Miracle-dz/UniFAR-FTUs huggingface dataset:
VOICE VERIFICATION USING I-VECTORS AND NEURAL NETWORKS WITH LIMITED TRAINING DATA

This study proposes an approach to voice identification based on neural networks (DNN) for i-Vector. Modern voice identification systems based on DNN use large amounts of labeled training data. Using the LRE i-Vector Machine Learning Challenge restricts access to ready-to-use i-Vector for learning and testing the voice identification system. This poses unique challenges in developing DNN-based voice identification systems, since optimized external interfaces and network architectures can no longer be used. We propose to use the training i-Vectors to train the initial DNN to identify the voice. Next, we present a novel strategy for using this initial DNN to strip the language labels of the inappropriate set from the development data. The final DNN for voice identification is trained using the original training data and the estimated out-of-set language data. We show that augmenting the training set with out-of-set labels leads to a significant improvement in voice identification performance. In this paper, we studied the possibility of using neural networks for speech identification. In particular, standard approaches to speech recognition were considered, the concept of an artificial neuron as an object used in speech identification was defined. A speech recognition option using a neural network was investigated, and steps were presented to perform this task. Accuracy using neural networks with limited learning data and a higher i-vector dimension is superior to others with a score of 92.1%. From this study, we can conclude that the size of the UBM and the dimension of the i-vector affect the accuracy of voice identification based on the i-vector.
Predicting annotation difficulty using Monte Carlo dropout

Developing products based on machine learning algorithms require relevant and accurate datasets. In particular when it comes to supervised learning algorithms whose performance is directly related to the quality and amount of training data. Within the field of computer vision, classification is a task that require training data in the form of annotated images. Annotating images is a manual task and I propose that the annotation difficulty of an image should be interpreted as the likelihood of someone else annotating an image differently. Knowing in advance which images are hard to annotate would facilitate the distribution of work between annotators with varying experience. In this thesis, it is shown that the uncertainty derived from Monte Carlo dropout resembles the variance of a group of persons annotations of the same image. This finding indicates that the level of agreement between persons can be predicted, and thus enable for better distribution of work between annotators. Furthermore, the finding could also be used to order images during training by prioritizing harder images higher.
Snoopy: Sniffing Your Passwords from Motion Sensors on Smartwatches

Demand for smartwatches has taken o in recent years with new models which can run independently from smartphones and provide more useful features, becoming rst-class mobile platforms. One can access online banking or even make payments on a smartwatch without a paired phone. is makes smartwatches more aractive and vulnerable to malicious aacks, which to date have been largely overlooked. In this paper, we demonstrate Snoopy, a password extraction and inference system which is able to accurately infer passwords entered on Android/Apple watches within 20 aempts, just by eavesdropping on motion sensors. Snoopy uses a uniform framework to extract the segments of motion data when passwords are entered, and uses novel deep neural networks to infer the actual passwords. We evaluate the proposed Snoopy system in the real-world with data from 362 participants and show that our system oers a ∼ 3 fold improvement in the accuracy of inferring passwords compared to the state-of-the-art, without consuming excessive energy or computational resources. We also show that Snoopy is very resilient to user and device heterogeneity: it can be trained on crowd-sourced motion data (e.g. via Amazon Mechanical Turk), and then used to aack passwords from a new user, even if they are wearing a dierent model. is paper shows that, in the wrong hands, Snoopy can potentially cause serious leaks of sensitive information. By raising awareness, we invite the community and manufacturers to revisit the risks of continuous motion sensing on smart wearable devices.
Automatic recognition of Sign Language structures in RGB videos: the detection of pointing and lexical signs

This work presents a generic approach to tackle continuous Sign Language Recognition (SLR) in ordinary RGB Sign Language videos. While usual SLR systems only analyze SL through the lexical level, it is shown here that both lexical and SL-specific features can be accurately detected and localized. This paves the way to better understanding Sign Language linguistics, as well as benefiting the Deaf community in need of SL-specific tools like video querying. Generic human motion features are first extracted from videos, so that a very compact modeling is obtained and future training time is limited. A Recurrent Neural Network is then trained on those generic features to recognize lexicon and SL-specific structures like pointing. Applied to the French Sign Language corpus Dicta-Sign, pointing signs detection gets a 78% sequence-wise F1-score on 4 seconds chunks. The network also gets a 70% sequence-wise F1-score for the detection of lexical signs with less than 50 instances in the training set. These are very promising results for a first SLR trial on a French Sign Language corpus, given its relatively short total duration, its low image resolution and frame rate, and the unconstrained nature of the recorded dialogs.
High Performance Spark: Best Practices for Scaling and Optimizing Apache Spark

