Miracle-dz/UniFAR-FTUs huggingface dataset:
Review on Learning based Interface Modelling using Augmented Reality

: Augmented Reality (AR) is a widely growing field with a lot of scope in future. The learning aspects of AR have been widely studied from student’s perception and learning effectiveness using AR. Making use of interactive concepts involving user involvement (via speech, actions, gestures) such as AR based User Interfaces(UIs) increases the efficiency of user learning. Augmented Reality can be used as learning element where user interacts with the developed augmented reality based Graphical user interface (GUI) to understand the different concepts which the user wants to learn. It helps the user for easy learning and better understanding of various concepts to be learned.
Redesigning Transaction Processing Systems for Non-Volatile Memory

Transaction Processing Systems are widely used because they make the user be able to manage their data more efficiently. However, they suffer performance bottleneck due to the redundant I/O for guaranteeing data consistency. In addition to the redundant I/O, slow storage device makes the performance more degraded. Leveraging non-volatile memory is one of the promising solutions the performance bottleneck in Transaction Processing Systems. However, since the I/O granularity of legacy storage devices and non-volatile memory is not equal, traditional Transaction Processing System cannot fully exploit the performance of persistent memory. The goal of this dissertation is to fully exploit non-volatile memory for improving the performance of Transaction Processing Systems. Write amplification between Transaction Processing System is pointed out as a performance bottleneck. As first approach, we redesigned Transaction Processing Systems to minimize the redundant I/O between the Transaction Processing Systems. We present LS-MVBT that integrates recovery information into the main database file to remove temporary files for recovery. The LS-MVBT also employs five optimizations to reduce the write traffics in single fsync() calls. We also exploit the persistent memory to reduce the performance bottleneck from slow storage devices. However, since the traditional recovery method is for slow storage devices, we develop byte-addressable differential logging, user-level heap manager, and transaction-aware persistence to fully exploit the persistent memory. To minimize the redundant I/O for guarantee data consistency, we present the failure-atomic slotted paging with persistent buffer cache. Redesigning indexing structure is the second approach to exploit the non-volatile memory fully. Since the B+-tree is originally designed for block granularity, It generates excessive I/O traffics in persistent memory. To mitigate this traffic, we develop cache line friendly B+-tree which aligns its node size to cache line size. It can minimize the write traffic. Moreover, with hardware transactional memory, it can update its single node atomically without any additional redundant I/O for guaranteeing data consistency. It can also adapt Failure-Atomic Shift and Failure-Atomic In-place Rebalancing to eliminate unnecessary I/O. Furthermore, We improved the persistent memory manager that exploit traditional memory heap structure with free-list instead of segregated lists for small memory allocations to minimize the memory allocation overhead. Our performance evaluation shows that our improved version that consider I/O granularity of non-volatile memory can efficiently reduce the redundant I/O traffic and improve the performance by large of a margin.
Hybrid Model of Convolutional LSTM and CNN to Predict Particulate Matter

Particulate matter (PM) can harm human health by causing lung cancer, pneumonia, or cardiovascular disease. There is a growing awareness of dangerous PM among people and governments. In order to prepare for the risk, the prediction performance of PM is important. Therefore, many kinds of research are developing various prediction models. Among the models, LSTM based models show the best result and it uses various auxiliary data, including spatial features to improve performance. However, spatial features can be depreciated because all input data has to be unfolded to 1D vector. In this paper, we apply Convolutional LSTM to our model to take advantage of the spatiotemporal relation of the wind and PM forecasting problem. Also, we add CNN to extract temporal features of the dataset on our model in parallel. Finally, we combine both Convolutional LSTM and CNN to predict more accurate PM concentration. In the experiment, we compared this model with LSTM and CNN-LSTM models in previous studies. At the result, the hybrid model showed the best performance.
AutoPer: Automatic Recommender for Runtime-Permission in Android Applications

