Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic recognition of Persian musical modes in audio musical signals

This research proposes new approaches for computational identification of Persian musical modes. This involves constructing a database of audio musical files and developing computer algorithms to perform a musical analysis of the samples. Essential features, the spectral average, chroma, and pitch histograms, and the use of symbolic data, are discussed and compared. A tonic detection algorithm is developed to align the feature vectors and to make the mode recognition methods independent of changes in tonality. Subsequently, a geometric distance measure, such as the Manhattan distance, which is preferred, and cross correlation, or a machine learning method (the Gaussian Mixture Models), is used to gauge similarity between a signal and a set of templates that are constructed in the training phase, in which data-driven patterns are made for each dastgah (Persian mode). The effects of the following parameters are considered and assessed: the amount of training data; the parts of the frequency range to be used for training; down sampling; tone resolution (12-TET, 24-TET, 48-TET and 53-TET); the effect of using overlapping or nonoverlapping frames; and silence and high-energy suppression in pre-processing. The santur (hammered string instrument), which is extensively used in the musical database samples, is described and its physical properties are characterised; the pitch and harmonic deviations characteristic of it are measured; and the inharmonicity factor of the instrument is calculated for the first time. The results are applicable to Persian music and to other closely related musical traditions of the Mediterranean and the Near East. This approach enables content-based analyses of, and content-based searches of, musical archives. Potential applications of this research include: music information retrieval, audio snippet (thumbnailing), music archiving and access to archival content, audio compression and coding, associating of images with audio content, music transcription, music synthesis, music editors, music instruction, automatic music accompaniment, and setting new standards and symbols for musical notation.
Scene Recognition & Classification Using Computer Vision Techniques

: This paper tries to explain the uses and ways of recognizing a scene and classifying into different data sets using computer vision. A scence is required to be recognized by a computer since we are using them in several intelligent systems such as Self driving car, Robots, Upcoming Vision Aide systems, Augmented Reality e.t. c . The need to classify increases as there are several overlapping categories of scenes the data of which could be further reused to make the system more efficient at recognizing the surroundings . We are going to use Several Image processing and Computer vision algorithms to test and compare the results of Final outputs.
Heritage Portuguese and Heritage Polish in Contact with German: More Evidence on the Production of Objects

This paper compares the production of different types of direct objects by Portuguese–German and Polish–German bilingual school-aged children in their heritage languages (HLs), Polish and European Portuguese (EP). Given that the two target languages display identical options of object realization, our main research question is whether the two HLs develop in a similar way in bilingual children. More precisely, we aim at investigating whether bilingual children acquiring Polish and EP are sensitive to accessibility and animacy when realizing a direct object in their HL. The results of a production experiment show that this is indeed the case and that the two groups of bilinguals do not differ from each other, although they may overgeneralize null objects or full noun phrases to some extent. We conclude that the bilingual acquisition of object realization is guided by the relevant properties in the target languages and is not influenced by the contact language, German.
Forum-based online knowledge communities : knowledge processes and their factors of influence : an empirical study using structural equation modeling

Knowledge is becoming ever more important as an individual good, and as a social and economic resource. The research interest of this thesis is how knowledge processes can be optimally supported in the cooperative context of forum-based Online Knowledge Communities (OKCs). The research focuses especially on the two key processes knowledge creation and knowledge transfer. The starting point of the research is a literature review and the outline of individual hypotheses on the basis of an interdisciplinary body of research from the fields of computer science and management science, specifically knowledge management literature, community of practice literature, human-computer interaction literature, and collaborative systems literature. Following the literature review, the empirical part of the research is based on an in-depth study of the users of the electronic communication forums at International University Bremen (IUB). Both empirical parts of research - an ANOVA-based calculation of type of content-based knowledge process values and a SEM-based hypotheses testing of individual factors of impact on an individual's knowledge process contribution - could be shown to be in line with the theoretically deduced hypotheses. Besides the careful selection of a forum-platform (factor service quality) and carefully drafting of the initial content in the case of newly established online knowledge communities (factor perceived quality of content), the best strategy to stimulate participants to contribute a larger share of knowledge-process-relevant content appears to be the emphasis of the online forum as an online knowledge community, with a focus on professional as opposed to social content (factors social vs. professional affiliation motivation). The thesis integrates existing, fragmented research with a limited focus on a restricted number of input aspects and/or knowledge processes into an integrated model of forum-based collaborative knowledge management.
Intelligent Traffic Classification for Detecting DDoS Attacks using SDN/OpenFlow

