposed the extraction of relevant features based on the evolutionary conservation of amino acids to predict protein interaction sites. At the same time, three methods are proposed to deal with the imbalance of positive and negative samples in data sets. Experimental results demonstrated the effectiveness of our method.
User-Prosthesis Interface for Upper Limb Prosthesis Based on Object Classification

The complexity of User-Prosthesis Interfaces (UPIs) to control and select different grip modes and gestures of active upper-limb prostheses, as well as the issues presented by the use of electromyography (EMG), along with the long periods of training and adaptation influence amputees on stopping using the device. Moreover, development cost and challenging research makes the final product too expensive for the vast majority of transradial amputees and often leaves the amputee with an interface that does not satisfy his needs. Usually, EMG controlled multi grasping prosthesis are mapping the challenging detection of a specific contraction of a group of muscle to one type of grasping, limiting the number of possible grasps to the number of distinguishable muscular contraction. To reduce costs and to facilitate the interaction between the user and the system in a customized way, we propose a hybrid UPI based on object classification from images and EMG, integrated with a 3D printed upper-limb prosthesis, controlled by a smartphone application developed in Android. This approach allows easy updates of the system and lower cognitive effort required from the user, satisfying a trade-off between functionality and low cost. Therefore, the user can achieve endless predefined types of grips, gestures, and sequence of actions by taking pictures of the object to interact with, only using four muscle contractions to validate and actuate a suggested type of interaction. Experimental results showed great mechanical performances of the prosthesis when interacting with everyday life objects, and high accuracy and responsiveness of the controller and classifier.
On-Demand Cooperation Among Multiple Infrastructure Networks for Multi-Tenant Slicing: a Complex Network Perspective

Network slicing is a promising technology for wireless networks to provide various services using limited resources of infrastructure networks. In order to decrease capital expenditure and operational expenditure, multi-tenant slicing provides resource sharing mechanisms among multiple infrastructure providers (InPs). However, one of the main challenges for cooperation among InPs is to meet the diverse demands of various services considering the different structural characteristics of each InP. In this paper, we investigate on-demand cooperation among multiple infrastructure networks from a perspective of the complex network. Four typical network topologies are used to imitate different practical infrastructure networks. The proposed cooperation strategy contains two stages, the selection of connectors and the creation of interconnections. We adopt the complex network theory to obtain the topological information of infrastructure networks. With the topological information, connectors are selected with selection fitness, and interconnections between connectors are created using the two-sided matching. Finally, extensive simulations are conducted, and the effects of variables in the two stages are analyzed. The simulation results illustrate that the proposed strategy performs better in terms of meeting the demand.
Effects of Electrical Pain Stimuli on Immersion in Virtual Reality

The ultimate goal of virtual realty is to create a simulated world around us which is indistinguishable from the physical world as we know it. In such an environment our actions could lead to severe effects on our body. What would happen if one gets hit by a bullet, car or lightning? How would the felt pain change our perception of the virtual environment? It turns out that the influence of nociception (pain) on the human perception in virtual environments is not well covered in the scientific literature besides pain control/management. The goal of this publication is to investigate the influence of pain stimuli on immersion as well as decision making and to foster research and discussion in this direction.
Adaptive Artificial Bee Colony for Numerical Optimization

Artificial bee colony (ABC) is a population-based optimizer. It simulates bees' social behavior for searching better solutions in solution space. Either too large or too small colony size will influence ABC's solution searching performance directly. In order to deal with the problem, in this paper, an adaptive colony is proposed. The adaptive colony will join potential bees or eliminate redundant bees, according solution searching situation. In experiments, 10 test functions of CEC 2015 are adopted for testing proposed method and compare it with three ABC variants. From the results, it can be observed that the proposed method performs better than other three related works.
An Autonomous Demand Response Algorithm based on Online Convex Optimization

