Miracle-dz/UniFAR-FTUs huggingface dataset:
Kajian terhadap pengajaran dan pembelajaran berasaskan teknologi kursus pengurusan perniagaan di Kolej Vokasional

Rangka Pelan Pembangunan Pendidikan Malaysia (2013-2025), menekankan kepada pembangunan modal insan yang kreatif dan inovatif agar menjadi pemangkin kepada transformasi negara. Sehubungan itu inovasi dalam bidang pengurusan perniagaan perlu diperbaharui dari semasa ke semasa bagi memenuhi keperluan pembangunan negara abad ke-21. Perubahan yang berlaku menyebabkan proses mendidik menjadi lebih kompleks. Pensyarah di pusat pengajian tinggi merupakan ejen utama dalam pendidikan perlu lebih berinovasi dalam pengajaran dan pembelajaran untuk menghasilkan modal insan yang kreatif dan berinovatif bagi mencapai hasrat negara. Oleh itu, kajian ini bertujuan untuk menentukan hubungan antara tahap inovasi dengan amalan pengajaran dan pembelajaran berasaskan teknologi dalam kalangan pensyarah Pengurusan Perniagaan di Kolej Vokasional di Malaysia. Pengumpulan data secara soal selidik melibatkan 198 pelajar yang memberi kenyataan terhadap penyarah mereka yang mengajar kursus Pengurusan Perniagaan. Perisian Statistical Package for Sosial Science Version 24.0 for Windows (SPSS) digunakan untuk menganalisis data dan Analisis Korelasi Persorn-Product Moment digunakan untuk mengukur hubungan antara pembolehubah yang dikaji. Dapatan kajian menunjukkan tahap inovasi pensyarah Kolej Vokasional dalam pengajaran dan pembelajaran adalah pada tahap tinggi (min= 3.88, sisihan piawai= .48) dan amalan pengajaran dan pembelajaran berasaskan teknologi pensyarah Kolej Vokasional juga berada pada tahap tinggi (min = 4.00, sisihan piawai = .33). Manakala hubungan positif yang rendah antara amalan pembelajaran berasaskan teknologi dengan tahap inovasi pensyarah Pengurusan Perniagaan Kolej Vokasional di Malaysia.
Key Node Oriented Black Hole Attack and Detection

The key node oriented black hole attack means once the attack node is distributed near the key nodes in the network, it will do serious damage to the network performance. Thus, in this paper, based on implementing network key node recognition by computing the reputation value, propose the detection method of the key node oriented black hole attack. NS2 simulation results show that the key node oriented black hole attack on network have greater impact on packet loss rate and delay than the key node non-oriented attack, and compared the detection method of the key node non-oriented attack, the proposed detection method can be more efficient detection for the black hole attack to key nodes.
FAEJ3D A File Attribute Explorer in Java3D

Information visualization has many advantages compared to a pure textural representation, but why and how can information visualization be used for a certain goal? This thesis gives an overview of the physiological and psychological aspects of seeing and perceiving data. Some popular approaches in information visualization are described. A prototype file attribute visualizer was implemented in Java3D. The File Attribute Explorer in Java3D (FAEJ3D) displays files as glyphs in 3D space with extrinsic and intrinsic mappings. The user can freely explore the space and change mappings dynamically. A secondary aim was to investigate the suitability of Java3D to such information visualizations. In this regard, it can be said that Java3D still needs to improve, in particular with regard to efficiency and bugs.
Water balance and water productivity of Japanese mint (Mentha arvensis) as affected by irrigation and mulching

A field experiment was conducted during the spring season (March to July) of 2014 and 2015 at the Punjab Agricultural University, Ludhiana, in sandy-loam soil, to study the effect of irrigation and mulching regimes on field water balance and water productivity of Japanese mint (Mentha arvensis L.). Mean seasonal soil-moisture storage and transpiration were significantly more in flood irrigation and drip irrigation at 120% of evapotranspiration than drip irrigation at 100% and 80% of evapotranspiration during both years of the experiment. Mulching @ 6 t/ha and 3 t/ha resulted in an increase of 8.50% and 7.19% mean seasonal transpiration and a decrease of 33.97% and 22.86% mean seasonal evaporation as compared to the treatment where no mulch was applied. Water productivity (l/ha-mm) was significantly higher in flood irrigation (0.36) and drip irrigation at 120% of evapotranspiration (0.38) as compared to drip irrigation at 100% (0.32) and drip irrigation at 80% of evapotranspiration (0.32). Compared to flood irrigation, drip irrigation at 80%, 100% and 120% of evapotranspiration saved about 60%, 52% and 44% of irrigation water respectively. The highest net returns (73, 140/ha) and benefit: cost ratio (2.17) were recorded in drip irrigation at 120% of evapotranspiration compared to other irrigation regimes. Mulching @ 6 t/ha also recorded the highest net returns and benefit: cost ratio (64, 580/ha and 2.06).
Informed Structural Priors for Bayesian Networks: Applications in Molecular Biology Using Heterogeneous Data Sources