Distributed denial of service (DDoS) attacks utilise many attacking entities to prevent legitimate use of a resource via consumption. Detecting these attacks is often difficult when using a traditional networking paradigm as network information and control are not centralised. Software-Defined Networking is a recent paradigm that centralises network control, thus improving the ability to gather network information. Traffic classification techniques can leverage the gathered data to detect DDoS attacks.This thesis utilises nmeta2, a SDN-based traffic classification architecture, to study the effectiveness of machine learning methods to detect DDoS attacks. These methods are evaluated on a physical network testbed to demonstrate their application during a DDoS attack scenario.
Job demands and Job Resources: A study of nurses at a General Hospital in Malaysia

Background: The nursing profession is stressful since they have to deal with an extensive number of patients daily. Therefore, the Job Demands-Resources (JD-R) model has been proposed as the fundamental model in this study. The model is important in describing the job demands and job resources among nurses, specifically in Malaysia. Additionally, the job demands and job resources reflect their performance and service outcome. Therefore, this paper extends knowledge by emphasizing on the descriptive analysis basis of job demands and job resources among staff nurses of a general hospital in Malaysia. 60 staff nurses were randomly selected from a general hospital, which is located in Peninsular Malaysia who were involved to determine the level of their job demands and job resources. Objective: This study is used to determine the level of job demands and job resources among staff nurses in Malaysia. Results: Based on overall means, the study found that the nurses received a high level of physical and mental demands. However, the result indicated that they received a low level of autonomy and performance feedback from their respective colleagues and superiors. Additionally, they also received a moderate level of social support from the same colleagues and superiors. Conclusion: The present study is used to provide the updated review of descriptive analysis basis of job demands and job resources among nurses of a general hospital in Malaysia as well as to fill the literature gap.
News Verification Suite: Towards System Design to Supplement Reporters’ and Editors’ Judgements

This paper offers a conceptual basis and describes elements for a multi-layered system to provide information users (newsreaders) with credible information and improve the work processes of the online news (content) producers. I overview criteria of excellence (what editors consider newsworthy) and how reporters (and traditional newsroom professionals used to) verify information to provide high quality of news. I compare the “traditional model of journalism” to the current journalistic practices of “news sharing a.s. a.p.” and identify certain processes that are currently either missing or could be complemented with automatic verification functions, capitalizing on Natural Language Processing (NLP) and Data-Mining (DM).
Effects of rising atmospheric CO2, climate change, and nitrogen deposition on aboveground net primary production in a temperate forest

Temperate forests regrowing from historical land use and land cover change in the eastern US serve as carbon (C) sinks. Environmental drivers have been significantly altered (e.g. rising atmospheric CO2 concentration, warmer temperature, and elevated nitrogen (N) deposition) and will have a wide range of impacts on future forest C sinks. However, the interactions among these environmental drivers are unclear and their effects are subject to uncertainty. We assessed the combined and interactive effects of rising CO2 concentration, climate change (temperature, precipitation), and N deposition on forest aboveground net primary production (ANPP) and their relative contribution to ANPP changes of a temperate forest in the eastern US. We used a process-based ecosystem model PnET-day to simulate coupled cycles of C, water, and N of forest ecosystems. We found that (1) climate change exerted negative effects on ANPP (−0.250 kg C m−2 yr−1) whereas rising CO2 and N deposition enhanced ANPP (+0.253, +0.014 kg C m−2 yr−1); (2) climate change interacted with rising CO2 and N deposition to decrease ANPP (−0.032, −0.018 kg C m−2 yr−1); rising CO2 and N deposition acted in synergy to increase ANPP (+0.014 kg C m−2 yr−1); (3) changes in ANPP were mainly attributed to rising CO2 and climate change whereas N deposition effects and any two- or three-factor interactive effects were relatively small. Our results suggest that the total negative effect sizes will not be offset by the total positive effect sizes, thus resulting in reductions in forest ANPP over the 21st century.
Dynamic Time Based Location Update Strategy in Cellular Networks

