Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of Remote Sensing Principles 1986: The Unsolved Problems

In the collection and dissemination of satellite remote sensing data, discrepancy of principle between principle of common heritage of mankind and principle of sovereignty have always became an issue of dispute. In order to uphold the sensed state’s rights as a sovereign state, Principles on Remote Sensing 1986 had been implemented on 11 April 1986 to govern the activities of satellite remote sensing by sense State. This article attempts to identify the lacuna of law in the implementation of Principles on Remote Sensing 1986, in particular in the occasion of collection and dissemination of satellite remote sensing data. From the critical analysis which has been done by the writer, the writer concluded that Principles on Remote Sensing 1986 fails to protect the legal interest of sensed state. Therefore, the writer proposes international cooperation as the most effective solution to the lacuna of law in the Principles on Remote Sensing 1986.
Recommender systems with Bayesian aspect models and the effect of approximate inference

Recommender Systems with Bayesian Aspect Models and the Effect of Approximate Inference D. Verrezen Department of Electrical Engineering, University of Stellenbosch, Private Bag X1, Matieland, 7602. Thesis: MScEng March 2018 Recommender systems form an important part of the modern world. These systems allow users to find relevant items in often huge item collections. Collaborative filtering is a pervasive and popular form of recommender that recommends to users based on their histories and the histories of other users. The field was long dominated by two forms of collaborative filtering: neighbourhood methods and matrix factorisation models. The two approaches were based on the assumption that the better the prediction of the rating a user would give an item, the greater the quality of the recommendations. This assumption has been criticised as being misleading. One major criticism is that the recommender systems are not being evaluated on the quality of the actual recommendation list. The systems are instead being evaluated on how well they perform a proxy task: predicting ratings. Another criticism is that it leads to recommenders that overfit to popular items that have the majority of the observed feedback. One possible improvement is to instead evaluate recommender systems by how effectively they rank items by determining how many relevant items they can return for a user in their top N results. However, i Stellenbosch University https://scholar.sun.ac.za
Facial recognition for police purposes using computer graphics and neural networks

Neural nets provide a viable solution to identifying criminals photographed at the scene of the crime. A program was written in lightspeed Pascal on a Macintosh II personal computer to simulate a WISARD neural network. A target face from the photograph collection was chosen at random and the net was trained to recognise it. The experimental work is outlined. < >
A Survey on Markov Model

This paper performs a survey on Web Page Prediction Markov model. Prediction and Prefetching methods of Web page have been widely used to reduce the access latency problem on the networks. However, if Prefetching of Web page is not accurate and Prefetched web pages are not visited by the users in their accesses, the limited bandwidth of network and services of server will not be used efficiently and may face the access delay problem. Therefore, it is critical that we have an effective prediction method during prefetching. The Markov models have been widely used to predict and analyse user’s navigational behaviour. All the activities of web users have been saved in web log files. The stored users’ session is used to extract popular web navigation paths and predict current users’ next web page visit.
A Safe Sharing Authentication Scheme for Marine Images on Cloud

Nowadays, Cloud computing has become a very hot technology. with the emergence of cloud storage model, more and more users choose cloud to store the image data, while it brings challenges for image data security and usability due to the openness of cloud. An image authentication scheme is proposed to protect the ocean remote sensing image confidentiality on cloud, combining the Hash function and the threshold scheme, detecting the changes of the image data in sensitive regions and verifying the consistency for Encryption and Recovery images, The sensitive-region image can’t be restored when the case that more than n-k out of n sub-secrets get lost or tampered happened. To avoid this, this paper divided the sensitive region image into blocks, and implemented secret sharing for each sub block, guarantee the lossless recovery of partial image and enhance the availability of image data. Through experiment contrast analysis, the algorithm can effectively prevent fraud during the process of secret image recovery, and it is more safe than the traditional methods.
The analysis of Grooming Factor on Optimization Algorithms in All-optical Network

