Reviewerly/exHarmony huggingface dataset:
Augmented reality, virtual reality and digital games: A research on teacher candidates

Virtual reality, although it is in the real world, is a three-dimensional simulation model that brings users to a different environment with computer-generated graphics, video and audio, and enables communication with the media. Virtual reality is the imitation of the physical structure from a real-world or an imaginary world in a computer-generated environment. Augmented reality is the combination of the real world with the virtual world, the creation of enriched environments using virtual objects, the combination of physical reality and digital holograms, or the creation of a virtual world suitable for the purpose by using digital products. The aim of this study is to determine the opinions of teacher candidates studying in the faculty of education in terms of augmented reality, virtual reality and digital games depending on some demographic variables. The working group of the study consisted of teacher candidates studying in different departments of the Faculty of Education of Mustafa Kemal University in 2018-2019 Academic Year. Mixed model and General survey model were used in the study. In order to determine the opinions of teacher candidates, Using Digital Educational Plays Scale was used as a data collection tool. As a result of the research, there was no statistically significant difference between the opinions of the teacher candidates about the use of digital educational games in terms of the affective component, perceived usefulness, perceived control and the scale generally in terms of the scale, however, however, it has been concluded that there is a difference in favor of women in sub-dimension of affective components. In addition, in the research, teacher candidates were not afraid to use computer, but they were afraid of playing computer games.
Uncertainty Measurements for the Reliable Classification of Mammograms

We propose an efficient approach to estimate the uncertainty of deep-neural network classifiers based on the tradeoff of two measurements. The first based on subjective logic and the evidence of soft-max predictions and the second, based the Mahalanobis distance between new and training samples in the embedding space. These measurements require neither modifying, nor retraining, nor multiple testing of the models. We evaluate our methods on different classification tasks including breast cancer risk, breast density, and patch-wise tissue type and considering both an in-house database of 1600 mammographies, as well as on the public INBreast dataset. Throughout the experiments, we show the ability of our method to reject the most evident outliers, and to offer AUC gains of up to 10%, when keeping 60% of most certain samples.
Exploring Regularizations with Face, Body and Image Cues for Group Cohesion Prediction

This paper presents our approach for the group cohesion prediction sub-challenge in the EmotiW 2019. The task is to predict group cohesiveness in images. We mainly explore several regularizations with three types of visual cues, namely face, body ,and global image. Our main contribution is two-fold. First, we jointly train the group cohesion prediction task and group emotion recognition task using multi-task learning strategy with all visual cues. Second, we elaborately design two regularizations, namely a rank loss and a hourglass loss, where the former aims to give a margin between the distance of distant categories and near categories and the later to avoid centralization predictions with only MSE loss. With careful evaluations, we finally achieve the second place in this sub-challenge with MSE of 0.43821 on the testing set. https://github.com/DaleAG/Group_Cohesion_Prediction
Optimizing Multi-Query Evaluation in Federated RDF Systems

This paper revisits the classical problem of multiple query optimization in federated RDF systems. We propose a heuristic query rewriting-based approach to optimize the evaluation of multiple queries. This approach can take advantage of SPARQL 1.1 to share the common computation of multiple queries while considering the cost of both query evaluation and data shipment. Although we prove that finding the optimal rewriting for multiple queries is NP-complete, we propose a heuristic rewriting algorithm with a bounded approximation ratio. Furthermore, we propose an efficient method to use the interconnection topology between RDF sources to filter out irrelevant sources, and utilize some characteristics of SPARQL 1.1 to optimize multiple joins of intermediate matches. The extensive experimental studies show that the proposed techniques are effective, efficient and scalable.
Cybercrime risk awareness rate among students in Central Asia: A comparative study in Kyrgyzstan and Kazakhstan

It is a well-known fact that the system is as secure as its weakest point. Often, organizations and countries invest in the technologies, forgetting that it is impossible to assure information security without raising awareness among users. Therefore, in this study, the cybercrime awareness rate of users in two Central Asian Countries was investigated. As a target group, students of two public universities from each country were chosen. Results of quantitative research showed that in Kazakhstan, gender and age of respondents affect the cybercrime awareness rate, while in Kyrgyzstan, none of the factors had an impact on this rate. Although there was a statistically significant difference in cybercrime awareness of users in terms of the country of residence, students in two Central Asian countries showed a little difference in information security knowledge, which is, in its own term, mostly depends on the computer literacy rate of students.
Identification of POS Tag for Khasi Language based on Hidden Markov Model POS Tagger