: Managing the location information is the most important aspect in Cellular Networks. This is even more important when the user is in moving condition (mobile). In today's world with the invention of latest mobile devices, the mobile device is not just a medium for communication but it is a replacement of the whole office. A person can read / write mails, do social networking, merchant transactions and a lot more through mobile devices. To handle all this tasks effective Location management in cellular communication is 100% needed. If the location of the user is not known then it will be difficult to perform call / sms / mobile data / other transactions with the cellular phone. Study of various location update strategies also covered in this paper. Algorithm for finding best location update strategy among all available strategies. Algorithm and simulation results given for Dynamic Time Based Location Update Strategy.
A multimode and multithreshold approach for energy efficiency in Internet of things systems

The IEEE 802.15. 4 is designed for wireless personal area networks. Indeed, wireless personal area network turns out to help greatly in maintaining a flexible mode of communication within limited area networks. It is in this context that our present study can be set, in which the beacon-enabled mode is enabled with cluster tree topology to reach the scope of a rather extended network, whereby the network turns out to be clustered into several subgroups. Every single subgroup is characterized by its specific duty cycle which is configured by its correspondent personal area network coordinator. Therefore, many modes are enabled in the same network. Based on a very special mathematical model developed by us for energy consumption, the personal area network coordinator detects the actual level of energy in the battery of node. Then, an interesting comparison is made with the multiple thresholds which are already set. After that, both beacon order and superframe order (the standard IEEE 802.15.4 parameters) are recomputed with reference to the remaining energy.
Modal shift to inland waterways: dealing with barriers in two Swedish cases

ABSTRACT This paper analyses strategies to overcome barriers to a modal shift to inland waterway transport (IWT). Barriers identified in research literature have been categorised as regulatory, financial, service quality and market characteristics. Using two cases involving Swedish entrepreneurs initiating IWT, this paper has shown how barriers to the modal shift to IWT can be managed where markets are limited. Modal shift involves multiple actors, and the entrepreneurs interacted with ports, shippers, shipping companies, regulatory actors, forwarders and hauliers, depending on the barrier. Strategies to manage barriers included negotiating, educating stakeholders, securing volumes, conducting a proof-of-concept run and identifying business opportunities for stakeholders. The findings can increase stakeholders’ knowledge of IWT. Policy makers aiming to promote modal shift must understand that fees and legislation act as barriers.
An information systems framework to improve the issuance of identity documents through enhanced data quality in the Republic of Angola

An individual identity document (ID) is the most important identification method within a country, for both the government and the citizens. However, the physical addresses captured and stored in the ID systems and printed on individual IDs in the Republic of Angola are consistently inconsistent, duplicated, and incorrect. As a result, government service delivery, such as basic sanitation, health care, and security are often challenged and compromised. Thus, the aim of this study was to develop an information systems framework to improve the current system that is used to issue IDs in the country. In order to achieve this aim, semistructured interviews were used to collect qualitative data. The data were collected from a governmental institution, the Ministry of Homeland, and from the community of Luanda in Angola. The data collected were hermeneutically analysed through the lens of the duality of structure from a structuration theory perspective. Based on the analysis, factors of influence were revealed and interpreted, which resulted in the development of a framework. The framework is intended to, from an information and communication technology (ICT) perspective, enhance the quality of data and improve the accuracy of issued IDs in Angola.
Highlighting a Key Question for the Common Agricultural Policy: Adoption of Agriculture System Types

One of the key questions that concerns policy makers, related to the long term planning of the EU's Common Agricultural Policy (CAP), is the form of agriculture that farmers intend to follow in the future. In order to highlight that question, a sample of producers from the region of Eastern Macedonia and Thrace in Greece were surveyed and analyzed in order to identify and assess the factors that influence farmers’ adoption of organic, conventional or integrated agriculture systems. The paper methodologically applies double-valued logistic regressions, one for each form of agriculture, to the selected sample. Results indicate that producers' training and high awareness of CAP policies are positively correlated with the future adoption of organic farming systems, while the adoption of integrated agriculture depends on producers’ age as well as their positive or negative opinions regarding the conventional agricultural system.
WEBGIS Application for Planning the Tsunami Evacuation Route

The tsunami that occurred on December 26, 2004 has resulted in massive casualties in Banda Aceh City. As one of the steps to avoid the huge casualty if the tsunami repeats itself, a number of Escape Buildings have been built in the city. In spite of the availability of the Tsunami Escape Buildings, the number of casualties can be reduced only if everyone in the tsunami-prone areas knows the nearest Escape Building from his or her position and the best route to reach the building. The purpose of this research was to build a GIS web application that can help determine the nearest Escape Building from a user-selected location and the best route to reach it. This application utilizes a web service that implements the Dijkstra algorithm to find the closest facility and the best route to the facility. The web GIS application was built using the open source API. The web GIS application developed in this research helps users find the nearest Escape Building and the best route to reach it without the need of any GIS software. The application is easy to use because it provides a user-friendly interface via a web browser.
Facial-image based Age Estimation Using Imbalanced Datasets