A price-based demand response program is a viable solution for distribution network operators (DNOs) to motivate electricity consumers toward scheduling their load demand during off-peak periods. This paper addresses the problem of load scheduling in a demand response program, while accounting for load demand uncertainty and the distribution network operational constraints. The centralized load control is a non convex optimization problem due to the ac power flow equations. We use convex relaxation techniques to transform the problem into a semidefinite program (SDP), which is solved using online convex optimization techniques to address the load demand uncertainty. To tackle the issue of computational complexity, we use proximal Jacobian alternating direction method of multipliers (PJ-ADMM) to decompose the centralized problem into the customers' load scheduling subproblems. The decentralized algorithm is executed by each customer to schedule its load demand in real-time. Via simulations on the IEEE 37-bus test feeder, we show that the proposed algorithm enables customers to approximate the optimal load profile in the benchmark scenario without load uncertainty, and the approximation is tight. Furthermore, we show a negligible gap of 2.3% between the customers' cost with the proposed algorithm and the cost in the benchmark scenario.
High Resolution Time-of-Flight Measurement with Narrow-Band COTS Ultrasonic Transducers

Ultrasonic transducers (a pair of speaker and microphone) are extensively used in distance and wind speed measurements. The measurement of Time-of-Flight (TOF) between an ultrasonic speaker and a microphone dominates the accuracy. This paper presents a method to improve the TOF measurement accuracy using narrow-band commercial off-the-shelf (COTS) ultrasonic transducers. The method features the use of an experimentally captured waveform to correlate the transmitted and received signals and the use of a phase modulated ultrasonic signal. The method is experimentally evaluated to show that the proposal can significantly reduce the TOF measurement error, from 25.5% to 0.3%.
Forecast of Solar Energy Production - A Deep Learning Approach

Solar energy penetration both at utility scale and residential scale has been increasing at an exponential rate. However, its stochastic nature poses great challenge to power grid operation. Knowing how much solar energy generation in advance is vital for power grid balancing, planning and optimization. Therefore, solar energy generation forecast is essential for the stability and operation efficiency of today's smart grid. Although the sun path and energy can be computed with physical laws, the prediction of solar energy generation and production remains very challenging problem both in the field of physical simulation and artificial intelligence. The main reason lies in the fact that the actual solar production are impacted by many factors including the sun position, weather condition and the characteristics of photovoltaic panel, curtailment, etc. Especially in cloudy day, where the cloud movement becomes the main factor in solar energy production. However, predicting the movement of cloud is extremely difficult. In this paper, we present several deep convolutional neural networks utilizing high resolution weather forecast data exploring various temporal and spatial connectivities to capture the cloud movement pattern and its effect on forecasting solar energy generation for solar farms. Comparing with state-of-the-art forecast error rate, we have been able to reduce the error rate from about 21% in the persistent model, to 15.1% from the SVR model, and to 11.8% from the convolutional neural networks. These improvements have significant impact on the healthy growth of the solar energy industry, will save billions of dollars for the US utilities and most importantly reduce dependency on fossil fuel and reduction in CO2 emission.
A Review on Rule-Based and Hybrid Stemming Techniques

Reducing a word to its original form called stemming is extremely important in the field of natural language processing, especially to develop morphological analyzer and information retrieval systems. Use of stemming has decreased the dimension of indexing. Stemming algorithms are developed during last few decades to automate the process requires different approaches as it is mostly language specific. In this paper we have discussed and given a comparative view about different stemming approaches developed so far. It focuses mainly on four stemming techniques namely Rulebased, Statistical, Hybrid and Unsupervised approach.
On fault detection and isolation applied on unicycle mobile robot sensors and actuators