The most important thing in all optical networks is to reduce the cost of using the network in electronic processing equipment, along with wavelength division multiplexing (WDM) technology are increasingly improved. Add-drop multiplexer (ADM) is not only the most important equipment but also the most basic equipment using the point difference in all-optical networks. The function of the ADM processing device ,is a separation or insert the desired wavelength to networks. The key to reducing the cost is to reduce the number of ADM uses and minimize the number of ADMs. Traffic grooming problem is an important generalization of the ADM minimization problem, which is NP-hard. The specific way is to effectively package the number of available wavelengths ADMS on low speed traffic streams by requiring the number of wavelength per fiber and the required network, so as to reduce the network cost as much as possible. Synchronous optical network (SONET), which is a fiber-optic network for high-speed data communications transmission network . This paper mainly use the algorithm GROOMBYSC(k) to solve the traffic grooming problems in bi-directional interconnected SONET/WDM rings networks. The algorithm analysis shows that the algorithm has a better approximation ratio. We further improve the algorithm by computational analysis. We obtain a similar result by using the algorithm, and the approximation ratio is 2 ln (ln ) g o g  , where g is the grooming factor. Algorithm analysis shows that the grooming factor smaller than its approximation ratio of the approximation algorithm, the better the performance of the algorithm can be seen, what’s more we can obtain proved a general of the approximation algorithm.
Development of Pocket Book Accounting Based E-Learning Media with Student Center Approach

: Classroom learning activities require creativity and innovation, especially in delivering materials in the classroom. Innovation and creativity of lecturers will affect learning, especially in terms of students' understanding of service journal entry adjustment materials. Digital pocket book is a form of media development of lecturers in increasing interest and motivation of students in the class so that students are more active. The purpose of this development is to determine the feasibility of E-learning based pocket book by material experts and graphic media experts, as well as to find out the student's response to the pocket book. This type of research is a research of development of ADDIE (Analysis, Design, Development, Implementation, and Evaluation) model. Data analysis techniques used the results of the review and validation of material experts and media experts and student response results. The results of validation from the material experts obtained a score of 87.5% with very worthy category. The results of validation from the media expert obtained a score of 85.75% with very worthy category. While the student response of 87.08% with very good category. The average percentage of all experts' validation is 86.77% with very worthy criteria.
INTER AND INTRA CLUSTER ON SELF-ADAPTIVE DIFFERENTIAL EVOLUTION FOR MULTI-DOCUMENT SUMMARIZATION

Multi – document as one of summarization type has become more challenging issue than single-document because its larger space and its different content of each document. Hence, some of optimization algorithms consider some criteria in producing the best summary, such as relevancy, content coverage, and diversity. Those weighted criteria based on the assumption that the multi-documents are already located in the same cluster. However, in a certain condition, multi-documents consist of many categories and need to be considered too. In this paper, we propose an inter and intra cluster which consist of four weighted criteria functions (coherence, coverage, diversity, and inter-cluster analysis) to be optimized by using SaDE (Self Adaptive Differential Evolution) to get the best summary result. Therefore, the proposed method will deal not only with the value of compactness quality of the cluster within but also the separation of each cluster. Experimental results on Text Analysis Conference (TAC) 2008 datasets yields better summaries results with average ROUGE-1 on precision, recall, and f - measure 0.77, 0.07, and 0.12 compared to another method that only consider the analysis of intra-cluster.
Metaheuristic Optimization in Power Engineering

A metaheuristic is a consistent set of ideas, concepts, and operators to design a heuristic optimization algorithm, that can provide a sufficiently good solution to an optimization problem with incomplete or imperfect information. Modern and emerging power systems, with the growing complexity of distributed and intermittent generation, are an important application for such methods. This book describes the principles of solving various problems in power engineering via the application of selected metaheuristic optimization methods including genetic algorithms, particle swarm optimization, and the gravitational search algorithm. Applications covered include power flow calculation; optimal power flow in transmission networks; optimal reactive power dispatch in transmission networks; combined economic and emission dispatch; optimal power flow in distribution networks; optimal volt/var control in distribution networks; optimal placement and sizing of distributed generation in distribution networks; optimal energy and operation management of microgrids; optimal coordination of directional overcurrent relays; and steady-state analysis of self-excited induction generators.
A Quantitative Analysis of Data Collected From the Last Planner System in Brazil

In Brazil, a large number of companies have implemented the Last Planner system in order to improve the performance of their production systems. However, most research studies developed so far have emphasized the analysis of qualitative data, based on a small number of case studies. Therefore, there seems to be a good opportunity to learn more about production control by analyzing data from a larger sample of projects, based on performance measures such as the percentage of plans completed (PPC) and the causes for the non completion of work packages. This paper presents further developments of a research project that was first presented at the IGLC13 Conference. It is based on data from 133 projects. Some of them have been investigated in previous research projects at the Building Innovation Research Unit (NORIE) of the Federal University of Rio Grande do Sul (UFRGS). The remaining data have been provided by companies that have been using the Last Planner system for several years. At first, the average PPC and the reasons for the non completion of tasks were analysed using descriptive statistics. In a sample of 96 projects multivariate regression analysis was used for explaining the variance of weekly PPC, using as independent variables the causes for non completion of work packages, number of assignments completed and timing of the projects. The results indicated that a major problem in most projects is the lack of effective implementation of look-ahead planning. Moreover, for multivariate regression analysis, some causes for the non completion of work packages, such as work force and planning have a strong contribution for explaining the variance of weekly PPC.
A Genetic Algorithm Approach for Group Recommender System Based on Partial Rankings