The main goal of this thesis is to investigate ways in which a collection of separate prior information sources, of varying scale and reliability, can be integrated in a principled manner to reveal a more complete understanding of a problem domain. We consider a case study from the biological domain: inferring a network of gene interactions. Biology has a rich and diverse body of knowledge already available upon which to leverage the task of learning such a model; the challenge is in developing meaningful ways to encode such prior knowledge which take into account the heterogeneity of the individual prior data sources. We consider a host of 'experts' which specify explicit or implicit interactions between genes, such as physical interaction between the genes, shared biological function or even co-occurrence of literature references. Though each individual source may be a poor indicator for gene interaction when used alone, we demonstrate techniques for combining the experts to formulate a consensus belief or likelihood that any two genes are related. The resulting probability distributions of gene relationships are then used in two distinct ways. The first is in providing structural priors for learning Bayesian Networks. We show how using a prior distribution over interactions between genes can significantly increase the speed and quality of search for high scoring Bayesian Networks when learning from gene expression data. Our studies make use of simulated data from a model of ICU ventilator management (ALARM), a benchmark for Bayesian Network learning, as well as real-world biological data from the Yeast genome. The second application uses the consensus priors over gene relationships to create visualization tools for large scale datasets. We provide results for examples in both Yeast and Mouse which demonstrate how a collection of weakly suggested or sometimes unreliable relationships can be combined to create a powerful and useful working model of interactions, allowing biologists to better understand an overwhelming amount of information.
Fast data analysis framework for scientific big data applications

Scientific breakthroughs are increasingly powered by advanced computing and data analysis capabilities. The data-driven scientific discovery has become the new fourth paradigm of scientific innovation after theory, experiment, and simulation driven innovations. The data-driven scientific discovery is based upon advanced high performance computing (HPC) that traditionally powers simulation driven research and further requires processing massive amounts of datasets. Revealing and exploring the interesting knowledge hidden inside scientific datasets faces critical challenges and the problem is beyond the capability of traditional HPC software systems. Not only the existing data and computing model, but also the runtime and the storage architecture in the HPC, need to be revisited to meet the “big data” challenges. The fundamental issue is the data movement that often dominates the overall analysis performance and the execution time. To optimize and speedup the discovery process, this dissertation research studies the scientific workflow and designs a Fast Data Analysis Framework, that builds a top-down high performance computing framework with a focus on reducing the data movement and boosting the scientific big data processing. This framework has a newly designed Statistical Data model with integrated statistics and subsetting schemes to speedup the query analysis. The framework also has an In-Advance Computing model, which is designed to better support generic scientific analysis routines. This computing model has a flexible two-level design, with a coarse-grain level that performs optimizations at the analysis operation level and a fine-grain level that moves computations in-advance to produce partial analysis results on the incomplete I/O (input/output) streams. The two-level model can work independently and coherently to utilize computation in-advance to reduce the required data movement. The framework further supports a Hierarchical Runtime Scheduling, which considers both the storage side I/O queues and the client side data redistribution, and an Automatic Storage Reorganization, which allows maintaining multiple data layouts and automatically redirecting the I/O to better layouts. These designs and developments provide novel algorithms to optimize the data movement in both runtime and file systems. The evaluation results confirm that the Fast Data Analysis framework improved and optimized scientific big data applications. It can have an impact on the the design and development of future infrastructures, algorithms, and systems for data-driven scientific discovery paradigm.
Brain computer interface: studio, evoluzione ed applicazione dell’interfaccia neurale

This research aims to illustrate the progress in the field of Artificial Intelligence, in particular with an examination of the Brain Computer Interface (BCI), also known as Neural Interface. These devices, which have a distinctly interdisciplinary character, binds primarily the medical engineering field with that of neuro - cognitive research and have as main objective the rehabilitation or better support for therapeutic purposes with the so-called lock-in patients. First a brief focus on the neural interface story was outlined, with attention to early studies and recent developments that have characterized this protocol. Then we moved to the clarification of the neuro - psychological components that form the basis of a Neural Interface and therefore, detection systems and the particular brain frequencies that activate a BCI will be analyzed. Finally, we’ll discuss some of the most recent studies which have guided the BCI to new scope for development
If journals embraced conditional equivalence testing, would research be better?