Computational Linguistic (CL) becomes an essential and important amenity in the present scenarios, as many different technologies are involved in making machines to understand human languages. Khasi is the language which is spoken in Meghalaya, India. Many Indian languages have been researched in different fields of Natural Language Processing (NLP), whereas Khasi lacks substantial research from the NLP perspectives. Therefore, in this paper, taking POS tagging as one of the key aspects of NLP, we present POS tagger based on Hidden Markov Model (HMM) for Khasi language. In this present preliminary stage of building NLP system for Khasi, with the analyses of the categories and structures of the words is started. Therefore, we have designed specific POS tagsets to categories Khasi words and vocabularies. Then, the POS system based on HMM is trained by using Khasi words which have been tagged manually using the designed tagsets. As ambiguity is one of the main challenges in POS tagging in Khasi, we anticipated difficulties in tagging. However, by running with the first few sets of data in the experimental data by using the HMM tagger we found out that the result yielded by this model is 76.70% of accurate.
Conceptualize and Infer User Needs in E-commerce

Understanding latent user needs beneath shopping behaviors is critical to e-commercial applications. Without a proper definition of user needs in e-commerce, most industry solutions are not driven directly by user needs at current stage, which prevents them from further improving user satisfaction. Representing implicit user needs explicitly as nodes like "outdoor barbecue" or "keep warm for kids" in a knowledge graph, provides new imagination for various e- commerce applications. Backed by such an e-commerce knowledge graph, we propose a supervised learning algorithm to conceptualize user needs from their transaction history as "concept" nodes in the graph and infer those concepts for each user through a deep attentive model. Online experiments demonstrate the effectiveness and stability of our model, and online industry strength tests show substantial advantages of such user needs understanding.
The Value of Data: Towards a Framework to Redistribute It

This note attempts a systematisation of different pieces of literature that underpin the recent policy and academic debate on the value of data. It mainly poses foundational questions around the definition, economic nature and measurement of data value, and discusses the opportunity to redistribute it. It then articulates a framework to compare ways of implementing redistribution, distinguishing between data as capital, data as labour or data as an intellectual property. Each of these raises challenges, revolving around the notions of data property and data rights, that are also briefly discussed. The note concludes by indicating areas for policy considerations and a research agenda to shape the future structure of data governance more at large.
Differentially Private Algorithms for Learning Mixtures of Separated Gaussians

Learning the parameters of Gaussian mixture models is a fundamental and widely studied problem with numerous applications. In this work, we give new algorithms for learning the parameters of a high-dimensional, well separated, Gaussian mixture model subject to the strong constraint of differential privacy. In particular, we give a differentially private analogue of the algorithm of Achlioptas and McSherry. Our algorithm has two key properties not achieved by prior work: (1) The algorithm’s sample complexity matches that of the corresponding non-private algorithm up to lower order terms in a wide range of parameters. (2) The algorithm requires very weak a priori bounds on the parameters of the mixture components.
Global Convergence of Gradient Descent for Deep Linear Residual Networks

We analyze the global convergence of gradient descent for deep linear residual networks by proposing a new initialization: zero-asymmetric (ZAS) initialization. It is motivated by avoiding stable manifolds of saddle points. We prove that under the ZAS initialization, for an arbitrary target matrix, gradient descent converges to an $\varepsilon$-optimal point in $O\left( L^3 \log(1/\varepsilon) \right)$ iterations, which scales polynomially with the network depth $L$. Our result and the $\exp(\Omega(L))$ convergence time for the standard initialization (Xavier or near-identity) \cite{shamir2018exponential} together demonstrate the importance of the residual structure and the initialization in the optimization for deep linear neural networks, especially when $L$ is large.
Lookahead: A Far-sighted Alternative of Magnitude-based Pruning

Magnitude-based pruning is one of the simplest methods for pruning neural networks. Despite its simplicity, magnitude-based pruning and its variants demonstrated remarkable performances for pruning modern architectures. Based on the observation that the magnitude-based pruning indeed minimizes the Frobenius distortion of a linear operator corresponding to a single layer, we develop a simple pruning method, coined lookahead pruning, by extending the single layer optimization to a multi-layer optimization. Our experimental results demonstrate that the proposed method consistently outperforms the magnitude pruning on various networks including VGG and ResNet, particularly in the high-sparsity regime.
Efficient and Information-Preserving Future Frame Prediction and Beyond

Applying resolution-preserving blocks is a common practice to maximize information preservation in video prediction, yet their high memory consumption greatly limits their application scenarios. We propose CrevNet, a Conditionally Reversible Network that uses reversible architectures to build a bijective two-way autoencoder and its complementary recurrent predictor. Our model enjoys the theoretically guaranteed property of no information loss during the feature extraction, much lower memory consumption and computational efficiency. The lightweight nature of our model enables us to incorporate 3D convolutions without concern of memory bottleneck, enhancing the model's ability to capture both short-term and long-term temporal dependencies. Our proposed approach achieves state-of-the-art results on Moving MNIST, Traffic4cast and KITTI datasets. We further demonstrate the transferability of our self-supervised learning method by exploiting its learnt features for object detection on KITTI. Our competitive results indicate the potential of using CrevNet as a generative pre-training strategy to guide downstream tasks.
Dynamics-Aware Embeddings

