Reviewerly/exHarmony huggingface dataset:
Voice Puppetry

State-of-the-art speech synthesis owes much to modern AI machine learning, with recurrent neural networks becoming the new standard. However, how you say something is just as important as what you say. If we draw inspiration from human dramatic performance, ideas such as artistic direction can help us design interactive speech synthesis systems which can be finely controlled by a human voice. This "voice puppetry" has many possible applications from film dubbing to the pre-creation of prompts for a conversational agent. Previous work in voice puppetry has raised the question of how such a system should work and how we might interact with it. Here, we share the results of a focus group discussing voice puppetry and responding to a voice puppetry demo. Results highlight a main challenge in user-centred AI: where is the trade-off between control and automation? and how may users control this trade-off?
A Reference Architecture for Applications with Conversational Components

Providing a multi-modal user interface adds value to any application. Allowing users to speak or chat with the system is one such area where software practitioners are putting a lot of effort. This involves building components which can understand the nuances of human conversation. Such components, often called “chatbots, can be built either from scratch, or using a commercial platform. The process of architecting such applications may differ significantly from the “conventional” applications that the software practitioners usually build. In this work, we present a Reference Architecture for building such applications. We apply the Reference Architecture to a sample use-case and provide two Concrete Architectures for the same. The two architectures are designed keeping in mind, two commercial platforms, IBM Watson Assistant and Google DialogFlow, assuming that they were used to build the conversational components.
Multi-Method Evaluation

Research in the field of information retrieval and recommendation mostly focuses on one single evaluation method and one single quality objective. On the one hand, many research endeavors focus on system-centric evaluation from an algorithmic perspective and consider the context of use only to a minor extent. On the other hand, there are research endeavors focusing on user-centric approaches to the design and evaluation of systems. However, algorithmic quality and perceived quality of user experience do not necessarily match. Thus, it is essential for system evaluation to substantially integrate multiple evaluation methods that cover a variety of relevant aspects and perspectives. Only such an integrated combination of methods may lead to a deep understanding of users, their behavior, and experience in their interaction with a system. This half-day tutorial follows the objective to raise awareness in the CHIIR community concerning the significance of using multiple methods in the evaluation of information retrieval and recommender systems. The tutorial illustrates the "blind spots'' when using single methods. It introduces the concept of "multi-method evaluation'' and discusses its benefits and challenges. While multi-method evaluations may be designed very flexibly, the tutorial presents broadly-defined basic options of how multiple methods may be integrated in an evaluation design. In group work, participants are encouraged to select and fine-tune a specific design that best matches their research endeavor's purpose.
Spoken Language Recognition Using CNN

This paper studies the use of Convolutional Neural Networks (CNNs) to address the problem of spoken language recognition. The solution uses Convolution Neural Networks in order to detect language specific phonemes. It supports 3 languages: German, English and Spanish. LibriVox Recordings were used to prepare the data set. The samples are equally balanced between languages, genders and speakers not to favour any subgroup. Attention has been paid to concentrate more on language properties than any specific voice. The data set has been boosted with data augmentation with transformations such as pitch, speed and noise. The input audio is normalized after which the filter banks are extracted. The pre-processed data is passed to the convolution neural network. The trained model generalizes well which was confirmed against real life content. The fact that audio samples were perfectly distributed among different languages and genders and the fact that speakers present in training set were not in validation set were the prominent reasons to achieve such a high performance on such a simple architecture.
Interference Detection and Recognition Based on Signal Reconstruction Using Recurrent Neural Network

Interference detection using deep neural network has recently received increasing attention due to its capability in learning rich features of data. In this paper, we proposed a low-complexity blind interference detection method. Our method operates on Time-frequency overlapped interference signal and can separate which from received signal. The proposed method uses AutoEncoder network to reconstruct the transmitted signal and separate interference signal. The AutoEncoder network consists of several layers of recurrent neural network (RNN) which is well-suited for learning representations from time-correlated data. Simulation results show that the separated interference signal has the same features of original interference, so it not only achieves good interference detection performance, but also can realize interference recognition.
Block Encryption Algorithm for Digital Information Using Open Keys for Selfgeneration of Closed Random Private Keys