Facial image based human age estimation is of great application significance. The public-available facial image datasets used for age estimation suffer greatly from the uneven distribution of images of different age groups, which may lead to the low estimation accuracy of the under-sampled age categories and limit the usage of the age estimation in certain applications. We propose a three-stage probability adjustment based CNN algorithm to solve the imbalanced distribution problem of the dataset. In particular, we construct an ENIN neural network structure by applying the Network in Network (NIN) structure to the traditional convolution neural network (CNN) and use the probability vector adjustment to improve the classification accuracy of the under-sampled age categories. Then, we filter out the images with high possibility of being misclassified after the probability vector adjustment and reset their categories by comparing cosine similarity and retraining the ensembled ENIN classifier. We also introduce a population-age-distribution based accuracy metric Accuracy-P to estimate the performance of the age estimation algorithm in real-world applications. Our experimental results confirm that our algorithm can effectively improve the overall estimation accuracy by significantly improving the accuracy of the under-sampled age groups while maintaining satisfactory accuracy for the other age groups.
Maximum Power Point Tracker Based Digital One Cycle Control Applied in PV Systems

The Photovoltaic energy shown an alternative to meet the growing energy demand and reduce dependence on fossil fuels. This paper makes an approach to the need to extract the maximum power of photovoltaic panels via algorithms, such as the constant voltage. It was also used a tracking algorithm based on temperature measurement, where it returns the value of the reference voltage when a temperature change occurs. To make the switching controller was used the one cycle control OCC by presenting a higher speed of convergence, disturbance rejection of the source and no steady-state error. It was implemented in a DC-DC Boost converter to extract the maximum power of the panel and was observed that OCC is faster than PWM and the temperature algorithm was more efficient than a pure constant voltage method.
Contribution of E-Marketing for the Income Generation in Travel Agencies of Nepal

The major objective of this study is to examine the relationship between internet marketing usage and its contribution for the income generation in travel agencies of Nepal. The research is descriptive in nature and is an applied research. The research is based on quantitative data. The populations for the study are the basically owners, chairman and managers of travel agencies and the sample size consists of 120 respondents. The data for the research are collected through primary source of data collection method that includes questionnaire. The research is also carried out with the help of secondary data as we need to study related theories to make research more insightful. Descriptive as well as inferential analyses have been done for the data to be analyzed. Frequency tables are analyzed through SPSS 16.0 and are used for descriptive analysis. Chi- Square test, and Hypothesis Testing (t-test) and Correlation have been done for inferential analysis. The data analysis is of cross sectional in nature. The survey resulted that, there is a strong correlation between internet marketing uses and increase in return on equity, increase in average sales volume and general increase in company income in travel agencies of Nepal. There exists a strong correlation between increases in return on equity and reduction in operating costs, increase average sales volume and general increase in profit margin of the business. From the research, it is noted that there are several benefits obtained from incorporating the internet in marketing strategies. Therefore, the travel agencies should utilize more internet marketing methods and try to incorporate the internet in all their marketing strategies in order to fully enjoy the benefits of internet marketing. Since internet coverage is increasing through the introduction of mobile internet, marketing on the internet becomes a critical factor concerning the company income.
Automatic Multi-word Term Extraction and its Application to Web-page Summarization

Automatic Multi-word Term Extraction and its Application to Web-page Summarization Weiwei Huo Advisor: University of Guelph, 2012 Prof. Fei S ong In this thesis we propose three new word associatio n measures for multi-word term extraction. We combine these association measures w ith LocalMaxs algorithm in our extraction model and compare the results of differe nt multi-word term extraction methods. Our approach is language and domain independent and requires no training data. It can be applied to such tasks as text summarization, inf ormation retrieval, and document classification. We further explore the potential of using multi-word terms as an effective representation for general web-page summarization. We extract mult i-word terms from human written summaries in a large collection of web-pages, and g enerate the summaries by aligning document words with these multi-word terms. Our sys tem applies machine translation technology to learn the aligning process from a tra ining set and focuses on selecting high quality multi-word terms from human written summari es to generate suitable results for web-page summarization.