Miracle-dz/UniFAR-FTUs huggingface dataset:
KarstMod User Guide - version 2.2

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. KarstMod User Guide version 2.2 Naomi Mazzilli, Dominique Bertin
PRNG Implementation Based on Chaotic Neural Network (CNN)

In this work, a neural network with chaos activation function has been applied as a pseudo-random number generator (PRNG). Chaotic neural network (CNN) is used because of its noise like behaviour which is important for cryptanalyst to know about the hidden information as it is hard to predict. A suitable adaptive architecture was adopted to generate a binary number and the result was tested for randomness using National Institute of Standard Technology (NIST) randomness tests. Although the applications of CNN in cryptography have less effective than traditional implementations, this is because these systems need large numbers of digital logic or even a computer system. This work will focus on applications that can use the proposed system in an efficient way that minimize the system complexity.
The application of blockchain for halal product assurance: A systematic review of the current developments and future directions

The demand for halal product assurance has increased recently at the global level due to the increase in users’ awareness as well as the Muslim population. Consequently, the demand for halal certification service by the product manufacturers is also increased. However, the conventional halal certification model is unable to support the increasing demand for certification services in which it does not support the halal industry efficiently that makes the certification process costly. Technological support, particularly blockchain, can facilitate and simplify the halal certification process, which leads to an automated and more efficient process. Blockchain for halal product assurance is considered a new multidisciplinary field; hence, there is a need to gather and synthesize the current information about the field of study in a thorough and unbiased manner so that a more general conclusion can be drawn as well as providing preliminary information to encourage further research in the field. Therefore, a systematic review was conducted to identify, evaluate, and interpret research and developments relevant to the implementation of blockchain technology for halal product assurance. The result is expected to cultivate new studies that can improve the entire process for halal product assurance.
Evaluation of emergency evacuation in school buildings_ Protocol for a Systematic Review

Children are amongst the most vulnerable affected groups by natural and human-made disasters. Disaster preparedness education programs have been developed to help reduce risk and increase resilience for hazardous events. A better understanding is needed about children evacuation behaviour in schools and the time of evacuation. Therefore, a systematic review is proposed to search for relevant information about emergency evacuation response in schools. This systematic review protocol was developed to present adequate guidelines that can provide relevant research results to fulfil the sought objective. Sixteen databases will be accessed (Scopus, Web of Science, and ScienceDirect, are some examples) and a total of three keyword expressions will be used. The selection process will be thoroughly described, including detailed data treatment and used eligibility criteria, to contribute to the general research on this field.
SearcHD: A Memory-Centric Hyperdimensional Computing With Stochastic Training

Brain-inspired hyperdimensional (HD) computing emulates cognitive tasks by computing with long binary vectors—also know as hypervectors—as opposed to computing with numbers. However, we observed that in order to provide acceptable classification accuracy on practical applications, HD algorithms need to be trained and tested on nonbinary hypervectors. In this article, we propose SearcHD, a fully binarized HD computing algorithm with a fully binary training. SearcHD maps every data points to a high-dimensional space with binary elements. Instead of training an HD model with nonbinary elements, SearcHD implements a full binary training method which generates multiple binary hypervectors for each class. We also use the analog characteristic of nonvolatile memories (NVMs) to perform all encoding, training, and inference computations in memory. We evaluate the efficiency and accuracy of SearcHD on a wide range of classification applications. Our evaluation shows that SearcHD can provide on average <inline-formula> <tex-math notation="LaTeX">$31.1\times $ </tex-math></inline-formula> higher energy efficiency and <inline-formula> <tex-math notation="LaTeX">$12.8\times $ </tex-math></inline-formula> faster training as compared to the state-of-the-art HD computing algorithms.
External Force Observer for Small- and Medium-Sized Humanoid Robots

External force observer for humanoid robots has been widely studied in the literature. However, most of the proposed approaches generally rely on information from six-axis force/torque sensors, which the small or medium-sized humanoid robots usually do not have. As a result, those approaches cannot be applied to this category of humanoid robots, which are widely used nowadays in education or research. In this paper, we propose a Kalman filter-based observer to estimate the three components of an external force applied in any direction and at an arbitrary point of the robot’s structure. The observer is simple to implement and can easily run in real time using the embedded processor of a small or medium-sized humanoid robot such as Nao or Darwin-OP. Moreover, the observer does not require any changes to the robot’s hardware, as it only uses measurements from the available force-sensing resistors (FSR) inserted under the feet of the humanoid robot and from the robot’s inertial measurement unit (IMU). The proposed observer was extensively validated on a Nao humanoid robot in both cases of standing still or walking while an external force was applied to the robot. In the conducted experiments, the observer successfully estimated the external force within a reasonable margin of error. Moreover, the experimental data and the MATLAB and C++/ROS implementations of the proposed observer are available as an open source package. https://goo.gl/VkhejY.
Minimum Cost Reconfigurable Network Template Design With Guaranteed QoS

