Miracle-dz/UniFAR-FTUs huggingface dataset:
Data quality in data mining and machine learning

With advances in data storage and data transmission technologies, and given the increasing use of computers by both individuals and corporations, organizations are accumulating an ever-increasing amount of information in data warehouses and databases. The huge surge in data, however, has made the process of extracting useful, actionable, and interesting knowledge from the data extremely difficult. In response to the challenges posed by operating in a data-intensive environment, the fields of data mining and machine learning (DM/ML) have successfully provided solutions to help uncover knowledge buried within data. DM/ML techniques use automated (or semi-automated) procedures to process vast quantities of data in search of interesting patterns. DM/ML techniques do not create knowledge, instead the implicit assumption is that knowledge is present within the data, and these procedures are needed to uncover interesting, important, and previously unknown relationships. Therefore, the quality of the data is absolutely critical in ensuring successful analysis. Having high quality data, i.e., data which is (relatively) free from errors and suitable for use in data ruining tasks, is a necessary precondition for extracting useful knowledge. In response to the important role played by data quality, this dissertation investigates data quality and its impact on DM/ML. First, we propose several innovative procedures for coping with low quality data. Another aspect of data quality, the occurrence of missing values, is also explored. Finally, a detailed experimental evaluation on learning from noisy and imbalanced datasets is provided, supplying valuable insight into how class noise in skewed datasets affects learning algorithms.
A Comparison of Two Tree Detection Methods for Estimation of Forest Stand and Ecological Variables from Airborne LiDAR Data in Central European Forests

Estimation of biophysical variables based on airborne laser scanning (ALS) data using tree detection methods concentrates mainly on delineation of single trees and extraction of their attributes. This study provides new insight regarding the potential and limits of two detection methods and underlines some key aspects regarding the choice of the more appropriate alternative. First, we applied the multisource-based method implemented in reFLex software (National Forest Centre, Slovakia), which uses the information contained in the point cloud and a priori information. Second, we applied the raster-based method implemented in OPALS software (Vienna University of Technology, Austria), which extracts information from several ALS-derived height models. A comparative study was conducted for a part of the university forest in Zvolen (Slovakia, Central Europe). ALS-estimated variables of both methods were compared (1) to the ground reference data within four heterogonous stands with an area size of 7.5 ha as well as (2) to each other within a comprehensive forest unit with an area size of 62 ha. We concluded that both methods can be used to evaluate forest stand and ecological variables. The overall performance of both methods achieved a matching rate within the interval of 52%–64%. The raster-based method provided faster and slightly more accurate estimate of most variables, while the total volume was more precisely estimated using the multisource-based method. Specifically, the relative root mean square errors did not exceed 7.2% for mean height, 8.6% for mean diameter, 21.4% for total volume, 29.0% for stand density index, and 7.2% for Shannon’s diversity index. Both methods provided estimations with differences that were statistically significant, relative to the ground data as well as to each other (p < 0.05).
Research on multi-document summarization based on latent semantic indexing

A multi-document summarization method based on Latent Semantic Indexing (LSI) is proposed. The method combines several reports on the same issue into a matrix of terms and sentences, and uses a Singular Value Decomposition (SVD) to reduce the dimension of the matrix and extract features, and then the sentence similarity is computed. The sentences are clustered according to similarity of sentences. The centroid sentences are selected from each class. Finally, the selected sentences are ordered to generate the summarization. The evaluation and results are presented, which prove that the proposed methods are efficient.
Selection and Research of Distributed Material Flow Information System

Some distributed system frames and design patterns are selected and researched in this paper.And introduces advanced features of the Java 2 platform enterprise edition(J2EE) and comprehensive application of the mixed browser/server(B/S) and client/server(C/S) models.On this basis,the advantages and key technologies of designing material flow information system are presented.
Research Publication Trends among Faculty of Bharathiar University: A Scientometric Study

The study examined the analysis of research publications of the faculty of Bharathiar University, Coimbatore. Bibliographic records of 4645 items were retrieved from Scopus database between 1982 and 2015 and increasing publications trends were seen in Bharathiar University. The average output of the organization was 137 per year. Among the 4645 papers published in the span of 34 years, highest number of 748 papers was published in the year 2015. The total numbers of citation received by the papers were 50964 and the citing articles were 3559. The average citation per article was 14.3. Keywords : Biblioimetrics, scientometics, collaboration pattern, publication density, Bharathiar University Cite this Article Sivakumar B. Research Publication Trends among Faculty of Bharathiar University: A Scientometric Study. Journal of Advancements in Library Sciences . 2018; 5(2): 42–50p
Hybrid optimization strategy based on genetic algorithm and tabu search