This paper reports on development of a block encryption algorithm for digital information, which meets the cryptography requirements. In addition, due to the algorithm parameters, it provides reliable protection of confidential information. The algorithm can be implemented both as software and as a combination of hardware and software. Data is encrypted and sent in blocks of 128 bit each. Furthermore, the algorithm can be easily modified to process blocks of 128 bit, 256 bit, etc. The size of the block does not significantly affect the processing speed.The developed algorithm is based on Vernam's idea. Vernam suggested refusing deterministic keys, randomly generating the keys instead, while their size should be equal to the size of data. The proposed algorithm provides both a synchronous and in phase operation of the random generators on both transmitting and receiving ends. At the same time, both the sender and receiver have the same random key. Both symmetrical random and private keys are used in the algorithm.
Improving the Performance of Cryptographic Voting Protocols

Cryptographic voting protocols often rely on methods that require a large number of modular exponentiations. Corresponding performance bottlenecks may appear both on the server and the client side. Applying existing optimization techniques is often mentioned and recommended in the literature, but their potential has never been analyzed in depth. In this paper, we investigate existing algorithms for computing fixed-base exponentiations and product exponentiations. Both of them appear frequently in voting protocols. We also explore the potential of applying small-exponent techniques. It turns out that using these techniques in combination, the overall computation time can be reduced by two or more orders of magnitude.
Comparison of Multi Layered Percepton and Radial Basis Function Classification Performance of Lung Cancer Data

Abstract Lung cancer was the most commonly diagnosed cancer as well as the leading cause of cancer death in males in 2008 globally. The way used to detect lung cancer are through examination chest X-ray, Computed Tomography (CT) scan, and Magnetic Resonance Imaging results. The accurate and efisien analysis of the imaging results are important to ensure the minimal time processing. A computed assisted diagnosis system is the crusial research which can conduct the analysis efficiently and efectively. This paper aimed to compare the classification performances of Multi Layered Perceptron (MLP) and Radial Basis Function (RBF) techniques. The public lung cancer datasets was used as training and testing data in the classfication techniques. Ten fold cross validation was used for dividing data before classifying techniques. The accuracy performances are compared to check a better technique for classification step.
SeMemNN: A Semantic Matrix-Based Memory Neural Network for Text Classification

Text categorization is the task of assigning labels to documents written in a natural language, and it has numerous real-world applications including sentiment analysis as well as traditional topic assignment tasks. In this paper, we propose 5 different configurations for the semantic matrix-based memory neural network with end-to-end learning manner and evaluate our proposed method on two corpora of news articles (AG news, Sogou news). The best performance of our proposed method outperforms the baseline VDCNN models on the text classification task and gives a faster speed for learning semantics. Moreover, we also evaluate our model on small scale datasets. The results show that our proposed method can still achieve better results in comparison to VDCNN on the small scale dataset.
Using Augmented Reality and Ontologies to Co-design Assistive Technologies in Smart Homes

Smart homes provide alternative means to foster autonomy for frail people living at home. Oral and visual cues are produced to help people carrying out activities. This necessitates to determine which sensors and effectors to choose for monitoring activities, which is is not trivial. A Do-it-Yourself approach is proposed for caregivers who know the frail people habits but needs a user-friendly interaction.
Detecting Critical Responses from Deliberate Self-harm Videos on YouTube

YouTube is one of the leading social media platforms and online spaces for people who self-harm to search and view deliberate self-harm videos, share their experience and seek help via comments. These comments may contain information that signals a commentator could be at risk of potential harm. Due to a large amount of responses generated from these videos, it is very challenging for social media teams to respond to a vulnerable commentator who is at risk. We considered this issue as a multi-class problem and triaged viewers' comments into one of four severity levels. Using current state-of-the-art classifiers, we propose a model enriched with psycho-linguistic and sentiment features that can detect critical comments in need of urgent support. On average, our model achieved up to 60% precision, recall, and f1-score which indicates the effectiveness of the model.
Estimation and variable selection for mixture of joint mean and variance models

Mixture of regression models are one of the most important statistical data analysis tools in a heterogeneous population. Similar to modeling variance parameter in a homogeneous population, we apply the idea of joint mean and variance models to the mixture of regression models and propose a new class of models: mixture of joint mean and variance models to analyze the heteroscedastic normal data coming from a heterogeneous population in this paper. The problem of variable selection for the proposed models is considered. In particular, a modified Expectation-Maximization (EM) algorithm for estimating the model parameters is developed. The consistency and the oracle property of the penalized estimators are established. Properties of the estimators of the regression coefficients are evaluated through Monte Carlo simulations. Finally, a real data analysis is illustrated by the proposed methodologies.
Forgetting Factor Kalman Filter with Dependent Noise Processes