Abstract Many recommender systems frequently make suggestions for group consumable items to the individual users. There has been much work done in group recommender systems (GRSs) with full ranking, but partial ranking (PR) where items are partially ranked still remains a challenge. The ultimate objective of this work is to propose rank aggregation technique for effectively handling the PR problem. Additionally, in real applications, most of the studies have focused on PR without ties (PRWOT). However, the rankings may have ties where some items are placed in the same position, but where some items are partially ranked to be aggregated may not be permutations. In this work, in order to handle problem of PR in GRS for PRWOT and PR with ties (PRWT), we propose a novel approach to GRS based on genetic algorithm (GA) where for PRWOT Spearman foot rule distance and for PRWT Kendall tau distance with bucket order are used as fitness functions. Experimental results are presented that clearly demonstrate that our proposed GRS based on GA for PRWOT (GRS-GA-PRWOT) and PRWT (GRS-GA-PRWT) outperforms well-known baseline GRS techniques.
Fingerprint identification using distributed computing.

Biometric systems such as face, palm and fingerprint recognition are very computationally expensive. The ever growing biometric database sizes have posed a need for faster search algorithms. High resolution images are expensive to process and slow down less powerful extraction algorithms. There is an apparent need to improve both the signal processing and the searching algorithms. Researchers have continually searched for new ways of improving the recognition algorithms in order to keep up with the high pace of the scientific and information security world. Most such developments, however, are architectureor hardware-specific and do not port well to other platforms. This research proposes a cheaper and portable alternative. With the use of the Single Program Multiple Data programming architecture, a distributed fingerprint recognition algorithm is developed and executed on a powerful cluster. The first part in the parallelization of the algorithm is distributing the image enhancement algorithm which comprises of a series of computationally intensive image processing operations. Different processing elements work concurrently on different parts of the same image in order to speed up the processing. The second part of parallelization speeds up searching/matching through a parallel search. A database is partitioned as evenly as possible amongst the available processing nodes which work independently to search their respective partitions. Each processor returns a match with the highest similarity score and the template with the highest score among those returned is returned as match given that the score is above a certain threshold. The system performance with respect to response time is then formalized in a form of a performance model which can be used to predict the performance of a distributed system given network parameters and number of processing nodes. The proposed algorithm introduces a novel approach to memory distribution of block-wise image processing operations and discusses three different ways to process pixels along the partitioning axes of the distributed images. The distribution and parallelization of the recognition algorithm gains up to as much as 12.5 times performance in matching and 10.2 times in enhancement.
Language processing in cochlear implant users using fNIRS

Objectives: When people age, their hearing thresholds increase and visual acuity decreases. The aim of this study was to investigate whether age affected the audio-visual (AV) integration of speech cues in young and old people who had normal hearing. Design: Sixteen young and 13 old NH participants were recruited. Reaction times in audio-alone (A), visual-alone (V), and AV conditions were measured during a speeded detection task in which participants were asked to detect a target /aSa/ sound among 11 alternatives in the format of /aCa/. A race model was applied to reveal whether AV integration occurred. In a second experiment, identification accuracy of consonants in a closed set task and identification of CNC words in an open set task were measured. AV integration was quantified by using a combination of a probability model and a cue integration model, which compared participants’ AV identification accuracy to modelled accuracy for minimal or optimal integration, respectively. Results: The reaction time measures and the race model revealed consistent AV integration in both young and old groups. However, no difference in the amount of AV integration was found between the young and old. The identification accuracy measures and the combination of a probability model and a cue integration model found that old participants had optimal and no different AV integration compared to the young group. Conclusions: The overall results of this study did not find any significant effect of age on the integration of audio-visual speech cues.
Group Key Based Session Key Establishment Protocol for a Secure Remote Vehicle Diagnosis