Permission mechanisms serve as the main measure to protect users privacy and security in Android applications. Modern smartphone operating systems (Android 6.0 and later versions) prompt users to regulate permissions using ask-on-first-use policy. Much research has been done to dynamically regulate permissions depending on user preferences and contexts in modern operation systems. However, all these techniques have limitations–they heavily rely on users' current or historical decisions on granting permissions, ignoring the fact that users are not experts on privacy protection, i.e., whether a permission shall be granted. In this work, we propose a system to automatically recommend runtime-permission to users. The main idea behind is that the application descriptions reflecting functional information can be used to analyze whether a permission is needed by the application. In more details, using description mining, we extract multiple topics and build a topic-permission mapper. Given an application as input, we first decide which topics it belongs to and then recommend the permissions according to the topic-permission mapper. As the output, besides binary recommendation of "allow" or "deny" recommendations, we provide explanations for the recommendations to uncover the reason for users. We implemented our approach in a tool– AutoPer, and evaluated the approach using 28,850 Android applications from Google Play. The experiments show that our approach achieves a fairly good performance with an accuracy of 81.0%, which demonstrates the effectiveness of AutoPer for permission recommendation.
Networks, communities, and consumer behaviour

Networks are an abstract representation of connections (the "edges") between entities (the "nodes"). One can represent many different types of data in this way, including many social, biological, technological and physical systems. Examples we discuss in this thesis include networks of friendship ties between individuals on Facebook, coauthorship networks between scientists, and similarities in voting patterns between members of the US Congress. Analysing intermediate-sized (or "meso-scale") features often reveals insights about a network's structure and function. A particular type of meso-scale feature are "communities", where one typically thinks of a community as a set of nodes that is particularly "well-connected" internally but has "few" connections to other nodes in a network. A complementary interpretation of a community is as a set of nodes that "trap" a diffusion-like dynamical process for a "long" time. Based on this dynamical interpretation, we investigate "size-resolved community structure" in networks by identifying bottlenecks of locally-biased dynamical processes that start at seed sets of nodes. By sampling many different local communities for different seeds and different strengths of the locality bias of the dynamical process, we obtain a picture of the way communities at different size scales compare in a network. This "size-resolved community structure" provides a signature of community structure in a network and its qualitative features are related to the way local communities combine to form the larger scale structure of a network. For many data sets, ordinary networks are not sufficient to represent the detailed connectivity patterns. For example, connections often evolve over time and one may have different types of connections between the same entities. Multilayer networks provide a framework to represent these different types of situations. The perspective of communities as bottlenecks to dynamical processes extends in a natural way to multilayer networks and we use it to illustrate that two types of random walk on a multilayer network that have been used as the basis for identifying communities in a multilayer network correspond to very different notions of what it means for a set of nodes to be a good multilayer community. This exemplifies the need for multilayer benchmark networks with known community structure to compare the ability of different methods to identify intuitive community structure. We propose a method for generating benchmark networks with general multilayer structure and use it as the basis for a preliminary comparison of different multilayer community detection methods. Finally, we use multilayer community detection to analyse survey data about people's perception of their hair. One key advantage of this type of data compared to most traditional network data sets is that we have a large number of potential explanatory variables that we can use to interpret the results of identifying communities which allows us to identify some potentially interesting hypothesis.
Gaze estimation with graphics

Gaze estimation systems determine where someone is looking. Gaze is used for a wide range of applications including market research, usability studies, and gaze-based interfaces. Traditional equipment uses special hardware. To bring gaze estimation mainstream, researchers are exploring approaches that use commodity hardware alone. My work addresses two outstanding problems in this field: 1) it is hard to collect good ground truth eye images for machine learning, and 2) gaze estimation systems do not generalize well – once they are trained with images from one scenario, they do not work in another scenario. In this dissertation I address these problems in two different ways: learning-by-synthesis and analysis-by-synthesis. Learning-by-synthesis is the process of training a machine learning system with synthetic data, i.e. data that has been rendered with graphics rather than collected by hand. Analysis-by-synthesis is a computer vision strategy that couples a generative model of image formation (synthesis) with a perceptive model of scene comparison (analysis). The goal is to synthesize an image that best matches an observed image. In this dissertation I present three main contributions. First, I present a new method for training gaze estimation systems that use machine learning: learning-by-synthesis using 3D head scans and photorealistic rendering. Second, I present a new morphable model of the eye region. I show how this model can be used to generate large amounts of varied data for learning-by-synthesis. Third, I present a new method for gaze estimation: analysis-by-synthesis. I demonstrate how analysisby-synthesis can generalize to different scenarios, estimating gaze in a deviceand personindependent manner.
Reproducible Workflow for Cartography – Migrants Deaths in the Mediterranean

