Miracle-dz/UniFAR-FTUs huggingface dataset:
Similarity by diverting supervised machine learning — Application to knowledge discovery in multimedia content

Knowledge discovery is the task of extracting new information from large databases, such as recurrent patterns or structural cues. In this framework, cluster analysis refers to the sub-domain dealing with partitioning a given data space such that two samples in the same cluster are similar, while those in di fferent ones are not. Clustering algorithms exploit an input similarity measure on the samples, which should be fi ne-tuned with the data format and the application athand. However, manually de ning a suitable similarity measure is a difficult task in case of limited prior knowledge or complex data structures for example.The purpose of this internship is to investigate an approach for automatically building such a measure by taking advantage of the discriminative abilities of state-of-the-art classi cation techniques. While classi cation systems usually require a set of samples annotated with their ground-truth classes, recent work has shown it is possible to exploit classi ers trained on an arti cial annotation of the data in order to induce a similarity measure. In this report, after introducing related scienti fic background, we propose a uni fied framework, SIC (Similarity by Iterative Classi cations), which explores the idea of diverting supervised learning for automatic similarity inference. We study several of its theoretical and practical aspects. We also implement and evaluate SIC on three tasks of knowledge discovery on multimedia content. Results show that in most situations the proposed approach indeed bene fits from the underlying classi er's properties and outperforms usual similarity measures for clustering applications.
Clustering of Laser Scanner Perception Points of Maize Plants

The goal of this work was to cluster maize plants perception points under six different growth stages in noisy 3D point clouds with known positions. The 3D point clouds were assembled with a 2D laser scanner mounted at the front of a mobile robot, fusing the data with the precise robot position, gained by a total station and an Inertial Measurement Unit. For clustering the single plants in the resulting point cloud, a graph-cut based algorithm was used. The algorithm results were compared with the corresponding measured values of plant height and stem position. An accuracy for the estimated height of 1.55 cm and the stem position of 2.05 cm was achieved.
A Labelling Technique Comparison for Indexing Large XML Database

The flexibility nature of XML documents has motivated researchers to use it for data transmission and storage in different domains. The hierarchical structure of XML documents is an attractive point to be researched for processing a user query based on labelling where each label describes the node structure in the tree. In this study, three categories of XML node labelling will be analysed to address the open problem of each category. A number of experiments are executed to compare performance of time execution and storage space required for labelling XML tree.
Correlation between PSNR and size ratio in audio steganography

Steganography is a form of security technique through obscurity; the science and art of hiding the existence of a message between sender and intended recipient. Steganography has been used to hide secret messages in various types of files, including digital images, audio and video. The three most important parameters for audio steganography are imperceptibility (indicated as PSNR), payload (bit rate or capacity), and robustness. Any technique which tries to improve the payload or robustness should preserve imperceptibility. The noise which is introduced due to bit modification would limit payload. This paper presents the correlation between PSNR and message to host size ratio that is calculated in an experimental way.
Qualitative Spatio-temporal Reasoning Based Group Activity Recognition

Human group activity recognition is still a challenging task in computer vision. However, most of the works focus on the feature extraction and the analysis of the motion trajectories for recognizing the multi-person or group activities in surveillance videos. A qualitative spatio-temporal relation based on Hidden Markov Model (HMM) method is proposed to classify human group activities. We first propose Unified QTCB relations to represent the relations of the group. And then Unified QTCB relation based on HMM is proposed for group activity classification. Experiments are successfully conducted on the human group activity video database, and the performance of our approach is evaluated and compared with some other methods. The results show that our approach is more suitable for recognizing group activities.
PENGEMBANGAN MULTIMEDIA INTERAKTIF BERMUATAN MATERI MIKROBIOLOGI BERBASIS EDMODO ANDROID

A more interactive and fun microbiology learning solution by utilizing information technology is the development of interactive multimedia of microbiological material based on Edmodo Android. The purpose of this research is to produce interactive multimedia microbiology based on Edmodo Android. The research method used is research and development. Interactive multimedia microbiology based on Edmodo Android developed with Thiagarajan development model. The research is done through define stage, design stage, and develop stage. The data of the research are validation questionnaire from media experts, microbiology material experts, and practitioners. The research data is corrected by using the percentage of eligibility criteria.
Cathode Side Engineering to Raise Holding Voltage of SCR in a 0.5-㎛ 24 V CDMOS Process