The paper addresses the problem of filtering the state of a normal dynamical process with a dependency between the process and the measurement noise variables in the presence of an inaccurate model description. As regards the time occurrence of the noise dependency, we discuss the dependency structure where both the variables are correlated at the same time. An adaptive formulation of the Kalman filter (KF) is designed in order to mitigate the impact of the process model uncertainty on the degradation of the filter performance. The filter we propose exploits the collaborative decision to introduce a variable forgetting factor into the time update to reduce artificially the effect of obsolete knowledge on the filtering solution. Within the decision-making rules, a loss functional quantifying the time update is constructed to optimally combine the prediction alternatives possessing the form of the normal probability density function (pdf). The result is an adjustment of the Kalman gain matrix in response to empirically confirmed performance.
Speech Command Classification System for Sinhala Language based on Automatic Speech Recognition

Conversational Artificial Intelligence is revolutionizing the world with its power of converting the conventional computer to a human-like-computer. Exploiting the speaker's intention is one of the major aspects in the field of conversational Artificial Intelligence. A significant challenge that hinders the effectiveness of identifying the speaker's intention is the lack of language resources. To address this issue, we present a domain-specific speech command classification system for Sinhala, a low-resourced language. It accomplishes intent detection for the spoken Sinhala language using Automatic Speech Recognition and Natural Language Understanding. The proposed system can be effectively utilized in value-added applications such as Sinhala speech dialog systems. The system consists of an Automatic Speech Recognition engine to convert continuous natural human voice in Sinhala language to its textual representation and a text classifier to accurately understand the user intention. We also present a novel dataset for this task, 4.15 hours of Sinhala speech corpus in the banking domain. Our new Sinhala speech command classification system provides an accuracy of 89.7% in predicting the intent of an utterance. It outperforms the state-of-the-art direct speech-to-intent classification systems developed for the Sinhala language. Moreover, the Automatic Speech Recognition engine shows the Word Error Rate as 12.04% and the Sentence Error Rate as 21.56%. In addition, our experiments provide useful insights on speech-to-intent classification to researchers in low resource spoken language understanding.
Creating job opportunities in computer assembly line for people with disabilities through augmented reality

This work presents the research to identify the most feasible way to deliver an augmented reality-based learning object to promote skill development among the deaf to prepare them to job positions in a computer manufacturing assembly line. A mobile app with augmented reality was developed to facilitate such training. To identify the best form of interaction the application had two forms of natural interaction and installed on two kinds of wearable devices. It allowed for the definition of four interaction set configurations. For identify the best arrangement, an experiment was carried out with 80 people (40 deaf and 40 hearing people) that allowed the analysis of quantitative and qualitative factors. After that, we evolved the tool to cope with real objects in the assembly line and assure. sustainable use.
Evaluation, optimization, and Enhancement of Chaos Based Reconfigurable Logic Design

Nonlinear dynamics and chaos can be utilized to build reconfigurable and adaptive logic gates. The inherent reconfigurability of these gates has promising security applications. The existing designs of chaos based logic gates have high overhead compared to CMOS gates. In order to make chaos based logic competitive, we need performance metrics to enable optimization of the design. In this work, we propose a metric that will enable circuit designers to optimize the logic gate design based on application specifications. We focus on a particular topology and study how different factors influence its performance and how they can be optimized to reduce overhead. However, the basic idea can be used for chaotic map circuit using other topologies as well. We also propose an enhancement technique to significantly expand the design space which is desirable for security applications.
Stochastic Fusion for Multi-stream Neural Network in Video Classification

Spatial image and optical flow provide complementary information for video representation and classification. Traditional methods separately encode two stream signals and then fuse them at the end of streams. This paper presents a new multi-stream recurrent neural network where streams are tightly coupled at each time step. Importantly, we propose a stochastic fusion mechanism for multiple streams of video data based on the Gumbel samples to increase the prediction power. A stochastic backpropagation algorithm is implemented to carry out a multi-stream neural network with stochastic fusion based on a joint optimization of convolutional encoder and recurrent decoder. Experiments on UCF101 dataset illustrate the merits of the proposed stochastic fusion in recurrent neural network in terms of interpretation and classification performance.
Complementary Neural Networks for Managing Innovation Projects