An hybrid optimization algorithm—TSGA which combines the characteristic of tabu search and genetic algorithm is given.On one hand, better original solutions are provided to tabu search, reduces the called times of tabu search, on the other hand, it can also improves the mountain climbing of the genetic algorithm, speeds up the convergence speed and gets satisfied results.The test results demonstrate the validity and feasibility of this hybrid optimization algorithm.By applying it to network congestion control, the simulation and research results offer an effective approach to implement network congestion control.
North State: Enabling intelligent GMES services for carbon and water balance modeling of Northern forest ecosystems

The North State project of the Framework Program 7 of the European Union will demonstrate how innovative methods applied to the new Sentinel data streams can be combined with models to monitor carbon and water fluxes for pan-boreal Europe. The project will provide a paradigm for the development of products for future Copernicus services that will be applicable far beyond its specific application to the boreal zone. The North State has sites for intensive study in Finland, Iceland and Russia. Additionally, the fluxes will be modelled over a panboreal – pan-arctic region reaching from Iceland to the Ural Mountains. The project (grant agreement 606962) started in October 2013 and will last three years.
Resource Planning Optimization in Network Schedule Using Genetic Algorithms

Two optimization problems:resource-constrained allocation and unlimited resource leveling,are solved by the adoption of the genetic algorithm technique in resource planning optimization,the results of which are more satisfying than those of the network schedule techniques and mathematical programming methods which can’t promise to find the best solution. On the basis of this, a multi-objective optimization combined the two aspects above is presented and the resource planning optimization in the shortest time is realized. Moreover, the feasibility and high efficiency of the method are verified by a case study. Therefore, this method has its practical application value for the resource planning optimization of project management.
Enhanced Permission Based Malware Detection in Mobile Devices Using Optimized Random Forest Classifier with PSO-GA

Smartphones and mobile devices are rapidly growing with their popularity as a part of global infrastructure powered communication system. As mobile devices become ubiquitous, used for a wide variety of application areas like personal communication, data storage, accessing online information, making payment, etc. The tremendous growth of smartphone usage makes it a target for malicious attackers to propagate malware attacks. Increased demand for mobile devices is due to the huge availability of applications that can be downloaded and installed easily on these devices. It is difficult for the general users to differentiate between the set of permissions which are potentially harmful and those which are not. This paper proposes to solve these issues by enhanced machine learning based malware detection framework using optimization algorithms. New classifier is developed by integrating GA and PSO with Random Forest algorithm. The Outcome from this paper is a new MSGP Malware Detection System consisting of MSGP-MDS Classifier. This reveals that classification of Android APK files using PSO plays a critical role in realizing higher accuracy with the minimum computation resource requirement.
An Analysis of the Effect of Students’ Geographical Background on Equity in Form One Admission in National Secondary School Clusters in Kenya

Abstract Through its educational policies and plans, Kenya is among countries whose aim is to achieve equity in access to secondary school education by pupils from different background as envisaged in Vision 2030 and Sustainable Development Goals. However, this dream still remains elusive. This paper provides empirical evidence of the effect of students’ geographical background on equity in form one admission in national secondary school clusters in Kenya using data from a sample of 1935 form one students admitted in Kenyan’s 103 national secondary schools in the year 2016. The results of the multinomial logistic regression indicate that even after holding other predictor variables constant, students’ geographical background significantly affected their admission to the national secondary schools clusters. Consequently, there is need to develop sound and inclusive strategies to accelerate equity in form one admission to national secondary school clusters irrespective of geographical background of the students. The Ministry of Education should design policies that equalise opportunities for students from different geographical background in accessing all national secondary schools clusters.
Cassava Mill Effluents Recycling throughBioenergy Production: A Review

The processing of cassava tuber into finished products such as gari generates large wastes water. The effluents are known to contain cyanide and acidic pH in addition to other chemical characteristics such as heavy metals, chemical oxygen demand among others. The effluents have toxicological impacts on the receiving ecosystem (Soil and Surface Water) as well as some of its associated fauna and flora. During degradation processes by the indigenous microbes in the soil, the effluents emits odour that are offensive to human. This study reviews recycling options of cassava mill effluents through bioenergy production. The study found that cassava mill effluents have demonstrated positive potentials for biogas, biohydrogen, bioethanol, bioelectricity using varying technologies. During bioenergy production, the characteristics of the raw effluents is improved upon in some of the parameters such as chemical oxygen demand, total solid, pH etc probably due to the activities of some microorganisms. As such, there is the need for research to focus on other possible utilization of the sludge generated such as bio-fertilizer production as an option for reuse.
From the deep earth to the atmosphere: new geochemical approaches to address marine productivity, long-term climate, and continental rifting