A set of novel silicon controlled rectifier (SCR) devices’ characteristics have been analyzed and verified under the electrostatic discharge (ESD) stress. A ring-shaped diffusion was added to their anode or cathode in order to improve the holding voltage (Vh) of SCR structure by creating new current discharging path and decreasing the emitter injection efficiency (γ) of parasitic Bipolar Junction Transistor (BJT). ESD current density distribution imitated by 2- dimensional (2D) TCAD simulation demonstrated that an additional current path exists in the proposed SCR. All the related devices were investigated and characterized based on transmission line pulse (TLP) test system in a standard 0.5-μm 24 V CDMOS process. The proposed SCR devices with ring-shaped anode (RASCR) and ring-shaped cathode (RCSCR) own higher Vh than that of Simple SCR (S_SCR). Especially, the Vh of RCSCR has been raised above 33 V. What’s more, their holding current is kept over 800 mA, which makes it possible to design power clamp with SCR structure for on chip ESD protection and keep the protected chip away from latch-up risk.
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF ARTS IN ENGLISH AS A SECOND LANGUAGE

The literature and research on the interaction between fantasies and depression is extremely limited and there has been no research on the interaction of these two forces during adolescence in females. The two basic possibilities that fantasies may contribute to depression or help to alleviate it are offered. The study presented utilizes a case study approach from a Narrative perspective. Multiple functions of fantasy are revealed, contributing to a complex interaction between fantasy and depression. There is also interplay between fantasy and reality that provides the basis for the therapeutic potential of fantasy.
WEB MINING TASKS AND TYPES: A SURVEY

In recent years the growth of the World Wide Web has exceeded all expectations. Today there are several billions of HTML documents, pictures and other multimedia files available via internet and the number is still rising.As a large and dynamic information source that is structurally complex and ever growing, the World Wide Web is fertile ground for data-mining principles, or Web mining. In 1996 it’s Etzioni who first coined the term web mining. Etzioni starts by making a hypothesis that information on web is sufficiently structured and outlines the subtasks of web mining. Web mining is a very hot research topic which combines two of the activated research areas: Data Mining and World Wide Web. The Web mining research relates to several research communities such as Database, Information Retrieval and Artificial Intelligence. Web mining basically can be divided into three categories: web content mining, web structure mining and web usage mining, these three categories deal with different features of a web page, web content mining deals with discovering useful information or knowledge from web page contents, web structure mining deals with discovering and modelling the link structure of web, web usage mining is used to discover interesting usage patterns from web data. This paper is a survey paper which explains in detail the concepts of web mining focusing on tasks and types of web mining.
Statistical reduction of EEG data

Analyzing multi-channel EEG can be a daunting chore due to the volume of information that must be examined. For this reason, it is often more productive to reduce the data set by limiting the analysis to certain features or by selecting a subset of the data according to a certain criteria. One traditional method is to use frequency domain techniques to focus upon certain aspects of the data. However, when the frequency domain is used to analyze EEG data, the temporal domain features are lost. In this paper we examine an alternative method of data reduction and channel grouping using a statistical approach which retains time information.
The origins, development and evaluation of Mathematics support services

This article is an introductory overview of the recent expansion in the development and provision of mathematics support services at third level. In the last ten years the establishment of Mathematics Support and Learning Centres has increased significantly in Ireland and the UK. Most third level institutes in Ireland now supply some level of mathematics support. We will discuss the development of these supports as well as the reasons why third level institutes have decided to introduce them. We also give an overview of how these services are evaluated and the impact that they appear to have on student retention and performance. [ABSTRACT FROM AUTHOR]; Copyright of Bulletin of the Irish Mathematical Society is the property of IMS Bulletin and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Feasibility investigation of cognitive radio systems

