Miracle-dz/UniFAR-FTUs huggingface dataset:
Justification for 30 TB Storage

This storage request is in relation to our ongoing effort in training deep learning models on large datasets in non-speech audio and radar domains. We note that for this effort we have recently submitted a major OSC grant application. The effort has a critical need for storage of datasets from these domains. Specifically, these datasets are being used in and derived from the following ongoing funded projects:
Spectrum Sensing of Multiple Channels Using Multiple Sensors

Cognitive radio (CR) is a class of wireless communication technologies that have the ability to learn from the surrounding radio environment and the intelligence to adapt communication resources to enhance quality of service. The problem of acquiring information from a CRs radio environment is called spectrum sensing, which can take on many forms. In particular, this thesis concerns the determination of whether a spectrum band (or channel) is in a busy or idle state. The binary nature of a channels availability means that spectrum sensing can be cast as a hypothesis testing problem. While an abundant literature exists on spectrum sensing as a signal detection problem, this thesis treats spectrum sensing differently, and features the following elements: 1) the system is equipped with an arbitrary number of sensors; 2) sensing is performed over multiple channels; 3) each channels availability is modelled by random periods of busy and idle times corresponding to packet transmission; and 4) the optimization criteria minimizes detection delay subject to a reliability constraint. A related spectrum sensing problem formulation based on the use of a single sensor has been proposed in the recent literature. The previous research employs an optimization framework based on modeling channel uses as an on-off process via partially observable Markov decision processes (POMDP). This thesis generalizes previous results from single-sensor to multiple-sensor spectrum sensing, i.e., detecting i idle periods with multiple sensors. In addition, an alternative reduced-complexity algorithm is proposed. For both proposed detectors, the performances are evaluated based on Monte Carlo simulation with calculated confidence intervals, and the results show that 1) adding sensors generally improves the system performance by reducing detection delay (improved agility); 2) the application of previously existing quickest detection methods result in error floors complicating test design. Finally, performance assessment using a channel model derived experimentally from the wireless local area network (WLAN) traffic is conducted and compared to that obtained using a geometrically-distributed channel traffic model.
‘Vote Me Up If You Like My Ideas!’ Experiences of Learning in a MOOC

To fully understand and support learners in MOOCs we need a way of detecting and differentiating distinct learner profiles (reflecting motivations, goals, preferences for course elements and online behaviours) - particularly given the “crowd like” nature of these settings. This paper highlights some steps towards this goal. Using a range of data sources from one case study MOOC on business strategy we address three questions: 1) can we distinguish coherent profiles of learners’ interactions within a MOOC? 2) how do these interaction profiles relate to learner characteristics? And 3) what is the relationship between these interaction profiles and learners’ performance and experiences of learning within a MOOC?
Founding Act on Mutual Relations , Cooperation and Security Between NATO and the Russian Federation Arms Control Today

Paris, May 27, 1997 The North Atlantic Treaty Organization and its member States, on the one hand, and the Russian Federation, on the other hand, hereinafter referred to as NATO and Russia, based on an enduring political commitment undertaken at the highest political level, will build together a lasting and inclusive peace in the Euro-Atlantic area on the principles of democracy and cooperative security.
UNITED NATIONS ECONOMIC COMMISSION FOR EUROPE CONFERENCE OF EUROPEAN STATISTICIANS

1. During the last years, the National Bureau for S tatistics of the Republic of Moldova disaggregates a lot of data by sex and publishes ma terials with a focus on gender aspect, for instance statistical publications “Women and Men of the Republic of Moldova”; “Women and Men on the Labour Market in the Republic of Moldova ”, “Gender Statistics User Guide” etc. However, until recently, the statistical framework has not allowed for a serious research of the gender aspect in entrepreneurship development, as t he purpose of business data collection characterized enterprises, and not individuals that manage them.
Visualização de operações de junção em sistemas de bases de dados para mineração de dados.

viii Capitulo 1 9 1. Introducao 9 1.1 Objetivos do Trabalho 10 1.2 Organizacao do Trabalho 10 Capitulo 2 12 2. Conceitos 12 2.1 Descoberta de Conhecimento em Bases de Dados 12 2.2 Mineracao de Dados 14 2.2.1 Deteccao de Agrupamentos (Clustering) 15 2.2.2 Classificacao 16 2.2.3 Associacao 17 2.3 Data Warehouse 17 2.3.1 Modelo Multidimensional dos Dados 18 2.3.2 OLAP 21 2.4 Visualizacao de Dados 22 2.5 Conclusao 23 Capitulo 3 25 3. Estado da Arte 25 3.1 Descoberta de Conhecimento em Bases de Dados e Mineracao de Dados. 25 3.1.1 Pre-processamento 26 3.1.2 Reducao de Dimensionalidade 26 3.1. 3 Deteccao de Agrupamento e Classificacao 27 3.2 Visualizacao de Dados 28
A Study on Contraceptive Action

