Miracle-dz/UniFAR-FTUs huggingface dataset:
DigitalCommons@CSB/SJU DigitalCommons@CSB/SJU

As AI becomes ever more competent and integrated into our lives, the issue of AI-human goal misalignment looms larger. This is partially because there is often a rift between what humans explicitly command and what they actually mean. Most contemporary AI systems cannot bridge this gap. In this study we attempted to reconcile the goals of human and machine by using EEG signals from a human to help a simulated agent complete a task.
Research on borrower's credit classification of P2P network loan based on LightGBM algorithm

The credit classification of a borrower is the main method to effectively reduce the credit risk of P2P online loans. In this paper, LightGBM algorithm has the advantage in the high accuracy of data classification. Feature extraction, selection and reconstruction of the original data are performed by feature engineering. The One hot Encoding technology is used to re-encode the discretised feature indicators. Z-score data normalisation normalises the characteristics of continuous variables. Re-sort all feature indicators by contribution and perform PCA dimensionality reduction, and filter out effective feature indicators for training and testing. Finally, the problem of imbalance of samples and optimisation of model parameters is solved by ten-fold cross-validation. Result of simulation experiment shows that the LightGBM model has good stability, good fitting ability and high classification prediction accuracy.
Indepth Analysis of Medical Dataset Mining: A Comparitive Analysis on a Diabetes Dataset Before and After Preprocessing

Most of the healthcare organizations and medical research institutions store their patient’s data digitally for future references and for planning their future treatments. This heterogeneous medical dataset is very difficult to analyze due to its complexity and volume of data, in addition to having missing values and noise which makes this mining a tedious task. Efficient classification of medical dataset is a major data mining problem then and now. Diagnosis, prediction of diseases and the precision of results can be improved if relationships and patterns from these complex medical datasets are extracted efficiently. This paper analyses some of the major classification algorithms such as C4.5 ( J48), SMO, Naïve Bayes, KNN Classification algorithms and Random Forest and the performance of these algorithms are compared using WEKA. Performance evaluation of these algorithms is based on Accuracy, Sensitivity and Specificity and Error rate. The medical data set used in this study are Heart-Statlog Medical Data Set which holds medical data related to heart disease and Pima Diabetes Dataset which holds data related to Diabetics. This study contributes in finding the most suitable algorithm for classifying medical data and also reveals the importance of preprocessing in improving the classification performance. Comparative study of various performances of machine learning algorithms is done through graphical representation of the results. Keywords: Data Mining, Health Care, Classification Algorithms, Accuracy, Sensitivity, Specificity, Error Rate
A Meta Analysis on Effective conditions to Offer Animation Based Teaching Style

Purpose - The purpose of this experimental study is to investigate the impact of teaching Oriental Music using Blended Learning (BL) approach for the students of senior secondary level in Sri Lanka specifically focusing on their achievement on required competencies of Oriental Music at Ordinary Level. The study analyzes the academic performance of students with detailed comparison of BL environment and traditional learning environment. Authors propose the application of BL approach to teach Oriental Music and study its impact on improvement of students’ competency. The study conducted with the application of a mixed instructional design model of objectivist and constructivist approaches for the design of the blended learning course in a student centred learning environment. Methodology - The study was directed by using true experimental study design with pretest and posttest control groups. BL was applied to the experimental group and the traditional instruction method was applied to control group. 9 schools from Colombo district were randomly selected for the experimental and control groups covering all the three existing school types of Sri Lanka. The study group consisted of 360 students of Grade 10 and Grade 11 who has been studying Oriental Music as a subject for General Certificate of Education Ordinary Level. To analyze the data Descriptive statistics, Paired samples t test, Independent samples t test were utilized. Findings - The findings of the experiment indicated that students who has studied Oriental Music under BL strategy showed a significant improvement in their music academic performances after the intervention. The mean post-test of the experimental group was 71.75 which is significantly higher than the mean control group which was 52.07. The mean difference was 19.68 1.91. Hence, there is a statistically significant increase in the performance of students who studied Oriental Music under blended learning. Thus, it is clearly evident that the blended instruction was effective. Significance - This study indicated a positive platform to mould and cater the entire teaching learning process by introducing BL strategy to Sri Lankan secondary education system and fulfilled an existing research gap by utilizing BL to teach highly traditional abstract art. Results of the study contributes to the curriculum designing field with novel ideas to adapt blended instructions to teach secondary level students effectively.
Visualization Authoring for Data-driven Storytelling