The modelling of neural networks for managing project innovation projects under uncertainty (observability, unverified information and imperfection of loss estimation methods) is considered. The principles and methods of forming innovation projects are defined. The properties of neural networks concerning the parallel processing of information, self-organization, training, generalization, etc. are determined. The general model of the neural network, its elements and training techniques are given. The concepts of neural network and complementarity classes are introduced. The concept of complementary neural network architecture is defined. Within the networks which are determined by elements of the complementary artificial neural network and a signature that forms classes of operations of the neural networks concerning innovation projects.
Analysis of Word Vector Representation Techniques with Machine-Learning Classifiers for Sentiment Analysis of Public Facebook Page’s Comments in Myanmar Text

This paper presents a study of comparison on three different machine learning techniques to sentiment analysis for Myanmar language. The fundamental part of sentiment analysis (SA) is to extract and identify the subjective information that is social sentiment in the source text. The sentiment class is positive, neutral or negative of a comment. The experiments are done on the collected 10,000 Facebook comments in Myanmar language. The objective of this study is to increase the accuracy of the sentiment identification by using the concept of word embeddings. Word2Vec is used to train for producing high-dimensional word vectors that learns the syntactic and semantic of word. The resulting word vectors train Machine Learning algorithms in the form of classifiers for sentiment identification. This experimental results prove that the use of word embeddings from the collected real world datasets improved the accuracy of sentiments classification and Logistic Regression outperformed the other two ML methods in terms of accuracy and F-measures.
Environment Estimation for Glossy Reflections in Mixed Reality Applications Using a Neural Network

Environment textures are used for the illumination of virtual objects within a virtual scene. Using these textures is crucial for high-quality lighting and reflection. In the case of an augmented reality context, the lighting is very important to seamlessly embed a virtual object within the real world scene. To ensure this, the lighting of the environment has to be captured according to the current light information. In this paper, we present a novel approach by stitching the current camera information onto a cube map. This cube map is enhanced in every single frame and is fed into a neural network to estimate missing parts. Finally, the output of the neural network and the currently stitched information is fused to make even mirror-like reflections possible on mobile devices. We provide an image stream stitching approach combined with a neural network to create plausible and high-quality environment textures that may be used for image-based lighting within mixed reality environments.
SmartFinger: A Finger-Sensing System for Mobile Interaction via MIMO FMCW Radar

In this paper, we present a finger-grained gesture recognition system that can be deployed on commodity Multiple Input Multiple Output Frequency Modulated Continuous Wave (MIMO-FMCW) radar platform as software, without any hardware modification. Firstly, we utilize the two-dimension fast Fourier transform algorithm (2D-FFT) to jointly estimate range-Doppler information. Secondly, by combining with binary phase modulation MIMO (BPM-MIMO) technique, a discrete Fourier transformation (DFT) based Multiple Signal Classification (MUSIC) algorithm is proposed to jointly measure range and angle of arrival (AOA) information without prior knowledge about the number of targets. Thirdly, a recurrent 3D convolutional neural network (R3DCNN) is employed to extract spatial-temporal fusion- features existing in range-Doppler and range-AOA map sequences. Next, we implement and evaluate this system utilizing commercial-off-the-shelf FMCW radar platform. The experimental results show that this system is able to achieve a high recognition rate of 93%.
Topography Relaxation in Determining Unsafe State Intersections for Uncertain CPS

Human-in-the-loop systems are systems that consider the presence of humans, human errors, and human interactions with a system. Accurate and validated sensing, coordinated control and decision over human errors and interactions, even at a scale and complexity, should be done reliably and seamlessly. In these systems, sensors, while being discretized and processed digitally, nevertheless represent physical properties. Therefore, modeling of these very large systems faces two key challenges: 1) While computing aspects can be represented using discrete-time events, physical parameters can only be monitored adequately using continuous-time models, and 2) with human factors involved, behavior modeling is intractable and highly inaccurate. Conventional and state-of-the-art systems always limit themselves in terms of the bounded measure calculated from the data available, without factoring in human rational information verification. In this article, uncertainty theory-based cyber-physical state-space approximation is proposed. These approximations are further morphed topographically based on Ricci flow and fuzzy qualifiers to match human verification of sensor data. The results show that sensor information can be formally approximated, verified, and identified by human belief degrees. To the best of our knowledge, this is the first implementation of Ricci flow in the domain of fuzzy cyber-physical systems, and it has the potential to improve overall reachable set interpretability with qualitative and quantitative interpretation.