Miracle-dz/UniFAR-FTUs huggingface dataset:
Resource Based Theory of Accelerator Programme

Accelerators help accelerate startups through formal education and mentorship connections during intensive, temporally-compressed programmes for a period of three -six months.This paper investigate how Resource Based Theory (RBT) posits accelerators for sustained competitive advantage of startups. Our research question was to determine the value (in terms of resources offered) that accelerators bring to benefit startups that have been through the acceleration programmes. Our focus was to uncover what value gets created by accelerator programme and how. Through this qualitative study we selected a multiple case study method of 5 accelerator programmes in South Africa. For analysis we linked the RBT (tangible and intangible resources) for each accelerator’s entrepreneurial ecosystem, to determine if the phenomenon of the burgeoning accelerator industry can be better understood. Document reviews were explored to determine intended impact that sustain success for startup (technology) companies. Findings revealed that accelerators ensure that fewer startups fail and more SME’s become sustainable into the future. Secondly accelerators help startup companies define and build their initial products. Thirdly upscale is achieved through, identification of promising customer segments. Finally mentorship and accelerator process enhanced and securing resources is strengthening necessary resources available to startups.
Development of web-based surveillance system for Internet of Things (IoT) application

With the advancement of wireless technology, our dependency on smart system has increases to a higher level than before. Without doubt, integration between different technologies becomes inevitable in order create affordable surveillance system. This paper presents the development of with web-based surveillance system with a dedicated Android-based mobile application using a Raspberry Pi and its supporting components i.e., Pi-Camera, PIR motion sensor, Ultrasonic sensor, web-based mobile application. The designed system also utilizes Node-Red development tools as the platform to integrate all components of the system, MQTT as the communication protocol for data acquisition and ThingSpeak as the middleware. The proposed system can be implemented over the internet using any computer and mobile devices, at anywhere and anytime. The system can automatically stream live video viewed from the Android mobile application and the Raspberry Pi device can send an alert notification to users via email and SMS. The system can be one possible features in smart home system and is considered as an affordable solution, customizable and easy to implement in comparison with other commercial surveillance system products such as CCTV or IP Camera.
Unsupervised Machine Learning in Fractography: Evaluation and Interpretation

Modern computer vision and machine learning techniques, when applied in Fractogra- phy bare the potential to automate much of the failure analysis process and remove human induced ambiguity or bias. Given the complex interaction between intrinsic (e.g. microstructure) and extrinsic (e.g. environment, loading history) factors leading to failure, deep learning methods, which exhibit very high efficiency in establishing complex interconnections between the input data, may end up revealing new correla- tions and information that is encoded onto the complex geometries of fracture surfaces and remained hidden from us so far. In this work, we examine the potential use of an unsupervised learning pipeline to classify fracture surfaces of five tungsten heavy alloys following their chemical content (i.e. Tungsten percentage). Encouraged by the success of the algorithms, we move on and analyze the features on the fracture surfaces which are governing the decision process of the algorithms. The fractographic interpretation of these features shows that the extent of plasticity on the fracture surface serves as a measure for the classification process. The examined pipeline can be used to identify failures originating from erroneous manufacturing processes, leading to locally varying Tungsten concentrations and ultimately premature failure.
The Determination of Lightning Disaster Hazard Index Using Analytical Hierarchy Process

In order to minimize the losses caused by lightning, a lightning disaster management system is compulsory. It should provide lightning disaster information in an area to get the risk values of lightning strikes. One way is to establish a lightning disaster risk map. The disaster risk map consists of hazard map, vulnerability map and capacity map. However, lightning yet has not been included in the regulation guiding the establishment of risk map for several disasters in Indonesia. This study is conducted to develop a process to determine general hazard index for lightning disaster.
An Exploration of Factors that Motivate Human Rights Workers in Areas of Armed Conflict in the Philippines

This study examined the experiences and motivation of human rights workers (HRWs) in areas affected by armed conflict in the Philippines. Six human rights workers from Karapatan responded to semi-structured interviews. Karapatan is a Philippine NGO whose mission is to uphold human rights and document instances of human rights violations. The results described the risks experienced by human rights workers in conflict afflicted areas in the country. Intrinsic factors that motivate HRWs to continuously engage in human rights work despite facing adverse situations include altruism, belief that they are advocating a just cause, feeling a sense of fulfillment, and strongly identifying with their work. Findings likewise show that human rights workers draw strength from the relationships that they have with their partner communities. They are motivated to match the courage of community members (tumbasan ang tapang), and are strengthened by the strong and reciprocal bonds that they have with the communities that they serve. Implications on selecting, preparing, developing and providing organizational support to human rights workers are discussed.
Detection and Classification of Plant Leaf Diseases by using Deep Learning Algorithm