In this paper we consider self-supervised representation learning to improve sample efficiency in reinforcement learning (RL). We propose a forward prediction objective for simultaneously learning embeddings of states and actions. These embeddings capture the structure of the environment's dynamics, enabling efficient policy learning. We demonstrate that our action embeddings alone improve the sample efficiency and peak performance of model-free RL on control from low-dimensional states. By combining state and action embeddings, we achieve efficient learning of high-quality policies on goal-conditioned continuous control from pixel observations in only 1-2 million environment steps.
Online Metric Learning for Multi-Label Classification

Existing research into online multi-label classification, such as online sequential multi-label extreme learning machine (OSML-ELM) and stochastic gradient descent (SGD), has achieved promising performance. However, these works lack an analysis of loss function and do not consider label dependency. Accordingly, to fill the current research gap, we propose a novel online metric learning paradigm for multi-label classification. More specifically, we first project instances and labels into a lower dimension for comparison, then leverage the large margin principle to learn a metric with an efficient optimization algorithm. Moreover, we provide theoretical analysis on the upper bound of the cumulative loss for our method. Comprehensive experiments on a number of benchmark multi-label datasets validate our theoretical approach and illustrate that our proposed online metric learning (OML) algorithm outperforms state-of-the-art methods.
Neural Conversation Generation with Auxiliary Emotional Supervised Models

An important aspect of developing dialogue agents involves endowing a conversation system with emotion perception and interaction. Most existing emotion dialogue models lack the adaptability and extensibility of different scenes because of their limitation to require a specified emotion category or their reliance on a fixed emotional dictionary. To overcome these limitations, we propose a neural conversation generation with auxiliary emotional supervised model (nCG-ESM) comprising a sequence-to-sequence (Seq2Seq) generation model and an emotional classifier used as an auxiliary model. The emotional classifier was trained to predict the emotion distributions of the dialogues, which were then used as emotion supervised signals to guide the generation model to generate diverse emotional responses. The proposed nCG-ESM is flexible enough to generate responses with emotional diversity, including specified or unspecified emotions, which can be adapted and extended to different scenarios. We conducted extensive experiments on the popular dataset of Weibo post--response pairs. Experimental results showed that the proposed model was capable of producing more diverse, appropriate, and emotionally rich responses, yielding substantial gains in diversity scores and human evaluations.
Part-of-Speech Tagger for Biomedical Domain Using Deep Neural Network Architecture

POS tagging is the process of classifying words into their parts of speech like noun, verb, preposition etc. to a word. It is the most important and basic process in NLP. It is acts as an essential preprocess for other applications in natural language processing (NLP) like sentiment analysis, NER, speech recognition and so on. POS tagging is treated as a sequence labeling problem in which it labels words with their appropriate Part-Of-Speech. This work implementing a POS tagger for biomedical domain using deep neural network architecture. The experiment is RNN, LSTM, and GRU will give better performance since they are able to access more context information and which we evaluated using publicly accessible dataset from GENIA. Most of the applications in NLP became solved due to the advancement of neural network or deep learning.
Privacy-Preserving Public Release of Datasets for Support Vector Machine Classification

We consider the problem of publicly releasing a dataset for support vector machine classification while not infringing on the privacy of data subjects (i.e., individuals whose private information is stored in the dataset). The dataset is systematically obfuscated using an additive noise for privacy protection. Motivated by the Cramér-Rao bound, inverse of the trace of the Fisher information matrix is used as a measure of the privacy. Conditions are established for ensuring that the classifier extracted from the original dataset and the obfuscated one are close to each other (capturing the utility). The optimal noise distribution is determined by maximizing a weighted sum of the measures of privacy and utility. The optimal privacy-preserving noise is proved to achieve local differential privacy. The results are generalized to a broader class of optimization-based supervised machine learning algorithms. Applicability of the methodology is demonstrated on multiple datasets.
Fusion of Elliptical Extended Object Estimates Parameterized With Orientation and Axes Lengths