We consider the reliability of published science: the probability that scientific claims put forth are true. Low reliability within many scientific fields is of major concern for researchers, scientific journals and the public at large. In the first part of this thesis, we introduce a publication policy that incorporates “conditional equivalence testing” (CET), a two-stage testing scheme in which standard nullhypothesis significance testing is followed, if the null hypothesis is not rejected, by testing for equivalence. The idea of CET has the potential to address recent concerns about reproducibility and the limited publication of null results. We detail the implementation of CET, investigate similarities with a Bayesian testing scheme, and outline the basis for how a scientific journal could proceed to reduce publication bias while remaining relevant. In the second part of this thesis, we consider proposals to adopt measures of “greater statistical stringency,” including suggestions to require larger sample sizes and to lower the highly criticized “p < 0.05” significance threshold. While pros and cons are vigorously debated, there has been little to no modeling of how adopting these measures might affect what type of science is published. We develop a novel model that, given current incentives to publish, predicts a researcher’s most rational use of resources in terms of the number of studies to undertake, the statistical power to devote to each study, and the desirable pre-study odds to pursue. Using this model, we investigate the merits of adopting measures of “greater statistical stringency” with the goal of informing the ongoing debate. We also use this model to investigate the merits of alternative publication policies, including the registered reports policy and our novel CET publication policy.
Design of Low-Profile Multi-Band Half-Mode Substrate-Integrated Waveguide Antennas

An effective method of designing low-profile cavity-backed multi-band antennas is introduced based on half-mode substrate-integrated waveguide (HMSIW) technique. By loading U-shaped strips outside the radiating aperture of an HMSIW cavity, additional resonances can be produced, thus leading to the realization of multi-band capability. An inset $50~\Omega $ microstrip feedline is employed to energize the HMSIW cavity, whereas the loaded strips are excited through proximity coupling from the radiating aperture of the HMSIW. The proposed antenna is constructed on a single-layered substrate with a low profile of 0.024 free-space wavelength. To validate the multi-band responses, prototypes of the single-band HMSIW antenna as well as dual-band and triple-band antennas are fabricated and measured, respectively. The experimental results show that the proposed antennas possess advantages of stable radiation patterns with a gain of 5–5.9 dBi as well as acceptable front-to-back (FTB) ratio of larger than 10 dB over different frequency bands.
Universal learning network-based fuzzy system and its application to nonlinear control system

The universal learning network (ULN) which can be used to model and control the large-scale complicated systems in the network framework has been presented. In this paper, a method to construct fuzzy model with multidimension input membership function using ULN is presented. The fuzzy model under the framework of ULN is called universal learning network-based fuzzy inference system (ULNFIS), which possesses certain advantages over other networks such as neural networks. It is also introduced how to imitate a real system with ULN and how to construct a control scheme using ULNFIS. Simulations are carried out in order to compare the performance of fuzzy control and neural network control by using ULNFIS. It has been shown that the fuzzy control has better performance for generalization capability than the neural network control.
ADAPTIVE CONTENT BASED TEXTUAL INFORMATION SOURCE PRIORITIZATION

The world-wide-web offers a posse of textual information sources which are ready to be utilized for several applications. In fact, given the rapidly evolving nature of online data, there is a real risk of information overload unless we continue to develop and refine techniques to meaningfully segregate these information sources. Specifically, there is a dearth of content-oriented and intelligent techniques which can learn from past search experiences and also adapt to a user's specific requirements during her current search. In this paper, we tackle the core issue of prioritizing textual information sources on the basis of the relevance of their content to the central theme that a user is currently exploring. We propose a new Source Prioritization Algorithm that adopts an iterative learning approach to assess the proclivity of given information sources towards a set of user-defined seed words in order to prioritise them. The final priorities obtained serve as initial priorities for the next search request. This serves a dual purpose. Firstly, the system learns incrementally from several users' cumulative search experiences and re-adjusts the source priorities to reflect the acquired knowledge. Secondly, the refreshed source priorities are utilized to direct a user's current search towards more relevant sources while adapting also to the new set of keywords acquired from that user. Experimental results show that the proposed algorithm progressively improves the system's ability to discern between different sources, even in the presence of several random sources. Further, it is able to scale well to identify the augmented information source when a new enriched information source is generated by clubbing existing ones.
Measuring ARTSE2017: Results from Wyoming and New York

