Miracle-dz/UniFAR-FTUs huggingface dataset:
A Survey of Intrusion Detection Models based on NSL-KDD Data Set

An Intrusion detection system is a key component of the security management infrastructure. Machine learning advances has benefited many domains including the security domain. Anomaly based Intrusion Detection Systems using machine learning techniques can be trained to detect even unknown attacks. In this paper we conduct a comprehensive review of various researches related to Machine Learning based IDS using the NSL-KDD data set. We propose a generic process flow for anomaly-based IDS and describe this process flow components in the context of related researches carried out. We then point out interesting future research ideas.
Knee Osteoarthritis Severity Level Classification Using Whole Knee Cartilage Damage Index and ANN

In this study, we extended our previous work on analyzing the relationship between cartilage thickness and osteoarthritis (OA) severity grade change. Cartilage thickness is measured by the Cartilage Damage Index (CDI) which includes 60 points marked on 3D MRI for each knee joint. In our previous work, we used CDI points on femur and tibia compartments only (36 points) as features and employed machine learning methods to predict the OA severity grade change. In this work, we added the 24 CDI points on patella into the feature space and explored whether CDI points from patella could improve the accuracy, on a larger dataset. Kellgren-Lawrence (KL) grade was used to measure OA severity in this study. Artificial neural network (ANN), which showed good performance in our previous study, was employed as the machine learning method. For KL grade classification, experiment results showed that adding patella points improved the performance remarkably, from AUC 0.822 to AUC 0.903 and the whole knee CDI achieved the best classification performance on the dataset.
Use of Artificial Intelligence to Analyse Risk in Legal Documents for a Better Decision Support

Assessing risk for voluminous legal documents such as request for proposal, contracts is tedious and error prone. We have developed "risk-o-meter", a framework, based on machine learning and natural language processing to review and assess risks of any legal document. Our framework uses Paragraph Vector, an unsupervised model to generate vector representation of text. This enables the framework to learn contextual relations of legal terms and generate sensible context aware embedding. The framework then feeds the vector space into a supervised classification algorithm to predict whether a paragraph belongs to a pre-defined risk category or not. The framework thus extracts risk prone paragraphs. This technique efficiently overcomes the limitations of keyword based search. We have achieved an accuracy of 91% for the risk category having the largest training dataset. This framework will help organizations optimize effort to identify risk from large document base with minimal human intervention and thus will help to have risk mitigated sustainable growth. Its machine learning capability makes it scalable to uncover relevant information from any type of document apart from legal documents, provided the library is pre-populated and rich.
Recommendation of Web Service Selection Algorithm Based on Web Application Review

As the number of web services has increased, the number of services with the same functions has steadily risen. Consequently, many QoS (Quality of Service)-based service selection algorithms have been proposed to determine which service should be selected among those with the same functions. The prediction accuracy of the QoS attribute depends on the selection algorithm. In addition, the appropriate selection algorithm depends on the type of domain. For example, a stock notification service, which relies on real-time transactions, should employ a selection algorithm with a highly accurate response time. However, an automated method to recommend the optimal service selection algorithm has yet to be established. In this paper, we propose a method to recommend selection algorithms.
A Process Model to Develop Educational Applications for Children with Dyslexia

Nowadays, the massive dissemination of technologies for the education at elementary schools is transforming the learning environments, for example the educational applications for dyslexia are easy to use notably in mobile devices beyond the classroom walls. However, the development of this technology has not been explicit in order to help reading the children with dyslexia. The development of this educational application is considering as a challenge because it is necessary to consider the different perspectives of stakeholders since the early phases of development process. This paper proposes as solution a software process model to develop educational applications considering the learning needs of children with dyslexia; it involves a user-centered approach since the participation of several actors are considered such as the teacher, the student, the psychologist and the software engineer. A case study has been conducted in order the present the performance of proposed model in particular for usability and accessibility factors.
An RF-to-DC Rectifier With High Efficiency Over Wide Input Power Range for RF Energy Harvesting Applications

This paper presents, a wide input range, 4-stage threshold voltage compensated RF-to-DC power converter, designed to efficiently convert RF signals to dc voltages by applying an optimum compensation voltage produced by subthreshold auxiliary transistors. The proposed optimally compensated rectifiers can achieve higher efficiency over a wider input power range compared to other threshold voltage compensation circuits where the level of the compensation is limited by the circuit structure and varies with input power. The designed rectifier is implemented in three possible ways. This proposed compensation technique can be applied to a rectifier chain with a relatively low number of stages. Designed and implemented in a 130 nm CMOS technology, the proposed rectifier exhibits a measured PCE of above 20% over the 8.5-dB input power range while driving a 1-<inline-formula> <tex-math notation="LaTeX">$\text{M}\Omega $ </tex-math></inline-formula> load resistor at 896-MHz. For the same load and utilizing a minimal number of compensated rectifier stages, the proposed circuit exhibits a maximum PCE of 43% at −11 dBm for single-ended Dickson-based CMOS rectifiers. The proposed circuit demonstrates a −20.5 dBm sensitivity for 1 V output across a 1-<inline-formula> <tex-math notation="LaTeX">$\text{M}\Omega $ </tex-math></inline-formula> resistive load.
Capacity of 3D MANETs Under Packet Redundancy and Receiver Probing