Efficient use of the scarce radio spectrum is important for accommodating the rapid growth of wireless communications. This thesis investigates the feasibility of Cognitive Radio (CR) to exploit unoccupied frequencies for an enhanced spectral utilisation. CR refers to a wireless architecture that enables dynamic spectrum access, where unlicensed devices are allowed to operate in temporally/spatially unused licensed channels. Extensive spectral opportunities have been identified from an intensive measurement study, indicating that a significant gain in spectral utilisation may be brought by a CR technology. The main challenge for CR is a robust protection mechanism to guarantee an adequate Licensed Users (LU) system performance at all times. This requires a reliable spectrum sensing technique to accurately identify frequency opportunities; and an autonomous transmit power control algorithm that remains effective in severe fading environments. Cooperation among CR devices to assist the LU identification process is shown to be an imperative CR attribute for maximising the CR performance and the overall spectral utilisation. Cooperation is particularly important for a CR coexisting with a small-coverage LU system, in which an accurate LU detection mechanism is the predominant limiting factor of CR system performance. The consequences of insufficient CR collaborations are an expensive individual CR detector and a large CR-LU separation requirement, yielding a suboptimal gain in spectral utilisation. The effectiveness of cooperative detection largely depends on system application, channel characteristics, and CR detector sensitivity. The feasibility of CR is evaluated via a simulated CR model using the achievable CR system performance and the corresponding operational requirements as performance metrics. The fundamental limit of permissible CR transmit power has been derived from an interference constraint imposed on the CR operation. Assuming the LU accepts a minor performance degradation (an outage probability < 5%), it has been shown that a low CR system outage (< 5%) may be achieved while simultaneously providing a reliable LU identification rate (> 99%) with the modest detector specifications commercially available. Above all the CR feasibility is strongly subject to the signal characteristics and the heterogeneous propagation conditions in the CR deployment scenario. An algorithmic approach is developed which demonstrates that a practical CR system independent of LU cooperation may be feasible.
Onboard Feature Indexing from Satellite Lidar Images

The purpose of the onboard feature indexing system is to perform pattern recognition and data compression onboard. We use the unsupervised machine learning algorithm kmeans to classify the lidar profile data and generate an index dictionary. Then we train the Radial Basis Function neural network with the index dictionary on ground computers. Finally, we use the same RBF model for the onboard feature recognition and indexing. We implemented a prototype of the onboard computer with ZISC (Zero Instruction Set Computing) chips and FPGA (Field Programmable Gate Array) so that it takes advantage of intrinsic parallel computing and reconfigurability. We tested a set of 44K profiles as the training set to learn prototypical profiles that make up the indexing dictionary. With 64 indices, we reach a high compression rate 99.17% with reasonable error range. We found the required neurons are equal to the indices. We also compared our method to wavelet algorithm and found that it significantly outperforms the wavelet compression technique.
Mechatronics examples for teaching modeling, dynamics, and control

This thesis presents the development of a single-axis magnetic suspension. The intention is to use this system as a classroom demo for an introductory course on modeling, dynamics, and control. We solve this classic nonlinear controls problem with feedback linearization; the main advantage with this technique is operating point independency. However, it is highly sensitive to modeling errors and unpredicted plant behavior. We overcome these barriers by using a model based on both theory and experimentally determined behavior. This paper details the theory, modeling, and implementation, concluding with performance analysis. Thesis Supervisor: David L. Trumper Title: Associate Professor
Secondary Structure Segments are Much More Conserved than Primary Sequence Segments

To be biologically functional, all proteins must adopt specific folded three-dimensional structures. Some believes in that the genetic information for the protein specifies only the primary structure, the linear sequence of amino acids in the polypeptide backbone, and most purified proteins can spontaneously refold in vitro after being completely unfolded, so the three-dimensional structure must be determined by the primary structure (Creighton, 1990). How this occurs has come to be known as 'the protein folding problem'. As a part of the protein folding problem, the existence of similar substrings in diverse proteins is remarkable. Some scientist call it “conserved core” which echoes the claim that all proteins diversified from a common ancestor protein, and these similar pieces of the two or several proteins are the substrings that resisted the pressure of the evolution. Due to naturally-occurring (DNA fails to copy accurately) and external influences just like ultraviolet radiation, electromagnetic fields, atomic radiations, protein coding genes and proteins may undergo some changes by the time in response to mutations. The rate of these mutations is strongly correlated to the intensity of the environmental conditions, and it is not possible to estimate a constant rate just in the case of radioactive decay. Also there is no much evidence that the diversity of proteins relies on only these mutations. For this reason we prefer the term "similar substrings". In this paper we focused in the relation between primary and secondary structure mismatches of the substrings of length seventeen residues. We have seen that the mismatches in the corresponding secondary structure sequence substrings of the same length lags behind primary mismatches. We constructed a conditional probability landscape that resembles the conditional probability of a certain secondary substring mismatch given the primary substring mismatch. This landscape shows that even when 6-7 mismatches exist in two primary substrings of length 17 that belong to the two different proteins, the probability of full match of corresponding secondary structure substrings is remarkable. We downloaded primary and secondary sequences of all 303,524 proteins of the PDB protein databank. Eliminating the duplicates and proteins of residue length less than 30, we have got a non redundant database of 80,592. We developed a search algorithm FIND-SIM to find similar primary sequence substrings in a query protein and target proteins. Some examples of full secondary structure matches of short substrings corresponding to short primary structure substrings with high mismatches are given.
A Data Analytics Framework for Regional Voltage Control