Author(s): House, Brian Morrison | Advisor(s): Norris, Richard D | Abstract: This dissertation, as indicated by its title, is eclectic, but is united around the theme of developing and applying new geochemical approaches to answer large-scale questions in earth science. And it represents an interdisciplinary contribution, drawing on paleobiology, paleoclimate and paleoceanography as well as incorporating near surface and deep earth seismic models and signal analysis techniques to expand the range of conclusions that can be extracted from large geochemical datasets. The first two chapters focus on the marine biosphere in the geologic past. Chapter 1 presents a new high-throughput method for establishing paleo export productivity as well as evidence that increased supply of Southern Ocean water masses during glacial intervals stimulated productivity off the coast of East Africa. The method presented in Chapter 1 enabled Chapter 2, in which an unprecedented global view of marine productivity reveals a global biological “heartbeat”: marine productivity varies at the same frequencies as Earth’s orbital obliquity and precession, indicating a fundamental link between astronomical and biological processes. Chapters 3 and 4 explore the information contained in the carbon in marine sediments to better constrain the amount and form of C subducting along the Sunda margin, Indonesia and to infer a multi-phased expansion of C4 grasslands on the Indian subcontinent, indicating punctuated episodes of aridification. The final chapter traces the He isotope signature of a mantle plume from the core-mantle boundary to elucidate the processes involved in forming and sustaining continental rifting in Ethiopia and Afar. The breadth of topics covered here reflects the range of my own curiosity in pursuing what Nietzsche termed froliche wissenschaft – joyful science.
A dynamic searchable encryption CP-ABE scheme in cloud storage

In this paper, we constructed a dynamic searchable encryption CP-ABE scheme (DSE-CP-ABE)which is based on cipher text index. The scheme combined the existing cipher text strategy of access control scheme, extended inverted index structure, and introduced the trusted third party, which not only realized the cipher text sharing, but also supported for local update of the cipher text.At the same time we used the Proxy Re-encryption to ensure the backward and forward security. Compared with the existing access control schemes, this scheme significantly reduces the number of times of local updates, and is of higher security, reliability and practicability.
Maximum A Posteriori-based frame synchronization algorithm

The transmission efficiency of digital co mmunication links with high bit error rates and high slip rates was enhanced by the Maximum A Posteriori-based frame synchronization algorithm. The algorithm u ses the a priori knowledge of the slipping and channel errors, as well as the c orrelation of adjacent synchronization codes to improve the synchronization. Sim ulation results showed that the algorithm outperforms the traditional Maximum Li kelihood algorithm. The missing synchronization rate was less than 5×10-5 even when the bit error rate was as high as 0.05 and the slip rate was as high as 10-8. Moreover, the algorithm can be easily extended to the synchroniz ation of variable-length frames or the demultiplexing of variable-length packe ts. 
An investigation of pedestrian traffic flow in a representative commercial area in China

Abstract The large number of people that can congregate in urban commercial areas poses a potential risk for pedestrian safety and is an important safety issue for urban administrators. This research describes our investigation of a crowded commercial area to reveal pedestrian traffic flow by counting pedestrians in all transportation nodes over a 14-h period. Some interesting findings were revealed from the on-site investigations: (i) the counting of pedestrian numbers via traffic flow is not exact because the destinations of pedestrians are not fixed, and attention should be paid to the key roads between subway exits and commercial areas; (ii) the proportion of the number of pedestrians between different bus stations is stable because bus numbers at every station are not variable and (iii) the proportion of the number of pedestrians between different subway exits is unstable because subway exits are linked and pedestrians from one station can leave via another exit. These findings serve to assist administrators in on-site crowd control and riot control. Highlights The proportion of the number of people between different bus stations is stable. The proportion of the number of people between different subway exits is unstable. Counting pedestrian numbers via transportation modes is not exact.
Solving Complex Modeling of System-on-a-Chip (SOC) Test Automation and Optimal Resource Allocation by Neural Networks