Conventional networks are based on layered protocols with intensive cross-layer interactions and complex signal processing at every node, making it difficult to meet the ultra-low latency requirement of mission critical applications in future communication systems. In this paper, we address this issue by proposing the concept of network template, which allows data to flow through it at the transmission symbol level, with minimal node processing. This is achieved by carefully calibrating the inter-connecting links among the nodes and pre-calculating the routing/network coding actions for each node, according to a set of preconfigured flows. In this paper, we focus on the minimum cost network template design to minimize the connections within the template, while ensuring that all the pre-defined configurations are feasible with the guaranteed throughput, latency and reliability. We show that the minimum cost network template design problem is difficult to solve optimally in general. We thus propose an efficient greedy algorithm to find a close-to-optimal solution. Simulation results show that the construction cost of the templates obtained by the proposed algorithm is very close to a lower bound. Furthermore, the construction cost increases only slightly with the number of pre-defined configurations, which confirms the flexibility of the network template design.
LITERATURE SURVEY ON MACHINE LEARNING BASED TECHNIQUES IN MEDICAL DATAANALYSIS

Machine Learning plays a significant role among the areas of Artificial Intelligence (AI). During recent years, Machine Learning (ML) has been attracting many researchers, and it has been successfully applied in many fields such as medical, education, forecasting etc., Right now, the diagnosis of diseases is mostly from expert's decision. Diagnosis is a major task in clinical science as it is crucial in determining if a patient is having the disease or not. This in turn decides the suitable path of treatment for disease diagnosis. Applying machine learning techniques for disease diagnosis using intelligent algorithms has been a hot research area of computer science. This paper throws a light on the comprehensive survey on the machine learning applications in the medical disease prognosis during the past decades
Glasses-Free 3-D and Augmented Reality Display Advances: From Theory to Implementation

Four of the most viable glasses-free three-dimensional display methods were chosen for our research at NTU's Advanced Displays Laboratory. Our multilayer light field display enables a smaller form factor. Faster algorithms combined with horizontal parallax-only speed-up Tensor Display computation time. A spin-off from this is an augmented reality light field near-eye display with focus cues. Our head-tracked displays use a directional backlit liquid crystal display panel to produce multiple sets of exit pupil pairs that employ a head tracker to follow the users’ eyes. This enables a ‘plug and play solution’ where existing content can be used without resolution loss. We have developed three different spatio-temporally multiplexed super multiview display systems based on liquid crystal displays, organic light-emitting diodes and fast projectors. In this article, the background, status of the prototypes and their performance are explained, and the future direction of our research is described.
A 102.2-dB, 181.1-dB FoM Extended Counting Analog-to-Digital Converter With Capacitor Scaling

Sensor interfaces demand ever-increasing energy efficiency from analog-to-digital converters (ADCs). For sensors requiring a wide dynamic range (DR) (>12 bit), the incremental ADC (IADC) is becoming a popular choice. In this article, the design of an energy-efficient IADC with a 16-bit signal-to-noise and distortion ratio (SNDR) and 17-bit DR is described. The IADC implements a novel capacitor scaling technique, which reduces power consumption in the critical first operational transconductance amplifier (OTA) by up to 37%. The new technique can be combined with the extended counting (EC) scheme utilizing an in-loop 4-bit asynchronous successive approximation register (SAR) (ASAR) ADC. Using a three-phase operation, this IADC architecture further reduces the power while enabling wider signal bandwidth with a much lower oversampling ratio (OSR). Silicon measurements in a 0.18-<inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula> m CMOS process show the IADC achieved a DR of 102.2 dB, within a bandwidth of 2.04 kHz, with power consumption of about <inline-formula> <tex-math notation="LaTeX">$25~\mu $ </tex-math></inline-formula> W, leading to a state-of-the-art Schreier figure-of-merit (FoM) of 181.1 dB.
Summaration Generating Timeline for Evolutionary Tweet Streams and Categorizing Tweets

: Now a day's social networking sites are the fastest medium which delivers news to the user as compared to the newspaper and television. There so many social networking sites are present and one of them is Twitter. People run-out of time and even then, they find a small time to know what’s happening all over the world. So, in such hefty schedule people can’t go through all the tweets which people post all day long and the model proposed is summarizing the tweets and generating timeline for evolutionary tweet streams. It consists three components, first tweet stream clustering for clustering tweets using Bisect k-means cluster algorithm, so that the tweets which are taken from the twitter Application Programming Interface (API) are pre-processed and then cluster. Second component tweet summarization cluster vector technique for generating rank summarization using LexRank algorithm, third component is to categorize tweets into positive, negative and neutral tweets using sentiment analysis where tweets are collected by using hash tags (screen names) of twitter individual account so that it will display the positive, negative ,neutral tweets of that individual account. Doing categorization tweets for the particular user then it will display the positive tweets percentage:22.2%, negative tweets percentage 11.11%, neutral tweets of 66.6% and the results are plotted on bar and pie chat.
Research on Communication Network Structure Mining Based on Spectrum Monitoring Data

