Reviewerly/exHarmony huggingface dataset:
Construction of Knowledge Graphs for video lectures

Knowledge Graphs (KG) have become very important in representing both structured and unstructured data. Knowledge graphs are penetrating our daily lives, be it intelligent voice assistants or Facebook friend search. In this research paper, we are focusing on how Knowledge Graphs can be constructed for a video lecture and list down the various important steps that are involved in the process of construction of the graph. Knowledge Graphs are a way of modelling a knowledge domain programmatically with the aid of tools and techniques like machine-learning algorithms, packages like NLTK, subject experts etc. A knowledge graph representation combines data both in structured and unstructured format. Moreover, the knowledge graphs are commonly built on top of existing databases like Wikipedia, Yago[1] to name a few. Video lectures are the most sought-after form of learning in this current scenario. With the rise in demand for video lectures, people have started to make a lot of videos lectures and made them available in YouTube or as online courses. Knowledge graphs offer a way to streamline workflows, automate responses and scale intelligent decisions. By representing the video lecture as a graph, we will be able to represent the content and the knowledge of the video as a graph. Knowledge Graph thus obtained from the video lectures will become a knowledge cloud that can be used for developing various intelligent applications like domain specific chat-bots, recommender engines and so on.
Evaluation of CNN Performance in Semantically Relevant Latent Spaces

We examine deep neural network (DNN) performance and behavior using contrasting explanations generated from a semantically relevant latent space. We develop a semantically relevant latent space by training a variational autoencoder (VAE) augmented by a metric learning loss on the latent space. The properties of the VAE provide for a smooth latent space supported by a simple density and the metric learning term organizes the space in a semantically relevant way with respect to the target classes. In this space we can both linearly separate the classes and generate meaningful interpolation of contrasting data points across decision boundaries. This allows us to examine the DNN model beyond its performance on a test set for potential biases and its sensitivity to perturbations of individual factors disentangled in the latent space.
Unsupervised Depth Estimation From Monocular Images For Autonomous Vehicles

We, humans, are pretty good at analyzing and inferring data from the 3D world we live in. We do it by combining the information from multiple sense organs with the prior knowledge of the object's geometry. Thus, even if an object is occluded, the guess will be almost right! Humans normally use a combination of stereo and monocular cues to identify the presence of an object and localize it but this is different for robots and self-driving vehicles. Understanding and capturing the third dimension information from a world coordinate system is challenging. Active sensors like LiDAR gives solutions to the above-mentioned problems. The sparse data and cost of such sensors hinders the development of such applications. Understanding depth from 2D images is a potential area of research which indeed can lead to 3D reconstruction and 3D object detection. Unsupervised learning is gaining interest since it doesn't require ground truth for training. In this paper, we propose DNN for depth estimation using unsupervised learning, then the proposed methods are evaluated using KITTI standard metrics which shows the promising way for self-driving cars. Our proposed methods outperforms the state-of the-art methods in unsupervised learning for depth estimation with approximately 75% less training data and with less input resolution.
Traffic Sign Recognition Using Distributed Ensemble Learning

Traffic sign recognition is one of the active areas of research in recent years. The automotive technology is moving towards automation in most of the aspects including traffic sign recognition. In an attempt to focus on driving and concentrate on road the driver often misses out the traffic signs, results of which may lead to catastrophic events. This can be avoided by automating the tasks of traffic sign detection and recognition. In this paper, we implement the traffic signs recognition through distributed ensemble technique (DEL), which is an efficient method to automate traffic sign detection. The primary goal of distributed ensemble learning is to decrease the complexity, reduce the training load on each model and improve the convergence. The impact of load distribution with respect to the number of workers has been studied and thereby understanding the trends of a distributed ensemble. Here we use an ensemble of CNN models to train with standard German data set. Keras is used for implementation of distributed ensemble in CNN. Detailed analysis on data distribution between workers and how it impacts the model accuracy is discussed.
A Track-oriented Approach to Target Tracking with Random Finite Set Observations