The population explosion is a global problem that poses significant threat to the quality of life, more particularly in the underdeveloped and developing countries [1]. The extraordinary growth of the world population stands as one of the significant events of the modern era to think over. The current world population is around 6.46 billion and that of India is around 1.1 billion. The United Nations ‘medium’ projection has predicted that world population, which crossed the five billion mark in 1987, will increase to 10 billion by the year 2050 [2]. One of the critical problems of the developing countries like India is its geometrical increase in human population. Today we understand that our sheer numbers have increased so much that they are straining Earth’s capacity to supply food, energy and raw materials. Advances in medicine and public health have led to a significant decrease in mortality and an increased life expectancy. This population explosion will have negative impact on our economic policies and would simultaneously misbalance our socio-economic infrastructure. Thus, the control of human fertility in the sense of its limitation is the most important and urgent of all-biosocial and medical problem confronting mankind today [3].
DATA MINING TECHNIQUES FOR EFFICIENT SOFTWARE ENGINEERING

Software Engineering is the computing field concern with designing, developing,implementing, maintaining, and modify software. SoftwareEngineering data consists of sequences, graphs, and text. Meaningful information can be extracted from the complex data using well established data miningtechniques such as association, classification, clustering etc. The potential of data mining technique on this large valuable data is to manage the software projects efficiently and to produce high-quality software systems that are delivered on time and within budget. Data mining for secure software engineering improve software productivity and quality. Recently software engineers are increasingly applying data mining algorithms for various software engineering tasks. This paper focuses on data miningtechniques for software engineering process[1]. Keywords—Software Engineering, Software Engineering goals and tasks, Data Mining, Mining Software Engineering data.
Modelling longitudinal data through matrix-variate normal mixtures

Multivariate longitudinal data occur when various attributes are measured on a sample of statistical units at different times. The collected data can be arranged in a three-way data structure characterized by rows (i.e. the statistical units),columns (i.e. the different times/occasions) and layers (i.e. the attributes). In this perspective, each observed statistical unit is a matrix of observations instead of the conventional p-dimensional vector. Clustering observational matrix units by means of a special mixture model is proposed. An explicit assumption of this approach is that the total variability arises from the compound of the ’within’ variability of the attributes and the variability ’between’ the different times, which explicitly accounts for the relationship between measurements at different time points through a modified Cholesky decomposition. The effectiveness of the proposed method is illustrated on longitudinal data from the Health and Retirement Study (HRS) study.
Modeling of Slovak Language for Broadcast News Transcription

The paper describes recent progress in the development the Slovak language models for transcription of spontaneous speech such as broadcast news, educational talks and lectures, or meetings. This work extends previous research oriented on the automatic transcription of dictated speech and brings some new extensions for improving perplexity and robustness of the Slovak language models trained on the web-based and electronic language resources for being more precise in recognition of spontaneous speech. These improvements include better text preprocessing, document classification, class-based and filled pauses modeling, web-data augmentation and fast model adaptation to the target domain. Experiments have been performed on the four different evaluation data sets, including judicial and newspaper readings, broadcast news recordings and parliament proceedings with the Slovak transcription system. Preliminary results show significant decrease of the word error rate for multiple transcription system configurations of acoustic and language models.
Overview of Emerging IEEE 802 . 11 Protocols for MAC and Above

During the last few years, the IEEE 802.11 Wireless LAN (WLAN) has become a dominant technology for the (indoor) broadband wireless networking. Along with its success, there have been demands to enhance the performance of the 802.11. To meet such needs, the IEEE 802.11 Working Group (WG) has been developing new protocols to amend the existing protocols. In this paper, we overview the emerging protocols of the IEEE 802.11 WLAN for the medium access control (MAC) layers and above. These include 802.11e for quality-of-service (QoS), 802.11f for Inter-Access Point Protocol (IAPP), 802.11h for spectrum management at 5GHz, 802.11i for security enhancement, 802.11k for radio resource measurement, and finally 802.11m for higher throughput.
Fast iterative reconstruction: a comparison of hardware-accelerated projection techniques

The availability of parallel computing platforms with increasing capabilities has led to a renaissance of iterative reconstruction algorithms in computed tomography. The forward (and back) projection being the most computational intensive part exists in a variety of methods, offering different trade-offs between accuracy and computational cost. GPU-accelerated implementations of two categories of projection methods are investigated in this work: voxel-based and blob-based. The focus lies on assessing their performance for non-destructive testing applications in the domain of high-perfomance materials for energy conversion.
MAPPING OF QUANTITATIVE TRAIT LOCI ASSOCIATED WITH CASSAVA MOSAIC DISEASE ( CMD ) RESISTANCE