Abstract. As any scientific production, maps must be disputed and debated. The implementation of reproducible processes based on free software and open data is essential. In this paper, we demonstrate that this objective can be achieved in the R software ecosystem. In our demonstration, we propose a set of cartographic visualizations based on the example of dead and missing migrants in the Mediterranean over the period 2014–2018. Each representation focuses on one aspect of the phenomenon and the R code used is available. We argue that this multi-visualization contributes to bring new knowledge on the migration debate at European borders and aims at illustrating its geographical complexity.
Factors driving the adoption of Facebook in higher education

Social network sites in general, and Facebook in particular, allow users with common interests to meet, share ideas, and collaborate, creating new forms of informal learning. In order to understand and eventually take advantage of the many benefits that Facebook can bring to the academic world, we need to study its adoption process. The objective of this article is to identify the factors that may motivate university students to adopt and use Facebook as an educational tool. We aim to contribute to the existing literature by adding a unique approach to examine this question, incorporating theories and constructs from the well-developed technology acceptance theory. According to our results, Performance Expectancy of Students is the most important factor on predicting the Behavioral Intention to Use Facebook for academic purposes, followed by Appropriateness for Academic Setting and Compatibility With Academic Work.
Exploring Discriminative Representations for Image Emotion Recognition With CNNs

Image emotion recognition aims to automatically categorize the emotion conveyed by an image. The potential of deep representation has been demonstrated in recent research on image emotion recognition. To better understand how CNNs work in emotion recognition, we investigate the deep features by visualizing them in this work. This study shows that the deep models mainly rely on the image content but miss the image style information such as color, texture, and shapes that are low-level visual features but are vital for evoking emotions. To form a more discriminative representation for emotion recognition, we propose a novel CNN model that learns and integrates the content information from the high layers of the deep network with the style information from the lower layers. The uncertainty of image emotion labels is also investigated in this paper. Rather than using the emotion labels for training directly, as in previous work, a new loss function is designed by including the emotion labeling quality to optimize the proposed inference model. Extensive experiments on benchmark datasets are conducted to demonstrate the superiority of the proposed representation.
Towards efficient analytic query processing in main-memory column-stores

Recently, there is a resurgence of interest in main-memory analytic databases because of the large RAM capacity of modern servers and the increasing demand for real-time analytic platforms. In such databases, operations like scan, sort and join are at the heart of almost every query plan. However, current implementations of these operations have not fully leveraged the new features (e.g., SIMD, multi-core) provided by modern hardware. The goal of this dissertation is to design efficient algorithms for scan, sort and join by judiciously exploiting every bit of RAM and all the available parallelisms in each processing unit. Scan is a crucial operation since it is closest to the underlying data in the query plan. To accelerate scans, a state-of-the-art in-memory data layout chops data into multiple bytes and exploits early-stop capability by high-order bytes comparisons. As column widths are usually not multiples of byte, the last-byte of such layout is padded with 0’s, wasting memory bandwidth and computation power. To fully leverage the resources, we propose to weave a secondary index into the vacant bits (i.e., bits originally padded with 0’s), forming our new storage layout. This storage layout enables skip-scan, a new fast scan that enables both data skipping and early stopping without any space overhead. With the advent of fast scans and denormalization techniques, sorting could become the new bottleneck. Queries with multiple attributes in clauses like GROUP BY, ORDER BY, SQL:2003 PARTITION BY are common in real workloads. When executing such queries, state-of-the-art main-memory column-stores
Learning contact-aware robot controllers from mixed integer optimization

The problem of handling contact is central to the task of controlling a walking robot. Robots can only move through the world by exerting forces on their environment, and choosing where, when, and how to touch the world is the fundamental challenge of locomotion. Because the space of possible contacts is a high-dimensional mix of discrete and continuous decisions, it has historically been difficult or impossible to make complex contact decisions online at control rates. This work first presents an approach to contact planning which is able to make some guarantees of global optimality through mixed-integer programming. That method is applied successfully to a humanoid robot in laboratory conditions, but proves difficult to rely on when the robot is experiences unmodeled disturbances. To overcome those limitations, this thesis also introduces LVIS (Learning from Value Interval Sampling) a new approach to the control of walking robots which allows complex contact decisions to be made online using a cost function trained from offline trajectory optimizations. The LVIS algorithm is demonstrated on a simple cart-pole system with walls as well as a simplified bipedal robot model, and its success at allowing both models to use contact decisions to recover from external disturbances is demonstrated in simulation. Thesis Supervisor: Russ Tedrake Title: Professor of Electrical Engineering and Computer Science
A MODIFIED SAMPLING METHOD FOR LOCALIZATION ACCURACY IMPROVEMENT OF MONTE CARLO LOCALIZATION