There are many articles addressed capacity in mobile ad hoc networks(MANETs), most of these works explored capacity in two-dimensional environment (2D MANETs), it is a carrier for 3D MANETs developing. We address the capacity of 3D MANETs in this paper, furthermore, this paper enhance our previous work related to capacity of 3D MANETs. In our previous work, the sender selects randomly receiver once in each time-slot, if the selected receiver has carrying a copy of the sending packet, the sender will keep idle in this time-slot, so that the precious bandwidth is wasted. To this end, we adopt receiver probing in this paper, for each time-slot, the sender has $\tau$ round chances to select a receiver. As a result, it raises the transmission opportunity under both source-to-relay transmission and relay-to-destination transmission. We develop a Markov chain theoretical model to characterise the packet transmission process, with the help of the theoretical model, we drive the closed-form expression for throughput capacity.
Design on the Waveform for QAM-FBMC System in the Presence of Residual CFO

In this paper, we propose a method for designing an optimal filter for QAM-FBMC system in the presence of the residual CFO. We model the QAM-FBMC system with residual CFO, and describe the optimization conditions for the prototype filter design. By using these criteria, we formulate the prototype filter design problem for the QAM-FBMC system with the residual CFO, and present the designed filter coefficients. Finally, simulation results show the performance comparison of the proposed prototype filters, and we confirm that the designed filters have the performances of the design intents.
A Robust System for Visual Pattern Recognition in Engineering Drawing Documents

In AEC (Architectural, Engineering and Constructional) domain, the architects use several types of drawing documents. These documents consist of various category of graphical objects and such objects can be classified based on their shape and curvature properties. Here, we have proposed a query enabled image searching system which can be used to locate objects in a set of documents. To achieve our goal, at-first we have identified a key point search window based on the inflection points of the contour of the object. Then, for each window we try to classify the curve segment into positive or negative class based on the curvature values. Then depending on the predicted class, we get the maxima or minima point on the contour. Now, we use these points as seed values (initial key points) to our feature detection mechanism. To extract the shape and/or curvature properties of the objects at different levels of scale and orientation, we have employed two feature extraction mechanisms. To get the local feature descriptor, we have introduced a neighborhood-based curvature extraction methodology and to consider the global shape of the object, Hu moments based shape descriptor of the object contour is computed. Finally, these two features are normalized, and their combined feature vector is used to compare and measure the similarity between the key points of two objects. We have also introduced a mechanism to identify the query object as a whole, when it consists of multiple overlapping contours for a single object. We have tested our system on a variety of AEC class of drawing documents and the results are quite encouraging. The proposed system will enable the users to locate similar objects inside a project consisting of hundreds of documents. Also, the method can be used for object hyperlinking with some alterations.
Nonlinear Output Feedback for Autonomous U-turn Maneuvers of a Robot in Orchard Headlands

This paper is devoted to the navigation of a robot in orchard headlands using embedded sensors such as lasers, Lidars or cameras. The main idea is to consider a differential robot model directly in polar coordinates and not in Cartesian coordinates which makes it possible to obtain simpler expressions of the outputs. Then two nonlinear output state feedback controllers are proposed to track two shapes based on spirals allowing to go from one row of fruit trees to another. These controllers are based on an input to output linearization and proved to be very efficient on simulations.
Heterogeneous Map Merging: State of the Art

Multi-robot mapping and environment modeling have several advantages that makeit an attractive alternative to the mapping with a single robot: faster exploration, higherfault tolerance, richer data due to different sensors being used by different systems. However,the environment modeling with several robotic systems operating in the same area causes problemsof higher-order—acquired knowledge fusion and synchronization over time, revealing the sameenvironment properties using different sensors with different technical specifications. While theexisting robot map and environment model merging techniques allow merging certain homogeneousmaps, the possibility to use sensors of different physical nature and different mapping algorithms islimited. The resulting maps from robots with different specifications are heterogeneous, and eventhough some research on how to merge fundamentally different maps exists, it is limited to specificapplications. This research reviews the state of the art in homogeneous and heterogeneous mapmerging and illustrates the main research challenges in the area. Six factors are identified thatinfluence the outcome of map merging: (1) robotic platform hardware configurations, (2) maprepresentation types, (3) mapping algorithms, (4) shared information between robots, (5) relativepositioning information, (6) resulting global maps.
Analysis of Linux Kernel Packet Processing on Manycore Systems