Plant leaf diseases and destructive insects are a major challenge in the agriculture sector. Faster and an accurate prediction of leaf diseases in crops could help to develop an early treatment technique while considerably reducing economic losses. Modern advanced developments in Deep Learning have allowed researchers to extremely improve the performance and accuracy of object detection and recognition systems. In this paper, we proposed a deep-learning-based approach to detect leaf diseases in many different plants using images of plant leaves. Our goal is to find and develop the more suitable deeplearning methodologies for our task. Therefore, we consider three main families of detectors: Faster Region-based Convolutional Neural Network (Faster R-CNN), Region-based Fully Convolutional Network (R-FCN), and Single Shot Multibox Detector (SSD), which was used for the purpose of this work. The proposed system can effectively identified different types of diseases with the ability to deal with complex scenarios from a
Design and Motion Control of a Two-Wheel Inverted Pendulum Robot

 Abstract — Two-wheel inverted pendulum robot (TWIPR) is designed with two-hub DC motors for human riding and motion control evaluation. In order to measure the tilt angle and angular velocity of the inverted pendulum robot, accelerometer and gyroscope sensors are chosen. The mobile robot’s moving position and velocity were estimated based on DC motor built in hall sensors. The control kernel of this electric mobile robot is designed with embedded Arduino Nano microprocessor. A handle bar was designed to work as steering mechanism. The intelligent model-free fuzzy sliding mode control (FSMC) was employed as the main control algorithm for this mobile robot motion monitoring with different control purpose adjustment. The intelligent controllers were designed for balance control, and moving speed control purposes of this robot under different operation conditions and the control performance were evaluated based on experimental results.
An Efficient High-Step-Up Soft-Switching Boost Converter for Photovoltaic Application

In this paper an efficient high step-up converter with a coupled- inductor with soft- switching operation is presented. The proposed topology includes a coupled-inductor boost converter for raising the voltage gain. Moreover, a simple auxiliary resonant circuit composed of an auxiliary switch, a clamping diode and a resonant tank (inductor, capacitor), is adopted in this paper. The coupled-inductor boost converter presents poor efficiency because of hard switching and switch losses. All switches in the adopted circuit perform zero-current switching at turn-on, and zero-voltage switching at turn-off. This soft-switching operation reduces the switching losses and diode reverse-recovery problems. The converter uses PWM method for switching and it is controlled by a PI controller to improve its dynamic output response. The paper presents detailed analysis of the converter operational modes. A 225W dc-dc converter used in PV application is designed and evaluated by simulations. The results are presented to confirm the theoretical analysis.
Methodology of construction accounting nomenclature codes of non-automatic informational base of computer accounting system

The basic approaches for creation a computer accounting informational system (KIS-BO) have been determined and the theoretical aspects of designing non-automatic information base (IB) have been found out. The main emphasis is on such a component of ectopic IB, as the codes of accounting nomenclature, because the further effective exploitation of KIS-BO depends on their rational construction. It is shown that classification and encoding is an undivided creation and operation element of the computer informational base. The aim of classification and encoding is to order and coordinate various objects, concepts, properties or other elements of information. The employment of codes can significantly reduce the amount of information and the complexity of its processing at all stages of the technological process of automated data processing. The method of designing codes of accounting nomenclature is proposed and an example of this implementation is given.
Underwater Imaging Using Underwater Vehicle for Subsea Surveillance

This Final Year Project (FYP) focuses on the improvement of images captured through the built-in underwater camera in the HydroView MAX, which is a Remotely-operated Vehicle (ROV) used to perform inspections in subsea environment. Images captured underwater are always degraded due to issues such as light scattering and colour changes. Image-processing algorithms are applied to improve the degraded images so that the images obtained will be enhanced and closer to their true colours for further analysis. These qualities are required so that the degree of corrosion of the underwater pipelines can be estimated with considerable reliability. The estimation of the corrosion degree is made possible by judging on the percentage of corroded surface over the pipeline surface based on the binary image generated.
Efficient Simulation of Field/Circuit Coupled Systems With Parallelized Waveform Relaxation