Cassava mosaic disease (CMD) is commonly considered as the most damaging disease constraint to cassava production in Africa. Identifying and breeding host-plant resistant to CMD is the most significant methods to counteract the disease and only strategy to offset the serious effect of the virus in cassava production. In this research, some landraces have been identified which exhibit high levels of resistance to CMD. However, molecular markers associated with resistance to CMD in a resistant landrace were identified, F1 progenies derived from a cross between the CMD resistant landrace TMS961089A and the susceptible line TMEB117 was initiated using 4394 SNP markers out of the 5272 derived polymorphic SNP markers from GBS were subjected to quantitative trait loci (QTL) for CMD resistance. The results revealed a single locus for CMD resistance with a strong peak LOD of 25.664cM and explaining 60% of the phenotypic variation identified on LG 15 of the map. The parent TMS961089A and nearly half of the FI progenies were resistant to CMD severity.
THE LEVEL OF FIDELITY IN RESTORATIVE PRACTICES AND ITS IMPACT ON SCHOOL-LEVEL DISCIPLINE DATA IN MIDDLE

iii Dedication. iv Acknowledgements. v List of Tables. vi Chapter 1: Introduction. 9 Background of Study. 10 Statement of the Problem. 11 Purpose of the Study. 11 Significance of the Study. 13 Theoretical Foundation. 13 Research Questions. 15 Limitations and Delimitations. 15 Definitions of Terms. 16 Organization of the Document. 17 Chapter 2: Literature Review. 18 History of Discipline. 18 Elements Impacting Student Behavior. 21 Zero Tolerance. 28 Restorative Justice. 31 Implementation. 40 Restorative Practice in Schools. 46 Chapter 3: Methodology. 53
A Unique Cipher Mechanism Integrating Steganography with Traditional Cryptography

Now a days most of the information is transmitted over the network whether personnel or professional and concept of electronic money is also in trend, bank transaction or online shopping are more in trend then before so we need to make the network more secure. Traditional cryptographic mechanisms are more vulnerable to attacks now as they are well known to attackers and many new forms of attacks are being developed exploiting their loop holes so we need to design new methods of encrypting or hiding data to make transactions more secure. Here we have developed a cipher mechanism where we can hide a gray scale image in RGB component of a colored image in random manner which adds an element of uncertainty and original and watermarked images are almost similar so it’s very difficult to predict any information hidden in the image.
Co-precipitation Synthesized MnOx-CeO2 Mixed Oxides for NO Oxidation and Enhanced Resistance to Low Concentration of SO2 by Metal Addition

NO oxidation was conducted over MnOx-CeO2 catalysts, which were synthesized by the co-precipitation method. The calcination temperature and third metal doping were the main considerations. MnCe catalysts calcined at 350 °C and 450 °C attained the highest NO conversion efficiency, compared to 550 °C. XRD results suggested that the higher the calcination temperature, the higher the crystallization degree, which led to a negative effect on catalytic activity. Subsequently, Sn, Fe, Co, Cr, and Cu were separately doped into MnCe composites, but no improvement was observed for these trimetallic catalysts in NO conversion. Nevertheless, MnCeSn, MnCeFe, and MnCeCo still exhibited a desirable NO conversion efficiency, so they were tested under SO2 addition together with MnCe catalyst. Among them, MnCeFe exhibited the highest NO conversion after whole poisoning testing. XPS results indicated that Fe could protect Mn and Ce metal oxides from being reduced during SO2 poisoning process. Furthermore, in in-situ DRIFTS measurement, part of nitrate species maintained undestroyed on the MnCeFe catalyst surface after SO2 poisoning. These characteristics reinforced that Fe dropping would achieve better performance under SO2 atmosphere.
Using boiling for treating waste activated sludge

In this work we investigated the feasibility of using short time, low superheat boiling to treat biological sludge. The treated sludge exhibited reduced filterability and enhanced settleability. The boiling treatment released a large amount of extra cellular polymers (ECPs) from the solid phase and reduced the microbial density levels of the total coliform bacteria and the heterotrophic bacteria. A diluted sludge is preferable for its high degree of organic hydrolysis and sufficient reduction in microbial density levels. 
The Influence of Satisfaction on Job Outcomes among Foreign and local Academic Staff at a Selected International University