Due to increased demand for high scalability and programmability in recent data centers, there has been a paradigm shift from traditional hardware-based network functions to software defined network functions. While implementing network functions in software offers great flexibility, it also reveals significant overheads and performance issues associated with the network stack in current operating systems. Since manycore processors and advanced network technology can provide significant performance improvements for applications such as router workloads that are responsive to parallelism, there is a need to optimize the packet processing performance of current operating systems. The network stack in the operating systems suffer from a number of overheads associated with core mechanisms of the kernel such as task scheduling, memory management, process synchronization, interrupt handling, etc. Each of these overheads snowballs into severe performance penalty of software network functions as each packet traverses its path through the network stack in the kernel. In this paper, we provide an analysis of the packet processing capability of manycore systems, running general-purpose operating system (OS). We identify the major performance bottlenecks in the packet processing path of Linux kernel network stack and provide an insight into various performance and scalability issues that need to be addressed.
An Interest Control Method in SVC-based Adaptive Streaming over Named Data Networking

In adaptive streaming, consumers set video segment quality levels based on the estimated throughput. However, in named data networking (NDN), the consumer ’s throughput estimation is not always accurate due to cache segments. As a result, in situations where bottlenecks exist between the consumer and the router, requested -higher quality segments arrive at the consumer after their designated playback time, which means the downloaded data cannot be used for playback. In this paper, we propose an interest packet control method to reduce unused data packets in scalable video coding (SVC)-based adaptive streaming in NDN. In our proposed method, when receiving an interest packet, the router determines if the requested data packet can be expected to arrive at the consumer in time for playback. If the router determines that the data requested will not arrive in time, it discards the interest packet. Through computer simulation-based performance evaluations, we show that our proposed method reduces the transmission of unused data packets.
A Lightweight and Secure IoT Remote Monitoring Mechanism Using DNS with Privacy Preservation

IoT remote control is faced with scalability, secure communication and privacy preservation issues and conventional solutions (HTTPS) have disclosed poor scaling problem and privacy concerns. In this paper, we propose a novel lightweight and secure IoT remote monitoring mechanism using DNS with privacy preservation. Basically, the communication between IoT devices and gateways uses the conventional protocols as usual such as CoAP and MQTT while only the remote monitoring uses DNS protocol. That is, encrypted IoT data, after being encoded with base64, is stored as a DNS TXT record of the domain name of the IoT device and only the designated users are allowed to query and decrypt the data based on TSIG authentication of DNS protocol and asymmetric cryptography. We implemented a prototype system over name-bound virtual networks (NBVNs) in which all virtual nodes are registered in DNS automatically and the network traffic is restricted within each NBVN. Through the preliminary evaluations we confirmed the effectiveness of secure communication and privacy preservation in IoT remote monitoring in the proposed mechanism.
Real-time Tide Prediction Based on An Hybrid HA-WANN Model Using Wind Information

Observed tidal level is a kind of signals in essence. The fluctuations of tide affect the maritime activities and human lives in offshore and coastal zone, including the waterfront distances in shallow waters, islands, and the lands with gentle slope, especially in locations having shallow water depths. Harmonic analysis is traditionally employed for long-term tide predictions, but it becomes unreliable if non-periodic meteorological events predominate. In addition of other signal processing methods, this study proposed a hybrid model for hourly tidal level forecasting by combining ANN with wavelet decomposition using SLA and wind shear velocity components as inputs. Six mother wavelets and nine data sets are computed in HA-WANN model in order to acquire the best models, and CE, r2, MAE, RMSE and MSRE indexes are used to measure the superiority of all models. Results obtained from this study indicate that the conjunction of wavelet decomposition and artificial intelligence models can definitely improve the accuracy of tidal level prediction, and the usage of SLA and wind shear velocity can yield better capability than HA and original ANN models.
Secrecy Performance of the UAV Enabled Cognitive Relay Network

Unmanned aerial vehicles (UAVs) have been recently applied to improve physical-layer security in wireless communication networks. This work considers a cognitive relay network where UAV is used as a mobile relay to bridge the communication from a secondary transmitter to a secondary receiver. Our purpose is to maximize the secrecy rate under certain UAV trajectory and power constraints by designing a joint optimization algorithm. Solving the optimization problem is computationally challenging as the utility functions are non-concave and constraints are non-convex. Thus, an efficient iterative convex approximation algorithm based on a successive optimization approach is introduced to address the proposed non-convex optimization problems. The computer simulations show that our algorithm can achieve a rapid coverage rate and the UAV trajectory is optimized successfully. Results in this study demonstrate that the proposed design outperforms conventional schemes.
Towards the operational implementation of the Fire Weather Index FWI based on the High-Resolution WRF Model