Apache Spark is amazing when everything clicks. But if you havent seen the performance improvements you expected, or still dont feel confident enough to use Spark in production, this practical book is for you. Authors Holden Karau and Rachel Warren demonstrate performance optimizations to help your Spark queries run faster and handle larger data sizes, while using fewer resources. Ideal for software engineers, data engineers, developers, and system administrators working with large-scale data applications, this book describes techniques that can reduce data infrastructure costs and developer hours. Not only will you gain a more comprehensive understanding of Spark, youll also learn how to make it sing. With this book, youll explore: How Spark SQLs new interfaces improve performance over SQLs RDD data structure The choice between data joins in Core Spark and Spark SQL Techniques for getting the most out of standard RDD transformations How to work around performance issues in Sparks key/value pair paradigm Writing high-performance Spark code without Scala or the JVM How to test for functionality and performance when applying suggested improvements Using Spark MLlib and Spark ML machine learning libraries Sparks Streaming components and external community packages
Cross-Modal Commentator: Automatic Machine Commenting Based on Cross-Modal Information

Automatic commenting of online articles can provide additional opinions and facts to the reader, which improves user experience and engagement on social media platforms. Previous work focuses on automatic commenting based solely on textual content. However, in real-scenarios, online articles usually contain multiple modal contents. For instance, graphic news contains plenty of images in addition to text. Contents other than text are also vital because they are not only more attractive to the reader but also may provide critical information. To remedy this, we propose a new task: cross-model automatic commenting (CMAC), which aims to make comments by integrating multiple modal contents. We construct a large-scale dataset for this task and explore several representative methods. Going a step further, an effective co-attention model is presented to capture the dependency between textual and visual information. Evaluation results show that our proposed model can achieve better performance than competitive baselines.
K-Multiple-Means: A Multiple-Means Clustering Method with Specified K Clusters

In this paper, we make an extension of K-means for the clustering of multiple means. The popular K-means clustering uses only one center to model each class of data. However, the assumption on the shape of the clusters prohibits it to capture the non-convex patterns. Moreover, many categories consist of multiple subclasses which obviously cannot be represented by a single prototype. We propose a K-Multiple-Means (KMM) method to group the data points with multiple sub-cluster means into specified k clusters. Unlike the methods which use the agglomerative strategies, the proposed method formalizes the multiple-means clustering problem as an optimization problem and updates the partitions of m sub-cluster means and k clusters by an alternating optimization strategy. Notably, the partition of the original data with multiple-means representation is modeled as a bipartite graph partitioning problem with the constrained Laplacian rank. We also show the theoretical analysis of the connection between our method and the K-means clustering. Meanwhile, KMM is linear scaled with respect to n. Experimental results on several synthetic and well-known real-world data sets are conducted to show the effectiveness of the proposed algorithm.
SimWorx: an Ada 95 distributed simulation application framework supporting HLA and DIS

This paper describes SimWorx, a reusable object-oriented application framework for distributed simulation which is compliant with both the DoD High Level Architecture (HLA) for Modeling and Simulation and the Distributed Interactive Simulation (DIS) standards. SimWorx provides a bridge between legacy DIS-based applications and the HLA. A researcher can create a new simulation which is an HLA federate using SimWorx which provides a built-in scheme for simulation operation. Then, by using the SimWorx DIS Surrogate Run-Time Infrastructure (RTI) in place of the standard HLA RTI (RTI F.0), the new simulation can interoperate with legacy applications. SimWorx was implemented in Ada 95 using software engineering methods. It is a concurrent system which takes advantage of Ada's task construct to ensure time-sensitive operations like dead-reckoning can take place while minimizing impact to other parts of the system design. It consists of numerous cooperating object-oriented classes. Client developers use inheritance extension to customize the behavior of the framework.
Investment Behaviors Can Tell What Inside: Exploring Stock Intrinsic Properties for Stock Trend Prediction