In this paper, a combination of model-based and hardware redundancy methods is proposed for both sensor and actuator fault detection and isolation (FDI) of unicycle mobile robots. A focus is brought on robot drift-like faults on wheels and sensors. The goal is to detect and isolate the faulty component as early as possible. The proposed method is based on a combination of hardware redundancy and a bank of Extended Kalman Filters (EKF). Each filter is tuned for a specific fault, to generate residuals with different signatures under different component faults. The different signatures allow the fault isolation. Simulation results show that the proposed method allow to detect both wheels and sensors small drift-like faults and isolate them as early as possible.Fault Detection and Isolation (FDI), Unicycle mobile robot, Drift-like faults, Extended Kalman Filter (EKF), Hardware redundancy.
Sensitivity Enhancement of Current Mirror Readout Circuit Based Piezoresistive Pressure Transducer Using Differential Amplifier

This work describes an idea of implementing differential amplifier for enhancing the sensitivity of NMOS current mirror readout circuitry based piezoresistive pressure sensor. The complete pressure sensing structure along with differential amplifier consists of five n-channel MOSFETs which were designed and simulated using five micron CMOS technology parameters in T-Spice software for a drain current of 1 mA. The NMOS current mirror circuit has three n-channel MOSFET. MOSFET M1 of current mirror circuit acts as a reference transistor whereas MOSFET M2 and M3 act as the pressure sensing transistor. The pressure sensing MOSFETs M2 and M3 are embedded on a flexible square silicon diaphragm to sense both tensile and compressive stresses induced under an externally applied pressure. An n-channel MOSFET based differential amplifier consist of two MOSFETs M4 and M5 in common source configuration has been used to enhance the pressure sensitivity by amplifying the output voltage. The piezoresistivity in n-channel MOSFET has been studied for the calculation of carrier mobility variation in the channel region MOSFET under uniformly applied externally pressure. The pressure sensitivity of current mirror readout circuit based pressure sensor enhances from 783 mV/MPa to 1638 mV/MPa by using n-channel MOSFET based differential amplifier circuit.
Dynamic Handover Control Parameters for LTE-A/5G Mobile Communications

Deploying ultra-dense small-cell base stations in the next-generation mobile networks is one of the most expected approaches to overcome the uncertainty of millimeter wave (mm- wave) communications. Mobility management is a critical issue that requires much concern to achieve seamless and highly reliable connection through user mobility. Handover in heterogeneous networks (HetNets) has gained attention, especially when 5G ultradense small cells coexist with the current 4G networks. Handover failure is one of the main issues in mobility, and it can be avoided by adjusting handover control parameters (HCPs): time-to-trigger (TTT) and handover margin (HOM). In this paper, we proposed dynamic HCPs in HetNets (LTE-A and mm-wave networks) with dense small cells. The proposed algorithm was compared with different settings of HCPs for different user mobile speed scenarios. The simulation results show that the proposed algorithm significantly reduces the probability of ping pong handovers and radio link failure, thus improving network performance.
An IoT-Aware Architectural Model for Smart Habitats

Internet of Things (IoT) is a set of massively emerging technologies that aim to make our lives better. One of the most suitable application domains is healthcare and wellbeing, where IoT technologies can enhance living environments (ELE). IoT applications are deployed into an IoT ecosystem, which is supported by a specific architecture. The usual IoT architecture model has three different layers (Acquisition, Network and Application), where all the different IoT elements are organized. This architecture has been designed for general purposes, but the deployment of IoT healthcare applications supported by this architecture model exhibits important drawbacks. In particular, this architecture model does not consider IoT devices coexisting with wearables devices, which hinders the design of smart habitats. In this paper we present an IoT-aware architectural model for smart habitats with special emphasis on healthcare domain. The new architecture adds a fourth layer to the traditional IoT architecture, describing the software artefacts and tips needed to support context-aware habitats through the interaction between wearable computing devices and IoT devices. The main goal of the IoT architecture presented is to enhance living environments for elderly people.
Detecting Overlapping Communities in Knowledge Graphs: A Density Optimization Based Approach