Author(s): Ren, Donghao | Advisor(s): Hollerer, Tobias | Abstract: Data-driven storytelling is the process of communicating insights and findings that are supported by data, forming a visualization-based narrative. However, most current visualization creation tools either only support fixed sets of designs or require an in-depth understanding of programming concepts. To enable non-programmers to create custom visualizations for data-driven storytelling, we design interactions and implement user interfaces for visualization authoring. In the first part of this dissertation, we introduce and evaluate a series of three visualization authoring tools using traditional user interfaces: (1) iVisDesigner, which uses a data-flow model and enables users to author visualizations by specifying mappings from data to graphics interactively; (2) ChartAccent, a tool for annotating a given visualization; and (3) Charticulator, which allows users to design custom layouts interactively. We then reflect on the evaluation of visualization authoring user interfaces. In the second part of the dissertation, we extend our approach to multiple presentation media or display environments, including traditional 2-dimensional screens, large projection-based virtual-reality (VR) systems, and head-mounted virtual/augmented reality displays (HMDs). To leverage such immersive visualization environments, we ported and extended the iVisDesigner authoring approach to projection-based virtual reality. To facilitate the development of immersive visualizations, we built a visualization library called Stardust, which provides a familiar API to utilize GPU processing power in a cross-platform way. Finally, we present Idyll-MR, a system for authoring data-driven stories in virtual and augmented reality. We evaluated these authoring tools and libraries, and demonstrated high expressiveness, usability, and performance, as well as portability across platforms. In summary, our contributions enable larger audiences to create visual data-driven stories using different presentation media, leading to an overall enriched diversity of visualization designs.
GENERATING 3D CITY MODELS BASED ON THE SEMANTIC SEGMENTATION OF LIDAR DATA USING CONVOLUTIONAL NEURAL NETWORKS

Abstract. Virtual city models are important for many applications such as urban planning, virtual and augmented reality, disaster management, and gaming. Urban features such as buildings, roads, and trees are essential components of these models and are subject to frequent change and alteration. It is laborious to manually build and update virtual city models, due to a large number of instances and temporal changes on such features. The increase of publicly available spatial data provides an important source for pipelines that automate virtual city model generation. The large quantity of data also opens an opportunity to use Deep Learning (DL) as a technique that minimizes the need for expert domain knowledge. In addition, many Deep Learning models calculations can be parallelized on modern hardware such as graphical processing units, which reduces the computation time substantially.We explore the opportunity of using publicly available data in computing multiple thematic data layers from Digital Surface Models (DSMs) using an automatic pipeline that is powered by a semantic segmentation network. To evaluate this design, we implement our pipeline using multiple Convolutional Neural Networks (CNN) with an encoder-decoder architecture. We produce a variety of two and three-dimensional thematic data. We focus our evaluation on the pipeline’s ability to produce accurate building footprints. In our experiments we vary the depths, the number of input channels and data resolutions of the evaluated networks. Our experiments process public data that is provided by New York City.
A Study on Facial Expression Recognition

This study focuses on the issue of automatic Facial Expression Recognition (FER) on little databases of 2D faces. Convolutional Neural Networks (CNN) is a relatively new classification technique, which reaches the state of the art on big databases; however, the use of CNN with a scarce number of samples is still an open and interesting challenge. Following the classical machine learning approach, we considered different combination of appearance based projection methods, feature extraction techniques and classifiers, and we compared their performances with special designed CNN. Experimental results underline the drawback of CNN with scares labeled data.
Cluster Analysis for Data Processing in Educational Research

Quantitative approaches to educational research have been undervalued and consequently less widely used. In this sense, this paper aims to present and analyze the techniques of Cluster Analysis as a possibility for research in sciences area. Therefore, the main hierarchical and non-hierarchical techniques of Cluster Analysis are presented, as well as some of their applications in educational research found in the literature. Cluster Analysis is adequate to simplify or elaborate hypotheses on massive data, such as large-scale educational research. The studies in the area of education that used Cluster Analysis methods proved to be fruitful to elicit results that collaborate with the area.
Investigating the Relationship between Sleep and Wake Behavior using Machine Learning and Smart Home Sensors