Unmanned vehicles are devices that can move around and perform tasks without an operator onboard. Such features are essential in many applications. Localization is a very important task in any autonomous mobile robot; in order to reliably navigate, the robot must keep accurate track of where it is. In the past few years Monte Carlo Localization (MCL) has been one of the most successful and popular approaches to solve the localization problem. MCL is a Bayesian algorithm based on particle filters. This paper is an attempt to increase the accuracy of localizing a mobile robot by modifying the way of generating samples from the proposal distribution of the MCL algorithm. Results show improvements in localization accuracy as compared to the basic MCL algorithm.
A Neighbor Discovery Method Based on Probabilistic Neighborship Model for IoT

Neighbor discovery, meaning that a node receives other nodes’ radio frequency (RF) signals to be aware of their existence, is an indispensable procedure in Internet of Things (IoT)-oriented peer-to-peer (P2P) networks with energy-limited nodes. The main objective of neighbor discovery methods is to improve the energy efficiency, since node energy is usually very limited. As the neighborship maintaining time is very short in the mobile networks, neighbor discovery should be achieved in a very energy-efficient manner. The existing neighbor discovery methods only consider the received RF signals from other nodes (or neighbor table information derived from the received RF signals) as the basis of neighborship evaluation. The neighborship evaluation is inaccurate when the single source of neighbor information is employed. Inaccurate neighborship causes incorrect active slot scheduling and low energy efficiency of neighbor discovery. This paper proposes a generalized and probabilistic neighborship evaluation model to unify a variety of neighborship information in IoT-oriented P2P networks into neighborship probability values. Based on the model, we propose an energy-efficient neighbor discovery middle-ware algorithm by carefully replanning active slots of nodes according to the neighborship probability. The simulation evaluation results show that our proposed method decreases the average discovery delay by up to 11.46%, approximately, compared with other methods at the same energy budget.
M.I.C. Computing Architecture Model

: This paper presents how the process of storing humongous data by organisations has been revolutionised through the introduction of cloud computing. Since, the cloud computing is being done by third party vendor it has opened the pandora’s box where Transparency, cost, portability of data remains the major issues. However, different models can be adopted in order to enhance the optimisation of cloud computing such as; Multitenant computing model, Resource provisioning, and No data center architecture. Light on the related work has also been thrown. Based on the comprehensive review need for further research has been observed, and the references are provided for further research work.
Channel estimation in mobile WiMAX systems

The use of channel adaptive transmission techniques requires accurate channel state information (CSI) which may not easily be achievable particularly near the cell boundary in multi-cell environments. In this paper, we consider the improvement of CSI estimation accuracy by exploiting the preamble signal as well as the pilot signal in ODFM based wireless systems. We first estimate the CSI from both the received preamble signal and the received pilot signal. By exploiting the channel correlation between the preamble and pilot signaling, we combine the two CSIs estimated from the preamble and pilot signals. The combining weight is analytically determined to minimize the mean squared error. For the interference cancellation with the use multiple receive antennas, we also consider the estimation of interference channel in a similar manner. Simulation results show that when applied to the mobile-WiMAX system, the proposed scheme is quite effective near the cell boundary.
Computer Vision-Assisted Surgery: Real-Time Instrument Tracking with Machine Learning

Visual tracking of surgical instruments is a key component of various computer-assisted interventions, yet a very challenging problem in the field of Computer Vision. This dissertation presents novel approaches which leverage machine learning techniques for precise real-time tracking and 2D pose estimation of instruments. The achieved results demonstrate that the proposed methods based on Random Forests and Deep Learning provide remarkable advantages with respect to the state of the art in terms of accuracy, robustness and generalization.
DBM Optimization with Additional Category Information

For unconstrained training of restricted Boltzmann machine (RBM), it is easy to appear that feature homogenization leads to poor generalization ability. This paper introduces category conditions into DBM training, proposes label conditional RBM which is used in the construction of DBM. According to unsupervised training characteristics of RBM, this paper adds the category information as the model hidden unit training condition to the implicit unit posterior activation probability calculation. This paper applies the model as the underlying structure of the deep Boltzmann machine (DBM) to the deep network construction. Through handwritten digit recognition set test, compared with the shallow model, the new model after adding the category condition has a great improvement in the model training speed and feature extraction effectiveness, and can effectively enhance the feature learning ability of the deep model.