We have earlier proposed a data-driven approach to target tracking, which models the target movement by using a trajectory function of time (T-FoT) rather than a Markov model. In this work, we extend the approach to account for random finite set observations consisting of both missing and false data. More challenging, the missing and false data are generated under unknown ratios, i.e., they can not be accurately modeled. To tackle this problem, we here propose a data-driven method for identifying the real measurement of the target from clutter if the target is detected and for declaring a misdetection otherwise. Simulation is conducted to demonstrate the effectiveness of our approach, in comparison with the Bayesian-optimal approach.
Computing Abstract Distances in Logic Programs

Abstract interpretation is a well-established technique for performing static analyses of logic programs. However, choosing the abstract domain, widening, fixpoint, etc. that provides the best precision-cost trade-off remains an open problem. This is in a good part because of the challenges involved in measuring and comparing the precision of different analyses. We propose a new approach for measuring such precision, based on defining distances in abstract domains and extending them to distances between whole analyses of a given program, thus allowing comparing precision across different analyses. We survey and extend existing proposals for distances and metrics in lattices or abstract domains, and we propose metrics for some common domains used in logic program analysis, as well as extensions of those metrics to the space of whole program analyses. We implement those metrics within the CiaoPP framework and apply them to measure the precision of different analyses on both benchmarks and a realistic program.
Vision Based Automated Badminton Action Recognition Using the New Local Convolutional Neural Network Extractor

Performance analysis is essential in sports practice where the athlete is evaluated to improve their performance. Due to the rapid growth of science and technology, research on automated recognition of sports actions has become ubiquitous. The implementation of automated action recognition is an effort to overcome the manual action recognition in sport performance analysis. In this study, we developed a model for automated badminton action recognition from the computer vision data inputs using the deep learning pre-trained AlexNet Convolutional Neural Network (CNN) for features extraction and classify the features using supervised machine learning method which is linear Support-Vector Machine (SVM). The data inputs consist of badminton match images of two classes: hit and non-hit action. Before pre-trained AlexNet CNN was directly extracting the features, we introduced the new local CNN extractor in recognition pipeline. The results show that the classification accuracy with this new local CNN method achieved 98.7%. In conclusion, this new local CNN extractor can contribute to the improvement of the performance accuracy of the classification task.
Google Traces Analysis for Deep Machine Learning Cloud Elastic Model

Data set used in machine learning must be meaningful and reasonable for them to be useful. Preparing data set to be used in machine learning is an important task to map between raw data input (cause) and output (result) relation. Google traces are dump tables that contain raw data about data center servers, workload jobs and tasks status, workload demands, data center provisioned resources and hardware reconfiguration. Machine learning considers input data set and output data set as a training learning model by relating these two correlated sets as normalized values for future use. In this work, a data cleaning and full analysis for Google data center traces have been done to be used in deep machine learning models, like conventional neural network or recurrent neural network with capability of using inline learning model. Data set are processed by re-normalizing resources capacities and workloads demands (jobs and tasks), and by removing or transforming non-available and opaque values into useful values. A novel correlation between input and output data sets for Google traces is introduced that relates workload demands with data center resources reconfiguration, and data center resources with data center capacity. The idea is to connect between demands and provisioned resources by evaluating the cloud data center configuration sets, which allows cloud manager to provision resources by setting up the best reconfiguration in scaling cloud data center. An evaluation factor (scale factor) has been introduced to evaluate elastic provisioning resources considering resources preparation time and minimum cost.
Detection and Identification of Vehicles from High-Resolution Aerial Images Using Deep Learning Approaches with the Tuned Parameters

Unmanned aerial vehicles have been utilized in various applications for the management of the civil and defense infrastructure. These applications include the surveillance of the crowd, transport, and disaster management and the inspection of the building architecture. This excessive use of the unmanned aerial vehicles also demands the devices and the deployed system that devices are fully autonomous, efficient, and accurate. Classification of vehicle type is the major component of intelligent transportation system. Traditional approaches like (Bayesian prior models, scale-invariant feature transform, speeded-up robust features, and support vector machine) image processing and machine learning are used by many researchers to automate the transportation system. However, they failed to provide the promising results because of limited viewpoints and lack of dataset. With the advancement in technology, deep learning has inspired us all with its autonomous feature extraction and accuracy. In this chapter we present a deep learning-based approach which involves two phases: region proposal network and classification network; both networks use the features from convolution neural network (CNN). Proposed system first detects the vehicle and then classifies its brand type accurately on self-generated dataset with the help of UAVs. Proposed system classifies the vehicles with the average accuracy of 96.2% on six types of vehicles.
Relationships and interactions between school owners and school principals: A case study of a Norwegian school district programme