Stock trend prediction, aiming at predicting future price trend of stocks, plays a key role in seeking maximized profit from the stock investment. Recent years have witnessed increasing efforts in applying machine learning techniques, especially deep learning, to pursue more promising stock prediction. While deep learning has given rise to significant improvement, human investors still retain the leading position due to their understanding on stock intrinsic properties, which can imply invaluable principles for stock prediction. In this paper, we propose to extract and explore stock intrinsic properties to enhance stock trend prediction. Fortunately, we discover that the repositories of investment behaviors within mutual fund portfolio data form up a gold mine to extract latent representations of stock properties, since such collective investment behaviors can reflect the professional fund managers' common beliefs on stock intrinsic properties. Powered by extracted stock properties, we further propose to model the dynamic market state and trend using stock representations so as to generate the dynamic correlation between the stock and the market, and then we aggregate such correlation with dynamic stock indicators to achieve more accurate stock prediction. Extensive experiments on real-world stock market data demonstrate the effectiveness of stock properties extracted from collective investment behaviors in the task of stock prediction.
Proposed Battery Storage System Model Connected to the Grid

Batteries are highly sensitive to environmental conditions such as temperature and heat, due to energy exchanges through electrochemical reactions, and these factors will diminish their useful life. However, the increasing development of distributed products and using renewable energy to provide electrical energy leads to more usage of batteries in power systems. Battery cell structure cause that desired capacity, power and voltage of battery storage system to be achieved through connecting different number of battery cells. In this paper, battery storage system connected to the network is based on accurate modeling system, control consisting of flow control strategy and voltage control strategy. Flow control strategy has the duty of DC voltage regulation and power factor control. In addition, it is expected that current control strategy of battery storage system to have good control against external fault and increase system reliability. Sampling from distribution sensors has been specified to obtain a real-time delay between system controls that is stable according to the characteristics of analysis network. keywords— energy storage system , bidirectional dc–dc converter, battery system controller.
Development of a Game Controller Interface for Soundgraphs

Soundgraphs have been researched as a way of communicating mathematical information to blind people for nearly 30 years. During this time a number of different user interfaces of varying levels of complexity have been suggested and tested. However, none have really taken hold. The aim of this project is to implement a user interface on top of a previously created soundgraph representation. Game controllers have been suggested as the primary input to the interface. This is due to the continuing development in the games industry leading to ever better, more easily used, and more adaptable controllers. In particular, motion sensing controllers such as the Microsoft Kinect and Nintendo Wii remote have showed promise in other assistive technologies. There will also be an evaluation of the interface undertaken to identify areas for future development.
The Cross-Sequential Approach: A Short-Term Method for Studying Long-Term User Experience

Copyright © 2018–2019, User Experience Professionals Association and the authors. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. URL: http://www.upassoc. org. The Cross-Sequential Approach: A Short-Term Method for Studying Long-Term User Experience
Survey on Multiframe Super-Resolution

Super-resolution technique is used for resolution enhancement. In multiframe super-resolution multiple low resolution images are combined to get high resolution image.This paper addresses multiframe super-resolution in which one or more low resolution images are combined to get high resolution image which can increase spatial resolution of image and different image quality matrices to measure the quality of the original image and reconstructed image.
Usability of an Immersive Augmented Reality Based Telerehabilitation System with Haptics (ARTESH) for Synchronous Remote Musculoskeletal Examination

This study describes the features and utility of a novel augmented reality based telemedicine system with haptics that allows the sense of touch and direct physical examination during a synchronous immersive telemedicine consultation and physical examination. The system employs novel engineering features: (a) a new force enhancement algorithm to improve force rendering and overcoming the “just-noticeable-difference” limitation; (b) an improved force compensation method to reduce the delay in force rendering; (c) use of the “haptic interface point” to reduce disparity between the visual and haptic data; and (d) implementation of efficient algorithms to process, compress, decompress, transmit and render 3-D tele-immersion data. A qualitative pilot study (n=20) evaluated the usability of the system. Users rated the system on a 26-question survey using a seven-point Likert scale, with percent agreement calculated from the total users who agreed with a given statement. Survey questions fell into three main categories: (1) ease and simplicity of use, (2) quality of experience, and (3) comparison to in-person evaluation. Average percent agreements between the telemedicine and in-person evaluation were highest for ease and simplicity of use (86%) and quality of experience (85%), followed by comparison to in-person evaluation (58%). Eighty-nine percent (89%) of respondents expressed satisfaction with the overall quality of experience. Results suggest that the system was effective at conveying audio-visual and touch data in real-time across 20.3 miles, and warrants further development.
Generating Diverse Translations with Sentence Codes