by Jennifer Ashleigh Williams, Ph.D. Washington State University May 2017 Chair: Diane J. Cook Smart home technologies offer a unique opportunity to monitor individuals in a noninvasive manner. Because of this, they provide an ideal opportunity for pervasive healthcare. Smart homes provide the resident the freedom of being at home while still providing monitoring and analysis of any health issues that may arise. The ultimate goal is to use smart home technology to enable people recovering from injuries or coping with disabilities (i.e., PostTraumatic Stress Disorder, or dementia) to live independently. The immediate goal of this dissertation is to monitor, analyze, and predict the overall behavior of the individual in a smart home environment for correlation with a range of chronic and sudden health issues. We accomplish this goal by designing computational approaches to analyze and forecast daily behavior. Additionally, we quantify daily behavior exhibited by an individual while they are both awake and asleep based on data collected in CASAS smart homes. Sleep is an important element in our everyday lives. In this dissertation we hypothesize that wake behavior has an impact on both current and future sleep and vice versa. To analyze the relationship between sleep and wake activity we consider sleep separately from other activities within the smart home. We examine the relationship based on prior studies and using our novel v computational methods. To computationally evaluate the relationship between wake and sleep behavior, we explore three main elements: 1) behavior cycle detection, 2) behavior quantification methods, and 3) behavior forecasting. We provide validation for these approaches by using data collected in actual smart home test sites. In addition to analyzing the relationship between wake and sleep behaviors, we propose that a reliable sleep quality analysis can be achieved using the CASAS smart home system. We pursue this goal by performing a pilot study in which wearable sensors are used in conjunction with the CASAS smart home sensors. We use the data from this pilot study to validate the use of smart home sensors as a tool for the analysis of sleep patterns.
Bug Report Classification based on Vector Space Model

As a vehicle for recording and tracking defects, bug reports provide a basis for solving software quality problems. Currently, software testing is often carried out in a multi-person and parallel state. The integration process of numerous bug reports, such as moving fake or duplication bug reports, is facing severe challenges. Therefore, this paper proposes an automatic detection modus for bug reports based on the vector space model. After pre-processing the bug report, a matching library is created according to the test requirements and test report samples. The vector space model is used to calculate the similarity between the two, and the correctness of the bug report is detected based on this. Experiments with the data of a software test contest show that the modus proposed in this paper can correctly judge most bug reports, effectively improving the efficiency of de-false and de-duplication.
Heat flux capacity measurement and improvement for the test of superconducting logic circuits in closed-cycle cryostats

With the current progress of superconducting integrated circuits, circuit complexities have reached more than 100,000 Josephson junctions. At the laboratory environment, circuit tests are generally being conducted at liquid Helium environments. With the commercialization of the superconducting integrated circuit-based systems and/or price increase of liquid Helium, utilization of the closed-cycle systems will be inevitable. Even though it is possible to implement closedcycle systems with quite substantial excess cooling powers after all the wirings and peripheral circuits, one point not to be overlooked is the power density at the superconducting chips. We have observed that the circuits designed for operation at liquid Helium bath have much higher power densities that exceed the heat flux capacities of closed-cycle cryocoolers. We have measured that the heat flux capacities of closed-cycle systems are about 0.1 W/cm when the chip is in vacuum. Thus, if the power density in any of the bias resistors are higher than these values, it is difficult to keep the chip in superconducting state. With the positive feedback from a local hot spot under current bias, the chip rapidly heats up due to Joule heating. To increase the limit of power density, we propose an encapsulation method that increases the heat flux capacity of the system. With the encapsulation, the heat flux capacity of the system is increased to about 0.4 W/cm .
A CONCEPT FOR AN AUTOMATED APPROACH OF PUBLIC TRANSPORT VEHICLES TO A BUS STOP

Abstract. This paper discusses the current methods for vehicle self-localization and compares previous findings to the use for urban public traffic vehicles. In specific, requirements for autonomous buses approaching a bus stop are defined. An autonomous system capable of reliable vehicle self-localization running in real-time in a city scenario shall be developed in a future work based on this paper. The comparison of filter-based estimation and graph-based optimization techniques shows that the latter suits the the automated approach to a bus stop in an urban environment the best. Based on these findings, a concept for self-localization of public transport vehicles equipped with a variety of imaging sensors with the help of a digital high definition map is presented. A current method is shown and a concept of improving the localization by inferring semantic information into landmark detection by low-level data fusion is provided. Validation and verification of the proposed fusion approach have to be carried out in the future, but a validation scenario is presented in this work.
Ensemble Learning from Crowds

Traditional learning from crowdsourced labeled data consists of two stages: inferring true labels for instances from their multiple noisy labels and building a learning model using these instances with the inferred labels. This straightforward two-stage learning scheme suffers from two weaknesses: (1) the accuracy of inference may be very low; (2) useful information may be lost during inference. In this paper, we proposed a novel ensemble method for learning from crowds. Our proposed method is a meta-learning scheme. It first uses a bootstrapping process to create $M$M sub-datasets from an original crowdsourced labeled dataset. For each sub-dataset, each instance is duplicated with different weights according to the distribution and class memberships of its multiple noisy labels. A base classifier is then trained from this extended sub-dataset. Finally, unlabeled instances are predicted by aggregating the outputs of these $M$M base classifiers. Because the proposed method gets rid of the inference procedure and uses the full dataset to train learning models, it preserves the useful information for learning as much as possible. Experimental results on nine simulated and two real-world crowdsourcing datasets consistently show that the proposed ensemble learning method significantly outperforms five state-of-the-art methods.
A Reinforcement Learning Framework for Autonomous Cell Activation and Customized Energy-Efficient Resource Allocation in C-RANs