Los incendios de vegetación son un problema importante en la Argentina y su control depende fuertemente de la posibilidad de tener los recursos necesarios preparados para ser usados en el momento adecuado. Para ello, la generación de índices de peligro de incendios es fundamental. En anteriores trabajos, los autores presentaron la implementación del índice australiano FFDI (Forest Fire Danger Index) para el Cono Sur, en base a un modelo de predicción meteorológica de 16 km de resolución espacial. En la presente contribución presentamos el diseno de una plataforma para la generación de un pronóstico de peligro de incendios basado en el índice canadiense FWI (Fire Weather Index), que es el que emplea el Sistema Nacional de Manejo del Fuego de Argentina. A través de esta plataforma se estima el FWI automaticamente, basado en el pronostico de 24 y 48 hs del Modelo de Pronóstico del tiempo (WRF), corrido a 4 km de resolución espacial para la región centro de Argentina. Para la cálculo del FWI se utilizan los valores de la temperatura, viento, humedad relativa y precipitación de las últimas 24 hs. Aunque el sistema está en fase experimental, los primeros resultados mostraron buena capacidad del producto FWI para identificar áreas con mayor probabilidad de ocurrencia de incendios. El sistema incluirá una componente de visualización, ya que exportará de manera automáticas sus salidas raster a páginas Web (en formatos PNG) y a servidores de mapas (en formato geotiff) existentes en las instituciones participantes del proyecto, desde donde se podrán descargar los productos.
Hardware Efficient Approximate Adder Design

This paper presents a new approximate adder architecture which when implemented on an FPGA consumes fewer logic resources compared to accurate adders of similar size and can achieve higher or comparable operating frequencies. For 32-bit addition, our approximate adder achieves a 25% reduction in the number of LUTs utilized compared to the accurate adder with no compromise on the speed performance. For 64-bit addition, our approximate adder achieves a 24% improvement in the maximum operating frequency, and a 25% reduction in the number of LUTs utilized compared to the accurate adder (post place and route on a Virtex-7 FPGA device). We also make comparisons with the FPGA-based implementations of some well-known gate-level approximate adders, and further provide insights into the error characteristics showing that the proposed approximate adder has a reduced error range.
Texture Recognition Using a Novel Input Layer for Deep Convolutional Neural Network

We introduce a new type of layer as input to the convolutional neural network. The layer is designed to capture pixel to pixel relational properties that are found in textures. These pixel relational properties are not easily captured in a conventional convolutional neural network (CNN) with RGB (red-green-blue) layer input. This approach uses the CNN architecture with a pre-input processing to capture the pixel relational properties such as the correlation between the center and surrounding pixels. We tested our approach against multiple texture datasets such as Colored Brodatz, Multiband Texture and Wood texture. The results are competitive against current best results, indicating that this approach is promising approach.
Hardware-Based Model of Node Clustering Using Q-Learning for Wireless Sensor Networks

Recent researches on the development and deployment of large-scale wireless sensor networks focus on the reduction of power consumption to increase network lifetime. This entails the development of more efficient hardware and the integration of the multiple components into a single platform; possibly, a system-on-a-chip sensor node platform. This paper focuses on the application of Q-learning to node clustering as an effective technique in reducing power consumption. A hardware implementation of CLIQUE, a Q-learning-based clustering algorithm that enhances the operation and resiliency of the network and improves the energy expenditure of the sensor nodes, is presented. The power consumption of the hardware is recorded to be around 6.4 mW. By incorporating this value into OMNeT++, it was demonstrated that CLIQUE improves energy expenditure by 6%-19% and extends network lifetime by 5%-22%, when compared to LEACH, a traditional clustering algorithm.
Human Gait Modelling Using Hidden Markov Model For Abnormality Detection

This paper presents a novel approach to human gait analysis using wearable Inertial Measurement Unit(IMU) sensor-based technique. The proposed system emphasizes on detection of certain abnormal gait patterns. It includes hemiplegic and equinus gait which are synthetically generated in our lab. The designed prototype contains an IMU sensor with 3 axial accelerometer and gyroscope. It provides linear acceleration and angular velocity of human foot. A probabilistic framework, Hidden Markov Model(HMM) is applied to model bipedal human gait. This model uses Symbolic Aggregate Approximation(SAX) method for generating observation sequences obtained from sample gait cycles.The detection of abnormal gait pattern is based on maximum log-likelihood of an unknown observerd sequence, generated from a gait cycle. The experimental results demonstarte that the proposed HMM-based technique is able to detect gait abnormality in gait data. The proposed personalized gait modelling approach is cost effective and reliable to implement in gait rehabilatation process.