In this paper, a group key based session key establishment protocol is introduced for a secure remote vehicle diagnosis. The proposed scheme aims at providing secure authentication and session key establishment between a vehicle manufacturer server and a group of in-vehicle electronic control unit based on a key graph. The detailed operations of the proposed scheme are provided while providing security analysis results of the proposal. Thanks to the use of the group key, the proposed scheme reduces the number of keys and management cost for the secure remote vehicle diagnosis compared with a traditional scheme that uses a symmetric key cryptosystem.
Pulse: an adaptive intrusion detection for the internet of things

The number of diverse interconnected Internet of Things (IoT) devices keeps increasing exponentially, introducing new security and privacy challenges. These devices tend to become more pervasive than mobile phones and already have access to very sensitive personal information such as usernames, passwords, etc., making them a target for cyber-attacks. Given that smart devices are vulnerable to a variety of attacks, they can be considered to be the weakest link for breaking into a secure infrastructure. For instance, IoT devices have recently been employed as part of botnets, such as Mirai, and have launched several of the largest Distributed Denial of Service (DDoS) and spam attacks in history. As a result, there is a need to develop an Intrusion Detection System (IDS) dedicated to monitor IoT ecosystems, which will be able to adapt to this heterogeneous environment and detect malicious activity on the network. In this paper, we describe the initial stages of developing Pulse; a novel IDS for the IoT, which employs Machine Learning (ML) methodologies and is capable of successfully identifying network scanning probing and simple forms of Denial of Service (DoS) attacks.
Application of Fast Fourier Transform (FFT) Algorithm in Finite Impulse Response (FIR) Linear Filtering Using MATLAB

The convolution operation involved in linear filtering is computationally involved task. Since the Discrete Fourier Transform (DFT) provides a discrete frequency representation of a finite duration sequence in the frequency domain, it is interesting to explore its use as a computational tool for linear system analysis and especially for linear filtering. In this paper, we propose how the DFT can be used to perform linear filtering in the frequency domain using MATLAB program. The frequency domain approach based on the DFT is computationally more efficient than time domain convolution due to the existence of the efficient Fast Fourier Transform (FFT) algorithms for computing DFT. We have implemented MATLAB Program to obtain the output results and the graph of input and output signal for two fast convolution methods, namely, overlap-add and overlap-save algorithms.
Predicting Credit Default Swap (CDS) Returns with Machine Learning

Credit Default Swaps (“CDS”) are contracts that insure one party against default in an underlying financial instrument, usually a bond. Therefore, the price of CDS reflects the perceived risk of default in an underlying financial instrument. This project applied Support Vector Machines (“SVMs”) to the prediction of CDS price changes for several individual companies across time. Previous research applying SVMs to predicting CDS prices used historical CDS prices as model inputs. This project proposed and applied several new input variables. Tests over a period of several years, across a group of CDS time-series, indicate that a combined model which uses the new input variables in addition to historical CDS price changes outperforms models that only use historical CDS price changes.
IMRBS: image matching for location determination through a region-based similarity technique for CBIR*

ABSTRACT This paper presents a technique for content-based image retrieval (CBIR) by selecting the regions on the basis of their contribution to image contents. We have analyzed the problems associated with matching regions among the pair of images over the large set of overlapping regions. It is being studied that matching images by using regions having unstructured association can be a serious problem. In this research, we propose a linear formulation technique, which involves simultaneous matching, so that the matched area can have color-similarity histogram, shape and having little overlapping region. It is also analyzed that the selected region can have a small number and overall concavity is low, and tried to cover both the images. It has been studied that CBIR has attracted many researchers and most of the previous CBIR systems have shown the searching procedure of the digital image on the basic features such as texture, color, size, and shape of a certain query image in a large database. According to this research, we are going to present a region-based image repossession system that is going to exhibit a model that would help specify multiple regions of interest inside the query image. In this research, we have presented a novel visual feature that might contain color size of the region query and its moments, however, to combine color and region-size information of the watershed region. Moreover, a technique has been modeled for region filtering that might depend upon the color size of the given query image and that would stimulate the process of screening out the most nonrelated region and images for pre-processing of the recovery of image. Therefore, the technique presented would help shorten the consequence of image background on image-matching decision; however, an object's color would receive much more focus. Apart from that, amendment to region-based similarity measurement has also been presented. It has been proved with the help of simulation results that the given descriptor with the similarity measure amendments outperforms the existing descriptor that would be considered in content-based image-retrieval applications. Moreover, the given approach has performed better than the previous approaches. Our method would be based upon a simple and reliable metrics, which is being used to calculate similarities. We have performed numerous simulations and verified that the given approach has outperformed the current techniques in localization, and is going to have vigorous object discovery in the existing mixed-class dataset.