Users of machine translation systems may desire to obtain multiple candidates translated in different ways. In this work, we attempt to obtain diverse translations by using sentence codes to condition the sentence generation. We describe two methods to extract the codes, either with or without the help of syntax information. For diverse generation, we sample multiple candidates, each of which conditioned on a unique code. Experiments show that the sampled translations have much higher diversity scores when using reasonable sentence codes, where the translation quality is still on par with the baselines even under strong constraint imposed by the codes. In qualitative analysis, we show that our method is able to generate paraphrase translations with drastically different structures. The proposed approach can be easily adopted to existing translation systems as no modification to the model is required.
Towards individual thermal comfort : model predictive personalized control of heating systems

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Box-Scan: An Efficient and Effective Algorithm for Box Dimension Measurement in Conveyor Systems using a Single RGB-D Camera

Different algorithms for object dimension measurement have recently been proposed, leveraging the power of RGB-D cameras. However, most of these algorithms are not suitable for deployment in the logistics industry: they are only able to deal with non-moving objects and/or the usage of multiple cameras introduces time delays. In this paper, we introduce Box-Scan, a novel algorithm that enables real-time box dimension measurement in conveyor systems using a single RGB-D camera. We discuss the industrial setting in which our algorithm needs to operate, as well as a prototype that integrates the proposed algorithm. Furthermore, we provide an analysis of the effectiveness of our prototype as a function of the conveyor speed, demonstrating that the prototype built comes with a maximum measurement error of less than 5% at a conveyor speed of 3.4 km/h.
Distortion Robust Image Classification with Deep Convolutional Neural Network based on Discrete Cosine Transform

State of the art CNN models for image classification are found to be highly vulnerable to image quality degradation. It is observed that even a small amount of distortion introduced in an image in the form of noise or blur severely hampers the performance of these CNN architectures. Most of the work in the literature strive to mitigate this problem simply by fine-tuning a pre-trained model on mutually exclusive or union set of distorted training data. This iterative fine-tuning process with all possible types of distortion is exhaustive and struggles to handle unseen distortions. In this work, we propose DCT-Net, a Discrete Cosine Transform based module integrated into a deep network which is built on top of VGG16 \cite{vgg1}. The proposed DCT module operates during training and discards input information based on DCT coefficients which represent the contribution of sampling frequencies. We show that this approach enables the network to be trained at one go without having to generate training data with different type of expected distortions. We also extend the idea of traditional dropout and present a training adaptive version of the same. During tests, we introduce Gaussian blur, motion blur, salt and pepper noise, Gaussian white noise and speckle noise to CIFAR10, CIFAR-100 \cite{cifar1} and ImageNet \cite{imagenet1} dataset. We evaluate our deep network on these benchmark databases and show that it not only generalizes well to a variety of image distortions but also outperforms sate-of-the-art.
Mutual Coupling Suppression With Decoupling Ground for Massive MIMO Antenna Arrays

A concept of decoupling ground is introduced in this paper to enhance the isolation of massive MIMO antenna arrays. For an array, mutual coupling between array elements can be achieved by the free-space coupling and the coupling currents flowing on the ground plane shared by elements. The isolation in this paper is improved by adjusting the shape of the ground plane under each element to make the mutual coupling from the free space and the ground plane out of phase. In this way, low mutual coupling is realized. As the first example, a single-polarization linear array with eight elements is designed, simulated, and measured to verify the concept as well as the simulation accuracy. The measurements align very well with the simulations. Another two examples of dual-polarization 2×2 and 4×4 square arrays are also given and simulated to demonstrate the effectiveness of the proposed technique. In all of these examples, the isolation can efficiently be enhanced with the DG for all the co-polarization and cross-polarization coupling paths of the massive MIMO elements. Compared with the previous literature, the arrays with the DG method can achieve either much better isolation or a much lower profile while keeping the other performance comparable.