This article considers the tracking of elliptical extended targets parameterized by center, orientation, and semiaxes. The focus of this article lies on the fusion of extended target estimates, e.g., from multiple sensors, by handling the ambiguities in this parameterization and the unclear meaning of the mean square error. For this purpose, we introduce a novel Bayesian framework for elliptic extent estimation and fusion based on two new concepts: 1) A probability density function for ellipses called random ellipse density which incorporates the ambiguities that come with the ellipse parameterization, and 2) the minimum mean Gaussian Wasserstein (MMGW) estimate, which is optimal with respect to the squared Gaussian Wasserstein (GW) distance-A suitable distance metric on ellipses. We develop practical algorithms for ellipse fusion and approximating the MMGW estimate. Different implementations, e.g., based on Monte Carlo simulation, are introduced and compared to state-of-the-art methods, highlighting the benefits of estimators tailored to the GW distance.
Learning a Single Neuron with Gradient Methods

We consider the fundamental problem of learning a single neuron $x \mapsto\sigma(w^\top x)$ using standard gradient methods. As opposed to previous works, which considered specific (and not always realistic) input distributions and activation functions $\sigma(\cdot)$, we ask whether a more general result is attainable, under milder assumptions. On the one hand, we show that some assumptions on the distribution and the activation function are necessary. On the other hand, we prove positive guarantees under mild assumptions, which go beyond those studied in the literature so far. We also point out and study the challenges in further strengthening and generalizing our results.
Geometry parameter calibration for focused plenoptic cameras

Due to the subtle structure, the exact geometry parameters of the focused plenoptic camera cannot be retrieved after packaging, which leads to inaccurate light field processing such as visible artifacts in the rendering images. This paper proposes a novel blind calibration method to calculate the geometry parameters for the focused plenoptic cameras with high precision. It translates the problem of deriving the value of the geometry parameters to be the problem of deriving the pixel patch-size of each micro-image used in subaperture image rendering based on the geometry projection of the relay imaging process in the focused plenoptic camera. Then, a dark image calibration algorithm is proposed to retrieve the position and the geometry parameters of the MLA for subaperture image rendering. A triple-level calibration board with random texture is designed to realize focus plane confirming blindly, to facilitate capturing light field images at different object distances via a single shot and to benefit intensity feature matching in determining the rendering patch size. The rendering patch-size is found by the proposed Gradient-SSIM-based fractional-pixel matching based on the geometry projection analysis. Experiments conducted on the simulated data and the real imaging system demonstrate that the proposed method can acquire the geometry parameters with high accuracy and is robust to different focused plenoptic cameras.
SO-Net: Joint Semantic Segmentation and Obstacle Detection Using Deep Fusion of Monocular Camera and Radar

Vision-based semantic segmentation and obstacle detection are important perception tasks for autonomous driving. Vision-based semantic segmentation and obstacle detection are performed using separate frameworks resulting in increased computational complexity. Vision-based perception using deep learning reports state-of-the-art accuracy, but the performance is susceptible to variations in the environment. In this paper, we propose a radar and vision-based deep learning perception framework termed as the SO-Net to address the limitations of vision-based perception. The SO-Net also integrates the semantic segmentation and object detection within a single framework. The proposed SO-Net contains two input branches and two output branches. The SO-Net input branches correspond to vision and radar feature extraction branches. The output branches correspond to object detection and semantic segmentation branches. The performance of the proposed framework is validated on the Nuscenes public dataset. The results show that the SO-Net improves the accuracy of the vision-only-based perception tasks. The SO-Net also reports reduced computational complexity compared to separate semantic segmentation and object detection frameworks.
Levelized Cost of Energy (LCOE) Analysis

The cost of the electric energy MWh is discussed and different energy sources are compared with MWh produced by photovoltaic (PV) plants and by floating PV (FPV) plants. The basic component of the price for a PV system is analyzed and the investment cost discussed. The method suggested is applied to typical examples of FPV plants, and a comparison of the investment with land-based PV plant is given. The energy cost is calculated under standard finance hypothesis and assuming a given annual energy yield. Cost analysis for different locations is given.
Character Recognition of MODI Script Using Distance Classifier Algorithms

Machine simulation of human reading is an active research area since the introduction of digital computers. Optical character recognition aims at the recognition of printed or handwritten text from document images and converting the same into a machine-readable form. The focus of this work is handwritten character recognition of MODI Script. A proper recognition system for handwritten documents enables it to be conveniently viewed, edited, and shared via electronic means. The development of a character recognition system for some of the ancient script is still a challenging task due to the complex nature of the script. MODI script is one such script which is the shorthand form of the Devanagari script in which Marathi was written. Though at present MODI script is not an official script, there exists a huge collection of MODI documents in various libraries. In addition, it is observed that scholars and historians are taking serious effort to revive the script. The purposed study based on the implementation of two algorithms for the classification of handwritten MODI script. The algorithms use distance classifier method. The first experiment is done using Euclidean distance classifiers and the second one is with Manhattan distance classifier and the accuracy achieved is 99.28% & 94% respectively.