KLOYPAYAN, JIRAWAN. Solving Complex Modeling of System-on-a-Chip (SOC) Test Automation and Optimal Resource Allocation by Neural Networks. (Under the direction of Professor Yuan-Shin Lee.) The objective of this research is to optimize the testing time and test resource allocation for System-on-a-Chip (SOC). The mathematical formulation and the neural networks with different techniques are proposed to solve these SOC test problems. First, a fixed-weight neural network combined with heuristic algorithms has been developed to solve the SOC test scheduling problems. The objective of this SOC test automation is to minimize the SOC testing time subject to different constraints: (i) precedence constraint, (ii) resource constraint, (iii) core constraint, and (iv) power constraint. Heuristic algorithms are often used to prevent the neural network from getting trapped in a local optima. The developed neural network can effectively solve the SOC test scheduling models with disjunctive constraints. The results show that the proposed method can efficiently solve a large-size SOC test scheduling problem within reasonable computing time. Second, to solve the resource allocation and the width selection problems for SOC test automation, a maximum neural network (MNN) has been proposed in this research for handling more complex SOC test problems. The SOC test automation problem with resource allocation is a NP-hard problem. The proposed maximum neural network can be used to solve the NP-hard SOC test problems within polynomial time. The results show that, by using the developed maximum neural network, the overall testing time for the SOC can be minimized with optimal resource allocation and test access mechanism (TAM) width selection. The computation time of the proposed method is significantly less than the time for traditional methods such as the integer linear programming (ILP) or heuristic algorithms. Third, the SOC test automation problems with core test wrapper design have been studied in this research. The core test wrapper design provides an interface between the core and the SOC in which the core is embedded. After the core test wrapper is designed, the total SOC testing time and the resource allocation for SOC test automation are optimized by using the developed maximum neural network. The proposed method is tested on five SOC benchmarks. The results show that it is possible to find the optimal SOC testing time of the complex SOC systems with shorter computation time than with the existing traditional methods. The techniques presented in this research can be used in test automation for System-on-a-Chip (SOC) design. Solving Complex Modeling of System-on-a-Chip (SOC) Test Automation and Optimal Resource Allocation by Neural Networks By Jirawan Kloypayan A dissertation submitted to the Graduate Faculty of North Carolina State University in partial fulfillment of the requirements for the Degree of Doctor of Philosophy INDUSTRIAL ENGINEERING
Physical and mechanical properties of the south Mashhads granites, Robat Toroghs area (North East of Iran)

Recognition of intact rockrsquos engineering characteristics in construction projects that are dealing with the foundation rock, is quite important and in the primary stages of implementing engineering structures will be seriously considered. In this research, to study the engineering properties of granitic rocks in south of Mashhad City (North East of Iran), three boreholes were drilled to a depth of 10 m in the rock mass and the rock samples were transported to the laboratory. To determine the physical and mechanical properties of these rocks experimental tests such as uniaxial compressive test, triaxial, direct shear, dry and saturated density, porosity and water absorption were performed on the core specimens. Also, jointrsquos roughness coefficient was estimated by the method of Barton. The results can be used for calculation of bearing capacity of rock mass and also in optimal designing of the construction of high-rise structures.
Analysis of near-surface wind flow in close proximity to tornadoes

The near-ground region of a tornado (i.e., corner flow region) experiences significant changes in structure (i.e., single-, two-, and two-cell with multiple vortices) and intensity (i.e., peak pressure drop, maximum wind speed) during its lifetime. Given the unpredictability of tornado occurrence and difficulty in obtaining observations, very little documentation outside of modeling studies exists relating these two components of tornado evolution (e.g. Kosiba et al. 2008; Karstens et al. 2010). Changes in intensity are often evidenced by spatial variations in the degree(s) of damage caused by the tornado. Tornado intensity has traditionally been determined from damage using the Fujita or Enhanced Fujita Scales. However, known complexities in estimating wind speeds from damage limit the value of these intensity determinations (Doswell 2009). In some instances, video documentation of the tornado may exist, and on rare occasions, aerial photographs of scour marks, in addition to damage, produced by the tornado are available. Although videos and photos offer a limited means of quantitatively estimating intensityrelated information (e.g., Golden and Purcell 1977), their qualitative value in deciphering information, such as vortex structure, can be beneficial. To characterize the flow governing tornado vortex structure, laboratory and numerical studies have employed the non-dimensionalized parameter swirl ratio. Swirl ratio can take on many forms, but traditionally it is defined as