South Africa is experiencing high turnover rates among academics in higher education institutions. Establishing the antecedents that retain and attract academics in the field of Tourism and other related disciplines is crucial in attracting and retaining these academics. The study sought to investigate the impact of job satisfaction and selected demographic variables on turnover intentions among academic staff members. In the study, a cross-sectional quantitative research design was adopted. Data was collected from a population of 102 local and international academics. SPSS 23 used for data analysis. Pearson’s correlation was conducted to establish the cause-and effect relationship between the independent and dependent variables in the study. Results indicated that there was a significant positive relationship between job satisfaction and turnover intentions. In this respect, it was evident that although the academics were satisfied with their jobs they still had intentions to leave their jobs. On the other hand it was noted that there was a statistically significant negative relationship between job level and turnover intentions. In this vein, junior staff members were noted to have high levels of turnover intentions. The main implications established in the study were that higher education institutions should use several human resource interventions such as employee engagement and work-life balance to ensure that they retain academics.
Optimized s-curve motion profiles for minimum residual vibration

A method for developing optimized point-to-point motion profiles to achieve fast motions with minimum vibration is presented. The proposed approach uses the well-known s-curve motion profiles, but optimizes the selection of the ramp-up (and ramp-down) time. The selection of ramp-up time is based on a frequency analysis that minimizes the excitation energy of the input forcing function at the system natural frequency. Simulation results on a lightly-damped system undergoing point-to-point motions demonstrate that the proposed approach decreases residual vibration by almost an order of magnitude over other approaches, even when the actual natural frequency is in error by 10%.
Reducing the template ageing effect in on-line signature biometrics

On-line signature recognition is an area of growing interest in recent years due to the massive deployment of high-quality digitising tablets, smartphones, and tablets in many commercial sectors such as banking. In addition, handwritten signature is one of the most socially accepted biometric traits as it has been used in financial and legal agreements for over a century. In this current environment for signature biometrics, the number of stored samples or templates per user can grow very fast, making it possible to train more robust statistical user models, improving the performance of the biometric systems and in particular reducing the template ageing effect. This study carries out an exhaustive experimental analysis of template update strategies for three well-known on-line signature verification approaches, extracts various practical findings related to the template ageing effect in signature biometrics, and configures time-adaptive improved versions of the considered baseline approaches overcoming to some extent the template ageing. The proposed improved approach achieves system performances of 2.1 and 0.2% equal error rate for skilled and random forgery cases, respectively. These results show the efficacy of the proposed methodology.
Topic Modelling for Short Text

Over the past few years, our increased ability to store large amounts of data, coupled with the increasing accessibility of the internet, has created massive stores of digital information. Consequently, it has become increasingly challenging to find and extract relevant information, thus creating a need for tools that can effectively extract and summarize the information. One such tool, is topic modelling, which is a method of extracting hidden themes or topics in a large collection of documents. Information is stored in many forms, but of particular interest is the information stored as short text, which typically arises as posts on websites like Facebook and Twitter where people freely share their ideas, interests and opinions. With such a wealth in data and so many diverse users, such stores of short text could potentially provide useful information about public opinion and current trends, for instance. Unlike long text, like news and journal articles, one of the commonly known challenges of applying topic models on short text is the fact that it contains few words, which means that it may not contain sufficiently many meaningful words. The Latent Dirichlet Allocation (LDA) model is one of the most popular topic models and it makes the generative assumption that a document belongs to many topics. Conversely, the Multinomial Mixture (MM) model, another topic model, assumes a document can belong to at most one topic, which we believe is an intuitively sensible assumption for short text. Based on this key difference, we posit that the MM model should perform better than the LDA. To validate this hypothesis we compare the performance of the LDA and MM on two long text and two short text corpora, using coherence as our main performance measure. Our experiments reveal that the LDA model performs slightly better than the MM model on long text, whereas the MM performs better than the LDA model on short text.
Experimental and Simulation Identification of Xanthohumol as an Inhibitor and Substrate of ABCB 1

Xanthohumol (XN) is a well-known prenylated flavonoid found in Humulus lupulus L. It is involved in several pharmacological activities, including the sensitization of doxorubicin-resistant breast cancer (MCF-7/ADR) cells to doxorubicin (DOX) through a reduction in cell viability and stemness. In the present study, we revealed another mechanism to further explain the reverse of the drug resistance of XN. In the MCF-7/ADR cell line, we found that XN inhibited the efflux functions of ATP-binding cassette subfamily B member 1 (ABCB1). We also observed that XN was a substrate of ABCB1 and stimulated its ATPase activity. Moreover, our results revealed that XN showed a synergic effect with the ABCB1 substrate colchicine (COL) in the MCF-7/ADR cell line. Further, we showed that XN bound to the central transmembrane domain (TMD) site, overlapping with the DOX binding site. This mechanism was supported by molecular modeling and simulation data, which revealed that XN bound to the ABCB1 transmembrane domain, where doxorubicin also binds, and its binding affinity was stronger than that of doxorubicin, resulting in less protein and ligand position fluctuation. These results support the XN-induced reversal of drug resistance via the inhibition of ABCB1-mediated transport of doxorubicin, stimulating ABCB1 ATPase activity and acting as a substrate of ABCB1.