Detecting overlapping communities in knowledge graphs is considered a problem of fundamental importance, since the growing trend of interdisciplinary makes it common for a piece of knowledge belong to different realms. Among all algorithms detecting overlapping communities, Speaker-listener Label Propagation Algorithm (SPLA) represents one of the state-of-the-art approaches due to its high accuracy. However, in the face of overlapping communities with instability and unbalanced partitions, the performance of SLPA drastically degrades. To fill such a gap, we propose a novel community detection algorithm based on density optimization. The proposed algorithm leverages Jaccard similarity coefficient to quantify the similarity between two nodes, and then propagate the label of a node to its neighboring node with the highest similarity. In this way, the initial community is obtained. Due to the existence of an oversized community in the initial community, we argue that a good community structure should have a higher density within the community than outside the community. Therefore, the initial community should be divided again. If the density of the new community is larger than that of the original community, the node's label information is updated; otherwise not. Finally, extensive experiments are carried out on both artificial networks and real networks. The results show that the proposed approach can achieve higher NMI index and overlapping modularity, hence outperforming existing methods.
Development of Generic CNN Deep Learning Method Using Feature Graph

we propose a method by applying Convolutional Neural Networks (CNN) to non-structured data. CNN has been successful in many fields such as image processing and speech recognition. On the other hand, it was difficult to adapt CNN to n non-structured data such as a csv file with multiple variables. The sequence of the data of the low dimensional grid structure such as the image has a meaning, and the CNN recognizes the order as the feature of the image and processes it. Due to this constraint, CNN could not perform feature recognition on non-structured data whose sequence can be reordered while leaving the meaning intact. In this work we developed a method to tackle this issue and make CNN applicable by endowing meaning to the sequence of non-structured data, and demonstrated its effectiveness by adding improvements.
A 0.5 V Low Power DTMOS OTA-C Filter for ECG Sensing Applications

This paper presents a 0.5 V ultra-low power Gm - C fourth order low pass filter with Butterworth response for ECG detection. An Operational Transconductance Amplifier (OTA) forms the basic building block of the filter. In order to achieve high performance at low power, the Gm cell has been designed using Dynamic Threshold MOS (DTMOS) in 180 nm CMOS N-well technology. A hybrid design that uses a DTMOS pseudo differential architecture with Gm reduction enables the OTA to operate at 0.5 V achieving a transconductance as low as 0.25 nS, which cuts down the area of large on-chip capacitance significantly for low frequency applications. The designed filter has a cutoff frequency of 7.3 Hz with a total power consumption of 29.7 nW. The simulated variation of the achieved cutoff frequency over the process corners is less than ± 10 %.
Confidence-Aware Negative Sampling Method for Noisy Knowledge Graph Embedding

Knowledge graph embedding (KGE) can benefit a variety of downstream tasks, such as link prediction and relation extraction, and has therefore quickly gained much attention. However, most conventional embedding models assume that all triple facts share the same confidence without any noise, which is inappropriate. In fact, many noises and conflicts can be brought into a knowledge graph (KG) because of both the automatic construction process and data quality problems. Fortunately, the novel confidence-aware knowledge representation learning (CKRL) framework was proposed, to incorporate triple confidence into translation-based models for KGE. Though effective at detecting noises, with uniform negative sampling methods, and a harsh triple quality function, CKRL could easily cause zero loss problems and false detection issues. To address these problems, we introduce the concept of negative triple confidence and propose a confidence-aware negative sampling method to support the training of CKRL in noisy KGs. We evaluate our model on the knowledge graph completion task. Experimental results demonstrate that the idea of introducing negative triple confidence can greatly facilitate performance improvement in this task, which confirms the capability of our model in noisy knowledge representation learning (NKRL).
The use of ETL and data profiling to integrate data and improve quality in food databases

This paper focuses on integrating food data sources into a central database using extract, transform and load processing and the subsequent data quality enhancement. The obtained data will be transmitted by a food data web service to certain health apps for further use. Furthermore, it is planned to identify inconsistent, incorrect, duplicate and incomplete data using methods of data profiling so that they can be corrected. In order to quantify the data quality purposefully and appropriately, certain quality metrics were used. These metrics were calculated and evaluated using random test data selected from the food data.