This article proposes an efficient parallelized computation of field/circuit coupled systems co-simulated with the waveform-relaxation (WR) technique. The main idea of the introduced approach lies in the application of the parallel-in-time method parareal to the WR framework. Acceleration obtained by the time-parallelization is further increased in the context of micro/macro-parareal. Here, the field system is replaced by a lumped model in the circuit environment for the sequential computations of parareal. The introduced algorithm is tested with a model of a single-phase isolation transformer coupled to a rectifier circuit.
Homogeneous Swarm Robots Exploration

Exploration process in a real application seems had attracts the interest from researchers and academics nowadays. The usage of robots rather than human themselves give many benefit in term of security and safety, also in time and energy consumption. This project aims to generate a code represent the exploration technique in an unknown environment for homogeneous multi-agents systems. In multi-agents system, there are many variable involve in the result of the exploration process. The number of the agents, network connectivity, type of hardware, the environment and other aspects must be considered in multi-agents system exploration investigation. However, due to time constraint, this project only studies the effect of the number of robots or agents in the exploration task. The technique chose for the task must have a certain rules and assumptions to make the exploration process run smoothly and efficient. The code has been developed by using NetLogo due to the availability of sample models and toolboxes. Recording of the data was also done through the use of NetLogo. Finally, the data is arranged by using Microsoft Excel to extract the recorded data for analysis process. The usage of Minitab software is used to plot the graph. Hopefully, this system can be used in real application with the appropriate hardware.
Research on Rectal Tumor Identification Method by Convolutional Neural Network Based on Multi-Feature Fusion

SUMMARY Aiming at the obscure features of tumors in rectal CT images and their complexity, this paper proposes a multi-feature fusion-based convolutional neural network rectal tumor recognition method and uses it to model rectal tumors for classification experiments. This method extracts the convolutional features from rectal CT images using Alexnet, VGG16, ResNet, and DenseNet, respectively. At the same time, local features such as histogram of oriented gradient, local binary pattern, and HU moment invariants are extracted from this image. The above local features are fused linearly with the convolutional features. Then we put the new fused features into the fully connected layer for image classification. The experimental results finally reached the accuracy rates of 92.6 %, 93.1 %, 91.7 %, and 91.1 %, respectively. Comparative experiments show that this method improves the accuracy of rectal tumor recognition.
PSO and TLBO based Reliable Placement of Controllers in SDN

SDN (software defined networks) is a programmable network architecture that divides the forwarding plane and control plane. It can centrally manage the network through a software program, i.e., controller. Multiple controllers are required to manage the current software defined WAN. Placing multiple controllers in a network is known as controller placement problem (CPP). Only one controller is not capable to handle the scalability and reliability issues. To tackle these issues, multiple controllers are required. Efficient deployment of controllers in SDN is used to improve the performance and reliability of the network. To the best of our knowledge, this is the first attempt to minimize the total average latency of reliable SDN along with the implementation of TLBO and PSO algorithms to solve CPP. Our experimental results show that TLBO outperforms PSO for publicly available topologies.
Detecting Free Standing Conversational Group in Video Using Fuzzy Relations

In Computer Vision and Pattern Recognition, surveillance-video crowded scenes have been analysed according to their structure, where the detection of distinguishable people groups is an essential step. In this paper, we are interested in detecting F-Formations (i.e. free standing conversational groups) on video, which are formed by people social relations. We proposed a new method based on fuzzy relations, where a new social representation for computing relation between individuals, fusion for search consensus in multiple frame and clustering are introduced. Finally, our proposal was tested in a real-world dataset, improving the already reported scores from literature.
Method of classification of global machine conditions based on spectral features of infrared images and classifiers fusion

ABSTRACT This paper describes an original method of global machine condition assessment for infrared condition monitoring and diagnostics systems. This method integrates two approaches: the first is processing and analysis of infrared images in the frequency domain by the use of 2D Fourier transform and a set of F-image features, the second uses fusion of classification results obtained independently for F-image features. To find the best condition assessment solution, the two different types of classifiers, k-nearest neighbours and support vector machine, as well as data fusion method based on Dezert–Smarandache theory have been investigated. This method has been verified using infrared images recorded during experiments performed on the laboratory model of rotating machinery. The results obtained during the research confirm that the method could be successfully used for the identification of operational conditions that are difficult to be recognised.
Classification Ensemble Based Anomaly Detection in Network Traffic