The 21 August 2017 total solar eclipse was the first total eclipse on the mainland of the United States since 1979. The Atmospheric Responses of 2017 Total Solar Eclipse (ARTSE2017) project was created to observe the response of the atmosphere to the shadow of the moon. During the eclipse, 10 sites launched radiosondes in a very rapid, serial weather balloon deployment along the totality path, and high-resolution mesoscale meteorological network (mesonet) data were collected in three states. Here, we focus on the results obtained from the radiosonde field campaign in Fort Laramie, Wyoming, and the New York State Mesonet (NYSM). In Fort Laramie, 36 people from 13 institutions flew 19 radiosondes and launched 5 large balloons carrying video payloads before, during, and after the eclipse while continuously recording surface weather data. Preliminary analysis of the radiosonde data provided inconclusive evidence of eclipse-driven gravity waves but showed that the short duration of darkness during totality was enough to alter boundary layer (BL) height, the lowest layer of the atmosphere, substantially. The statewide impact of the partial eclipse in New York State (NYS) was observed for solar radiation, surface temperature, surface wind, and surface-layer lapse rate using NYSM data. Importantly, the radiosonde and mesonet data collected during the eclipse will be available for public access. ARTSE2017 also focused on education, including students from all demographics (undergraduate and K–12) and the general public. Finally, we summarize goals accomplished from leveraging resources for education, research, and workforce development on undergraduate students from a variety of fields.
Low bit and variable rate speech coding using local cosine transform

An average1.6kb/s low bit and variable rate speech coder based on local cosine transform (LCT) algorithm for a two-way conversational speech is designed for the first time in the paper. The result of the voice activity detector (VAD) based on support vector machine (SVM) and the classification method of the voicing modes of the GSM half rate standard for active speech are adopted in the design of the variable bit rate coder. The moderately voiced mode and the strongly voiced mode of the voicing modes are combined as a voicing mode, the new combined voicing mode is named as a moderately and strongly voiced mode. A few segment vector quantizers of the LCT coefficients for each voicing mode and silence voicing frame (background noise) are employed, and LGB algorithm is applied to design the codebooks. A tree fast search technique is used to select the vector of the LCT coefficients for each segment. The evaluation using subject informal listening tests and a few object parameters indicates that the speech quality (intelligibility and naturalness) of the designed speech coder is better than that of the FS1015 standard coder. The new coder has higher robust than the FS1015 standard coder, which is suitable for speech coding in any environments.
Recognition of Underlying Causes of Power Quality Disturbances Using Stockwell Transform

This paper presents an effective and intelligent methodology for the recognition of several underlying causes responsible for power quality (PQ) disturbances. The disturbance signals at a 40-dB signal-to-noise ratio (SNR) from various locations of the power distribution system have been analyzed with computationally efficient Stockwell transform (ST). Besides the residual voltage and the duration, characteristics like automatic segmented phase angle jump (PAJ) have been extracted and proposed for linking the PQ disturbance with the respective underlying cause. PQ disturbances have been simulated in a MATLAB environment as per the IEEE-1159 standard with five causes, namely symmetrical fault, unsymmetrical fault, induction motor starting, capacitor bank energizing, and transformer energizing. A block scheme has been devised and presented here for the effective recognition of the underlying cause in a power distribution network so that corrective measures can be proposed for the trouble-free operation of the equipment. The performance of the proposed method has also been evaluated with the real-time PQ data to judge the effectiveness of the algorithm in the real environment.
An efficient and refined minimum spanning tree approach for large water distribution system

For the past several years, large graph mining applications have been used for a wide range of analyses. One such application is a water distribution system (WDS). WDSs are networked with both topological and behavioural complexity since it comprises of a large number of pipes and junctions. Therefore, it becomes highly expensive to design such large WDSs. Hence, in this paper, we suggest to construct a reduced WDS network with minimum cost by applying a minimum spanning tree (MST) approach. However, it becomes difficult to apply traditional MST algorithms to large graphs because they have a time complexity of O(N2log N). Therefore the authors propose a divide-and-conquer mechanism for finding an approximate MST. The proposed approach shows the novelty of the algorithm by reducing the time complexity to O(N1.5log N). Experimental results show that the proposed approach had performed well and fit the expected results for both synthetic and real-world data.
On the discrepancy between the objective and risk neutral densities in the pricing of European options

A technique known as calibration is often used when a given option pricing model is fitted to observed financial data. This entails choosing the parameters of the model so as to minimise some discrepancy measure between the observed option prices and the prices calculated under the model in question. This procedure does not take the historical values of the underlying asset into account. In this paper, the density function of the log-returns obtained using the calibration procedure is compared to a density estimate of the observed historical log-returns. Three models within the class of geometric Lévy process models are fitted to observed data; the Black-Scholes model as well as the geometric normal inverse Gaussian and Meixner process models. The numerical results obtained show a surprisingly large discrepancy between the resulting densities when using the latter two models. An adaptation of the calibration methodology is also proposed based on both option price data and the observed historical log-returns of the underlying asset. The implementation of this methodology limits the discrepancy between the densities in question.