The physical characteristics of the massive spectrum signals carrying the communication information and the statistical laws of these characteristics also potentially reflect the communication behavior of the communication individuals and the intelligence information related to the communication behavior. Intercepting and cracking signal content usually faces enormous difficulties and costs, and more often, we are not able to crack the encrypted signal content. However, by studying the physical features extracted from the spectrum monitoring signals and the statistical laws of these features, it is also possible to dig out the hidden relationships between communication individuals and even the communication network structure, so as to analyze the communication behaviors of the communication individuals. Based on the characteristics of carrier frequency, bandwidth, power, signal monitoring time and direction information of spectrum monitoring signals, this paper identifies each spectrum signal and studies the distribution characteristics and statistical laws of massive spectrum monitoring signals in the column coordinate system. Due to the clustering of the spectrum signals generated by the sources in the power, monitoring time and direction, and the correlation of the spectrum signals generated by the two parties in the communication process, based on the improved density clustering algorithm, this paper proposes a method for mining the communication relationship between communication individuals from the spectrum monitoring data, and guesses and constructs the communication network structure by matching the communication individual with the communication relationship. Finally, we analyze the communication network structure mined from the spectrum monitoring data.
Visibility Enhancement during Severe Weather—Object Tracking with Bounding Box

: A large number of vehicle accidents are caused by low visibility during severe weather conditions. According to the US Department of Transportation, there are more than 1,259,000 crashes each year caused by severe weather conditions. These accidents could be significantly prevented if real time visibility enhancement systems were to exist. This work is aimed to improve driver safety during severe weather conditions. We propose a method to improve driver’s visibility during harsh weather conditions: a multistage framework based on Retinex algorithm and followed by a detection process of the vehicles in the front view of the driver. Retinex is an algorithm that has a capability to maintain a good dynamic range compression and spectral rendition effect. Retinex is used because it has the ability to overcome the loss of background details which occur during severe weather conditions scenes. We present simulation results of different visibility degradation. We also evaluate our method by using peak signal to noise ratio (PSNR) and Structural SIMilarity (SSIM) parameters.
Intelligent Chatbot Model to Enhance the Emotion Detection in social media using Bi-directional Recurrent Neural Network

A Chatbot is like a digital assistant, can be pretrained or have self learning capability. It can be trained to understand the user queries and respond in natural language of the user during a conversation. Chatbot can actively help human to involve in a digital conversation since it can make use of the natural language processing. The existing Chatbot enhances the communication but it does not improvise the information when it is needed. And sometimes they go beyond the topic during conversation which is a major drawback. The proposed bot could identify the emotional status of the users and can provide suggestions using Bi-directional Recurrent Neural Network and tensor flow library functions. The popularity of the chat bots lets our system to be used for much variety of applications. These chat bots be used to automate the various fields like e-learning, e-government and web based models.
Accurate Indoor Localization Assisted With Optimizing Array Orientations and Receiver Positions

Owing to the multiple antennas equipped at modern Wi-Fi infrastructures, the angle-of-arrival (AoA) based indoor localization systems have successfully achieved the accuracy of tens of centimeters. However, the high accuracy is acquired at the cost of employing the additional resources in the domains of frequency, space or time, which requires complex processing and hinders the practical application. In this paper, we present the design and implementation of RcLoc, which takes full advantages of the flexible array orientations and receiver positions, based on limited resources. Particularly, RcLoc devises a receiver configuration scheme for guiding the system deployment. Optimized array orientation could effectively improve the AoA estimation accuracy and well-designed receiver positions contribute to the Cramer-Rao lower bound of localization error. In the stage of system realization, we further devise an array calibration method to calibrate the actual array and develop an improved AoA estimation algorithm, which make RcLoc robust to the array arrangement. We prototype RcLoc on commodity Wi-Fi devices without manual intervention. Comprehensive experiments in a multipath-rich indoor environment show that RcLoc achieves a median localization accuracy of 0.4 m, which provides useful insights for receiver deployment.
Similarity Based Large Scale Malware Analysis: Techniques and Implications