The literature has revealed that high-performing districts establish collaborative working relations with school leaders as a part of the strategy to improve student achievement. This study was designed to investigate the relationships and interactions that school owners and principals in municipalities with high-performing school results employ at the district level in a part of rural Norway. The district subjected to the study was characterized by small municipalities with a few school administrative units, which can be a challenging context for school owners’ involvement and interaction with principals. The research design involved a collective case study and draws on data from surveys, interviews and local school policy documents. The findings suggest that the proximity between neighbouring professional actors seems to be important, and that rural school district actors should build systemic competence and a purposefully tailored infrastructure to reinforce interactions and relationships with the subordinated principals. Further, the findings highlight the advantage of collaborative governance across municipal boundaries and the significance of a trust-based professional network for inter-organizational learning to flourish.
Learning Decorrelated Hashing Codes With Label Relaxation for Multimodal Retrieval

Due to the correlation among hashing bits, the retrieval performance improvement becomes slower when the hashing code length becomes longer. Existing methods try to regularize the projection matrix as an orthogonal matrix to decorrelate hashing codes. However, the binarization of projected data may completely break the orthogonality. In this paper, we propose a minimum correlation regularization (MCR) for multimodal hashing. Rather than being imposed on projection matrix, MCR is imposed on a differentiable function which approximates the binarization. On the other hand, binary labels could not precisely reflect the distances among data. Hence, we propose a label relaxation scheme to achieve better performance.
An accelerated stochastic variance-reduced method for machine learning problems

Variance reduction techniques provide simple and fast algorithms for solving machine learning problems. In this paper, we present a novel stochastic variance-reduced method. The proposed method relies on the mini-batch version of stochastic recursive gradient algorithm (MB-SARAH), which updates stochastic gradient estimates by using a simple recursive scheme. However, facing the challenge of the step size sequence selection in MB-SARAH, we introduce an online step size sequence based on the hypergradient descent (HD) method, which only requires little additional computation. For the proposed method, referred to as MB-SARAH-HD, we provide a general convergence analysis and prove linear convergence for strongly convex problems in expectation. Specifically, we prove that the proposed method has sublinear convergence rate in a single outer loop. We also prove that the iteration complexity outperforms several variants of the state-of-the-art stochastic gradient descent (SGD) method under suitable conditions. Numerical experiments on standard datasets are provided to demonstrate the efficacy and superiority of our MB-SARAH-HD method over existing approaches in the literature.
New contextual collaborative filtering system with application to personalized healthy nutrition education

Nowadays, the Internet is becoming a platform of choice where the number of users and items grows dramatically making recommender systems (RS) the most required and widespread technology. This paper deals with context aware collaborative RS and presents a double contribution that consists of a two Dimensions Contextual Collaborative Recommender System (2DCCRS) and a related application. Our first contribution proposes a new framework for collaborative context aware RS that relies on two key ideas. The first one suggests splitting the context into two parts namely internal and external contexts in order to deal with both internal and external context attributes in different and more appropriate manners. This allows addressing the complexity of the context model in a more effective way. In the second idea, we introduce two concepts; namely the “Stakeholders” and “Aggregation” to effectively alleviate the problems of new user and new item. 2DCCRS is based on a multi-layer architecture. Its highest layer relies on a pre-filtering algorithm that deals with the cold start system problem, and is mainly based on the similarity between the user profile and the items features. The middle layer is based on a collaborative filtering algorithm that takes into account the users’ preferences, interests and priorities; while the deepest layer, which is considered the most relevant in our multilayer architecture, focuses on a post-filtering algorithm in which the recommendations are much more adapted to the user environment. In Our second contribution, we present a case study of 2DCCRS in order to demonstrate the usefulness and effectiveness of the proposed approach. Indeed, we propose a personalized Healthy and Tasty application (H&T) that generates items based on 2DCCRS framework to guide the user toward the healthy and tasty meals that best meet his needs. The obtained results are very promising and show the effectiveness of our proposal.
Recognition of Character from Handwritten