Cloud radio access networks (C-RANs) have been regarded in recent times as a promising concept in future 5G technologies where all DSP processors are moved into a central base band unit (BBU) pool in the cloud, and distributed remote radio heads (RRHs) compress and forward received radio signals from mobile users to the BBUs through radio links. In such dynamic environment, automatic decision-making approaches, such as artificial intelligence based deep reinforcement learning (DRL), become imperative in designing new solutions. In this paper, we propose a generic framework of autonomous cell activation and customized physical resource allocation schemes for energy consumption and QoS optimization in wireless networks. We formulate the problem as fractional power control with bandwidth adaptation and full power control and bandwidth allocation models and set up a Q-learning model to satisfy the QoS requirements of users and to achieve low energy consumption with the minimum number of active RRHs under varying traffic demand and network densities. Extensive simulations are conducted to show the effectiveness of our proposed solution compared to existing schemes.
Multicast Optimization for CLOS Fabric in Media Data Centers

Multicast is widely deployed in data centers for point-to-multi-point communications. Multicast is increasingly being used to carry uncompressed video in media data centers with very large bandwidth requirements per flow. Multicast control protocols such as IGMP and PIM build multicast trees without trying to maximize the overall fabric capacity, leading to decreased fabric utilization and inability to service flows. In addition, existing multicast protocols are not bandwidth-aware and could cause links to over-subscribe leading to packet loss and negative user quality of experience. In this paper, we formulate offline optimization for multicast trees in clos fabric. We then design and implement two novel algorithms, iRP and LiRP, to optimize multicast tree formation and increase overall fabric multicast capacity. iRP algorithm optimizes online formation of multicast trees while addressing bandwidth requirements using SDN controller. We share analysis of TV studio repetitive traffic patterns the benefits of time series forecasting to predict multicast group membership and bring online optimization efficiency closer to offline optimization results. We then implement and test LiRP algorithm to increases iRP’s fabric efficiency by implementing k-fold cross validation method to predict future multicast group memberships leading to optimized multicast tree placement. We implement iRP and LiRP algorithms using controller-based system and test both algorithms using Cisco Nexus commercially available switches. Testing results confirm that iRP Algorithm increases fabric capacity by 60% compared to PIM performance. LiRP system increases the efficiency of iRP by up to 40% through prediction of multicast group memberships with online arrival.
A COMPARATIVE ANALYSIS OF UNSUPERVISED AND SEMI-SUPERVISED REPRESENTATION LEARNING FOR REMOTE SENSING IMAGE CATEGORIZATION

Abstract. This work aims at investigating unsupervised and semi-supervised representation learning methods based on generative adversarial networks for remote sensing scene classification. The work introduces a novel approach, which consists in a semi-supervised extension of a prior unsupervised method, known as MARTA-GAN. The proposed approach was compared experimentally with two baselines upon two public datasets, UC-MERCED and NWPU-RESISC45. The experiments assessed the performance of each approach under different amounts of labeled data. The impact of fine-tuning was also investigated. The proposed method delivered in our analysis the best overall accuracy under scarce labeled samples, both in terms of absolute value and in terms of variability across multiple runs.
Design Optimisation and Manufacturing Analysis of Transmission Fork of Heavy Motor Vehicles

: During the process of Milling for v cut Slot minor Cracks are generated in fork Surface, which during induction hardening process converts in to deep cracks and can only be detected during the non-destructive testing like MPI (Magnetic Particle Inspection). This leads to higher rejection ratio and incur financial and material losses. The existing design of fork is heavy and not compact which leads to problems during assembly and leads to bending in rails. This necessitates a new design of fork which is compact and light weight. The second design is manufactured without v slot using standard manufacturing techniques which includes process flow diagrams and PFMEA.
Enhancing face pose normalization with deep learning

In this study, we propose a hybrid method for face pose normalization, which combines the 3-D modelbased method with stacked denoising autoencoder (SDAE) deep network. Instead of applying a mirroring operation for the invisible face parts of the posed image, SDAE learns how to fill in those regions by a large set of training samples. In the performance evaluation, we compare the proposed method to four different pose normalization methods and investigate their effects on facial emotion recognition and verification problems in addition to visual quality tests. Methods evaluated in the experiments include 2-D alignment, 3-D model-based method, pure SDAE-based method, and generative adversarial network-based normalization method. Experiments performed on Multi-PIE dataset show that the proposed method produces visually reasonable results and outperforms the others in facial emotion recognition. On the other hand 2-D alignment is sufficient in the verification problem where the detailed face characteristics should be preserved in the normalization process.