Modern power grids are some of the largest and most complex engineered systems. Due to economic competition and deregulation, the power systems are operated closer their security limit. When the system is operating under a heavy loading condition, the unstable voltage condition may cause a cascading outage. The voltage fluctuations are presently being further aggravated by the increasing integration of utility-scale renewable energy sources. In this regard, a fast response and reliable voltage control approach is indispensable. The continuing success of synchrophasor has ushered in new subdomains of power system applications for real-time situational awareness, online decision support, and offline system dianostics. The primary objective of this dissertation is to develop a data analytic based framework for regional voltage control utilizing high-speed data streams delivered from synchronized phasor measurement units. The dissertation focuses on the following three studies: The first one is centered on the development of decision-tree based voltage security assessment and control. The second one proposes an adaptive decision tree scheme using online ensemble learning to update decision model in real time. A system network partition approach is introduced in the last study. The aim of this approach is to reduce the size of training sample database and the number of control candidates for each regional voltage controller. The methodologies proposed in this dissertation are evaluated based on an open source software framework. A Data Analytics Framework for Regional Voltage Control
NEURAL NETWORK BASED DEEP LEARNING AND ENSEMBLE TECHNIQUES FOR DATA CLASSIFICATION

In this paper, four neural network architectures are proposed for data classification. The four neural networks are constructed based on deep learning and ensemble architectures. Supervised and unsupervised learning paradigms are adopted. Stack of Supervised Neural Network (SSNN), Stack of Unsupervised Neural Network (SUNN), Ensemble of Supervised Neural Network (ESNN), and Ensemble of Unsupervised Neural Network (ESNN) are the proposed neural network classifiers. Australian credit approval data set from the University of California, Irvin is used to evaluate the classifiers. Supervised neural networks have produced more accurate classification results than unsupervised networks. Stack architectures are comparatively better than ensemble architectures. It is found from this research that the combination of supervised learning method and stack architecture leads to better performance.
Neural network control for intelligent end effector of manipulators via proximity sensing

The authors develop a technique using an artificial neural network system for the intelligent control of a robot manipulator (e.g. Unimation PUMA 560) that can grasp objects dexterously with active optical proximity sensors. Multi-layer neural networks are presented to process the sensor information for grasping objects dexterously via iterative learning of the network. The sensory system uses a transmission (beam-block) sensing mode and a reflection sensing mode. Complex nonlinear operations for processing the information can be performed easily by the artificial neural network, and total processing can be performed in real-time by using a DSP board (AT&T WE DSP32C, 49.152 MHz). <<ETX>>
Teachers and Students Poised to Use Mobile Phones in the Classroom

ABSTRACT This study examined the perceptions of 15 teachers and 103 middle school students at a progressive private school located in Mumbai, India to determine their support for the use of mobile phones in the classroom, their perceptions of the mobile phone features that are beneficial for school-related work, and the instructional benefits and barriers to mobile phone use prior to beginning a mobile phone pilot program. The results indicated that most teachers (86%) and students (92%) supported the use of mobile phones in the classroom while those remaining voiced uncertainties. The participants perceived many mobile phone features as being useful in the classroom, but the teachers and students significantly differed in their views. Participant responses revealed little concern about most of the barriers reported in the literature; both teachers and students were least concerned about the use of mobile phones causing a disruption to learning.
Survey: Models and Prototypes of Schema Matching

Schema matching is critical problem within many applications to integration of data/information, to achieve interoperability, and other cases caused by schematic heterogeneity. Schema matching evolved from manual way on a specific domain, leading to a new models and methods that are semi-automatic and more general, so it is able to effectively direct the user within generate a mapping among elements of two the schema or ontologies better. This paper is a summary of literature review on models and prototypes on schema matching within the last 25 years to describe the progress of and research chalenge and opportunities on a new models, methods, and/or prototypes.