This idea deals with the identification of English letters. The input is given to process the character further, it is trained with the computer system modeled on nervous system to identify the design of characters. Pattern identification is one of the effective method used in neural networks. The purpose of neural network is to provide algorithm to recognize the letters. The neural network is given with a target which has the design information; it may be a representation of handwritten data with different size and styles. The neural network will establish the exact given details if it matches with the design that neural network has remembered. This project will be useful in identifying the letters of English and Tamil.
A SVM based Gender Classification from Children Facial Images using Local Binary and Non-Binary Descriptors

Gender being an important component within the interaction between individuals. Generally in face acknowledgment systems, local descriptors are broadly used as descriptors of features to attain more appealing results in dynamic conditions of the image such as lights & shades, pose etc. The complex characteristics of human confront are sex, facial expressions, age, ethnic origin etc. In this paper, we are taking a challenge to predict the gender of kids between 6 to 8 years by analyzing there features with various methods like Local Binary Pattern (LBP), Histogram Oriented Gradients (HOG) Local Directional Pattern (LDP), and the SVM-Support Vector Machine is utilized in defining the gender, this includes three kernel functions. Exponential results indicate that combination of HOG & SVM linear kernel get better performed on the CFID which we have created of about 60 images with an accuracy of 96.66 percentage.
Layerwise Noise Maximisation to Train Low-Energy Deep Neural Networks

Deep neural networks (DNNs) depend on the storage of a large number of parameters, which consumes an important portion of the energy used during inference. This paper considers the case where the energy usage of memory elements can be reduced at the cost of reduced reliability. A training algorithm is proposed to optimize the reliability of the storage separately for each layer of the network, while incurring a negligible complexity overhead compared to a conventional stochastic gradient descent training. For an exponential energy-reliability model, the proposed training approach can decrease the memory energy consumption of a DNN with binary parameters by 3.3× at isoaccuracy, compared to a reliable implementation.
A Secure Shoulder Surfing Resistant Hybrid Graphical User Authentication Scheme

User authentication is one crucial part of information security. Initially, the text-based password schemes were developed to secure the information, but there were some drawbacks. People choose short and weak passwords for easy memorization. Text-based password schemes suffer from the dictionary, brute force, shoulder surfing, phishing, social engineering attacks and many other. To overcome some of these shortcomings graphical password approaches were introduced. Studies have shown that pictures are remembered more often than text by humans. In this paper, a graphical user authentication scheme is proposed that is based on the hybrid method as it combines the positive aspects of graphical and text-based passwords. In the proposed scheme, images are combined with dynamic graphics and it is required to click instantly by the user when the desired color on the correct image at login time is displayed. The scheme is safe from shoulder surfing, guessing and brute force attacks.
Participatory Action Research and design pedagogy: Perspectives for design education

Design’s scope of practice has grown from one that was traditionally defined by materials and processes to one where designers are working on some of the most pressing challenges of our times. Once a reactive, artefact-based practice (e.g. poster, typeface, chair, etc.), design is now being situated as a proactive, social and participatory practice focused on outcomes as much as artefacts. Historically, as an academic subject, professional practice and research area, design has suffered from a lack of formal, established research frameworks and theoretical practices. By drawing on established literature, this article makes the case for the use of methods and practices developed in Participatory Action Research (PAR) to inform and enrich design practice, research and particularly education. The article identifies three shared areas between PAR and design that offer an opportunity for further interrogation; these are: a central concern of working with people; the use of iteration and reflection; and the measuring of success through change.
Face Depth Estimation and 3D Reconstruction

In the world of fast growing technology people look for more realistic representation and hence 3D representation of 2D images acquires great importance. 3D models are used in various fields like face recognition and animation games. They are widely used in medical industry to create interactive representations of human anatomy. However, generation of 3D models from 2D images is still one of the major challenges faced by researchers. Many methods have been introduced and developed for generating 3D representation. Here in our work, we used a Generative Adversarial Network(GAN) based model for estimating the depth map of a given face image. Pix2pix GAN, a variant of conditional GAN is used in this method. It is capable of performing image-to-image translation using the unsupervised method of machine learning. We found that it is the most robust method.