Recently, the expansion of information technologies and the exponential increase of the digital data have deepened more the security and confidentiality issues in computer networks. In the Big Data era information security has become the main direction of scientific research and Big Data analytics is considered being the main tool in the solution of information security issue. Anomaly detection is one of the main issues in data analysis and used widely for detecting network threats. The potential sources of outliers can be noise and errors, events, and malicious attacks on the network. In this work, a short review of network anomaly detection methods is given, is looked at related works. In the article, a more exact and simple multi-classifier model is proposed for anomaly detection in network traffic based on Big Data. Experiments have been performed on the NSL-KDD data set by using the Weka. The offered model has shown decent results in terms of anomaly detection accuracy.
Utilizing Virtual Machines Efficiently using Synchronized Throttled Algorithm in Cloud

Cloud computing is network model which is used for accessing, storing and managing the data or resources present in the cloud and utilizing it for further purpose. There are various issues present in the cloud computing such as security, privacy, Service quality, Load balancing on virtual machines and soon. Load balancing is major issue in cloud environment. Load balancing is mechanism in which load is distributed among different nodes to get the best performance and to have minimum response time. Cloud computing consists of large number of network and components that are present. So, there is a need of balancing of load on machines. There are different algorithms that are implemented earlier. This paper proposes an algorithm that focuses on utilization of all virtual machine present. Experimental analysis is done using cloud analyst.
Securing Wireless Networks against Multiple Spoofing Attacks

Wireless spoofing attacks can significantly impact the performance of networks as they are easy to launch. Though we can use cryptographic authentication for verifying the identity of the node, conventional securities approaches are not desirable because of their overhead requirements. In this paper we propose to use physical properties associated with the node which is hard to falsify and reliant on cryptography, as the bases for detecting spoofing attacks, determining the number of attackers when there are multiple adversaries attacking as the same node entity. We propose to use the unique key generated using hashing algorithm for wireless nodes to detect the spoofing attacks. Cluster based mechanisms are used to determine the number of attackers. Our detection results using a representative set of algorithms provides a strong evidence of high accuracy of localizing multiple
Small data: practical modeling issues in human-model -omic data

Human-model data are very valuable and important in biomedical research. Ethical and economical constraints limit the access to such data, and consequently these datasets rarely comprise more than a few hundred observations. As measurements are comparatively cheap, the tendency is to measure as many things as possible for the few, valuable participants in a study. With -omics technologies it is cheap and simple to make hundreds of thousands of measurements simultaneously. This few observations–many measurements setting is a high-dimensional problem in the technical language. Most gene expression experiments measure the expression levels of 10 000–15 000 genes for fewer than 100 subjects. I refer to this as the small data setting. This dissertation is an exercise in practical data analysis as it happens in a large epidemiological cohort study. It comprises three main projects: (i) predictive modeling of breast cancer metastasis from whole-blood transcriptomics measurements; (ii) standardizing a microarray data quality assessment in the Norwegian Women and Cancer (NOWAC) postgenome cohort; and (iii) shrinkage estimation of rates. These three are all small data analyses for various reasons. Predictive modeling in the small data setting is very challenging. There are several modern methods built to tackle high-dimensional data, but there is a need to evaluate these methods against one another when analyzing data in practice. Through the metastasis prediction work we learned first-hand that common practices in machine learning can be inefficient or harmful, especially for small data. I will outline some of the more important issues. In a large project such as NOWAC there is a need to centralize and disseminate knowledge and procedures. The standardization of NOWAC quality assessment was a project born of necessity. The standard operating procedure for outlier removal was developed so that preprocessing of the NOWAC microarray material should happen in the same way every time. We take this procedure from an archaic R-script that resided in peoples email inboxes to a well-documented, open-source R-package and present the NOWAC guidelines for microarray quality control. The procedure is built around the inherent high value of a single
Comparative Analysis of Standard Interoperable Protocols of Internet of Things

Internet of Things (IoT) consists things that have unique identities that are connected to the internet. IoT is anticipated to bridge various technologies to enable new applications by connecting physical objects together in support of intelligent decision making. In this paper we compare protocols of different layers based on factors like speed, distance,frequency and cost by varying number of packets transmitted and number of nodes took part in communication.The purpose is to support wireless connectivity of large number of industrial and home applications etc.This also introduces various aspects of Contiki Operating system designed for the IoT environment along with the Cooja platform.