vii CHAPTER 1: INTRODUCTION 1 1.1 Background 1 1.2 Malware Analysis 2 1.2.1 Desktop Malware Analysis 3 1.2.2 Android Malware Analysis 5 1.2.3 Machine Learning Based Malware Analysis 7 1.3 Objectives 9 1.4 Contributions 11 1.4. 1 Accurate and Efficient Binary Similarity Analysis Tool 11 1.4. 2 Specialized Android Malware Clustering System 11 1.4. 3 Practical Implications of ML Based Android Malware Detection 12 1.5 Organization of the Dissertation 12 CHAPTER 2: ACCURATE AND EFFICIENT BINARY SIMILARITY ANALYSIS 14 2.1 Approach Overview 16 2.1.1 Basic Block Type Abstraction 16 2.1.2 Blended n-gram Graphical Features 18 2.1.3 Graph Signature Format and Comparison 19 2.1.4 Fuzzy Hash Generation and Comparison 21 2.2 Evaluation of CFG Similarity Analysis Algorithms 22 2.2.1 Existing CFG Similarity Comparison Algorithms 23 2.2.2 CFG Comparison Algorithm Evaluation Strategy 24 2.2.3 Experiment Data Preparation 26 2.2. 4 Evaluation Results 27 2.2. 5 Overall Performance 30 2.3 Evaluation of Binary Similarity Analysis Tools 31 2.3. 1 Binary Graph Signature Accuracy 32 2.3. 2 Graphical Fuzzy Hash Accuracy 33 2.3. 3 Similarity Tool Output Consistency 34 2.3. 4 Similarity Tool Accuracy Analysis 35 2.3. 5 Malware Detection Analysis 37 2.3. 6 Obfuscation State Analysis 39
Development of Web-Based Application for Shapefile Coordinate System Conversion for Malaysia

: It is difficult to process GIS vector data when they are not aligned with one another. The need for different coordinate systems rose from the fact that some coordinate systems are better fitted to describe the geographical phenomenon occurring in a specific area. However, even commercial software had been proven to have questionable accuracy in coordinate system conversions. The purpose of this study is to develop a web application capable of converting the coordinate system of a GIS data format such as a shapefile for Peninsular Malaysia. The web application named Coordinate Conversion Application (CCA v1.1) was developed using Django 2.0 with Python 3.6 and is capable of five coordinate transformations namely WGS84 to GDM2000 (forward and backward), WGS84 to MRSO (old) (forward only), MRSO (old) to Cassini (old) (forward and backward). Results obtained were compared with existing software such as GDTS v4.01 and ArcGIS 10.3, and analysis shows that CCA v1.1 has achieved satisfactory accuracy.
Design Principles for Business Model Analytics Tools

In this doctoral thesis, a current and widespread approach to business modelling is developed further using findings from data analytics and theories to improve user understanding. The focus is on the goals of an objective business modeling that is comprehensible to all users. Business modelling is an important part of companies to determine their strategy and as a linking element between the strategic and operational level. Different research areas investigate how such business modelling can be supported or applied in different situations (e.g. after disruptive shocks). However, the starting point for these approaches is always a subjective top-down approach, based on the knowledge of the modeler. This involves the challenge that the created business models are incorrect and depend on the knowledge and perspective of the modeler. In addition, they are strongly linked to the strategic level and the abstract management view associated with it. In a design science research approach in this thesis, requirements and design principles for objective and easily understandable business modeling were developed. In addition, these principles were instantiated in a tool that can (semi-)automatically create a business model by using enterprise data. At the same time, the understanding of all users is increased by integrating the relationships between the elements, the value flow and an extension of value capturing. This provides the foundation for a common communication platform, similar to a blueprint in the construction industry. The result of this work is not only the tool but also an extension of the current state of the art of research in the field of business modeling, as well as corresponding principles and findings, which can be used to master individual challenges in practice and science.
SOLVING OF THE INVERSION PROBLEM OF THE METHOD MULTICHANNEL ANALYSIS OF SURFACE WAVES BASED ON THE ARTIFICIAL NEURAL NETWORK

The study is devoted to improving the efficiency of the method of multichannel analysis of surface waves due to the implementation of a new algorithm for the selection of 1D-models of the shear wave velocity by inverse dispersion curves based on machine learning. It is proposed to use a multilayer fully-connected artificial neural network (ANN) for the estimate of horizontally layered models composed of interpolated velocity values on a uniform grid with an arbitrary step. Various types of correlation dependence of the parameters of the velocity model on dispersion curves were investigated. The optimal type of the objective function was determined for ANN learning on the basis of these studies. The advantages of using a trained ANN for the problem of inversion dispersion curves are: the ability to estimate layered models with sufficient accuracy for their use at subsequent stages of seismic data processing, resistance to noise, no significant requirements for computational resources and the absence of the need for additional adjustment of parameters. The tests of the trained ANN, with the architecture proposed in this study, show high accuracy in validation (about 96%), which indicates the success of training and the actually perspective of using neural networks for inversion problems.