Reviewerly/exHarmony huggingface dataset:
Cloud-based IoT Device Authentication Scheme using Blockchain

The number of interconnected internet of things (IoT) devices is expected to achieve a fivefold increase over the last decade. This increasing number of IoT devices brings into focus the security challenges of IoT networks. Blockchain (BC) is one of the promising technologies that can be employed for achieving this challenging task. BC is used to store data blocks in the distributed ledger (Database) that is immutable. In this paper, we aim to take the advantages of BC technology in improving the IoT network security. In particular, we propose a security framework for IoT networks to enhance the devices' authentication. The proposed architecture consists of three layers, blockchain layer, authenticator layer and requester layer. The devices' authentication process is divided into two phases: device enrollment phase, and device authentication phase. The proposed framework satisfies the three pillars of security namely, confidentiality, integrity, and availability. Confidentiality is achieved by limiting the access to authorized device only which keep the data hidden from outsiders. Integrity is guaranteed by using BC that maintains the data from being altered. Availability is assured by maintaining the BC database on the cloud.
Comparison of User Models Based on GMM-UBM and I-Vectors for Speech, Handwriting, and Gait Assessment of Parkinson’s Disease Patients

Parkinson's disease is a neurodegenerative disorder characterized by the presence of different motor impairments. Information from speech, handwriting, and gait signals have been considered to evaluate the neurological state of the patients. On the other hand, user models based on Gaussian mixture models - universal background models (GMMUBM) and i-vectors are considered the state-of-the-art in biometric applications like speaker verification because they are able to model specific speaker traits. This study introduces the use of GMM-UBM and i-vectors to evaluate the neurological state of Parkinson's patients using information from speech, handwriting, and gait. The results show the importance of different feature sets from each type of signal in the assessment of the neurological state of the patients.
Improving Language Identification for Multilingual Speakers

Spoken language identification (LID) technologies have improved in recent years from discriminating largely distinct languages to discriminating highly similar languages or even dialects of the same language. One aspect that has been mostly neglected, however, is discrimination of languages for multilingual speakers, despite being a primary target audience of many systems that utilize LID technologies. As we show in this work, LID systems can have a high average accuracy for most combinations of languages while greatly underperforming for others when accented speech is present. We address this by using coarser-grained targets for the acoustic LID model and integrating its outputs with interaction context signals in a context-aware model to tailor the system to each user. This combined system achieves an average 97% accuracy across all language combinations while improving worst-case accuracy by over 60% relative to our baseline.
Decision support model for effects estimation and proportionality assessment for targeting in cyber operations

Cyber operations are relatively a new phenomenon of the last two decades. During that period, they have increased in number, complexity, and agility, while their design and development have been processes well kept under secrecy. As a consequence, limited data(sets) regarding these incidents are available. Although various academic and practitioner public communities addressed some of the key points and dilemmas that surround cyber operations (such as attack, target identification and selection, and collateral damage), still methodologies and models are needed in order to plan, execute, and assess them in a responsibly and legally compliant way. Based on these facts, it is the aim of this article to propose a model that i)) estimates and classifies the effects of cyber operations, and ii) assesses proportionality in order to support targeting decisions in cyber operations. In order to do that, a multi-layered fuzzy model was designed and implemented by analysing real and virtual realistic cyber operations combined with interviews and focus groups with technical – military experts. The proposed model was evaluated on two cyber operations use cases in a focus group with four technical – military experts. Both the design and the results of the evaluation are revealed in this article.
Speaker-Aware Training of Attention-Based End-to-End Speech Recognition Using Neural Speaker Embeddings

In speaker-aware training, a speaker embedding is appended to DNN input features. This allows the DNN to effectively learn representations, which are robust to speaker variability.We apply speaker-aware training to attention-based end-to-end speech recognition. We show that it can improve over a purely end-to-end baseline. We also propose speaker-aware training as a viable method to leverage untranscribed, speaker annotated data.We apply state-of-the-art embedding approaches, both i-vectors and neural embeddings, such as x-vectors. We experiment with embeddings trained in two conditions: on the fixed ASR data, and on a large untranscribed dataset. We run our experiments on the TED-LIUM and Wall Street Journal datasets. No embedding consistently outperforms all others, but in many settings neural embeddings outperform i-vectors.
Recursive Prediction of Graph Signals With Incoming Nodes

Kernel and linear regression have been recently explored in the prediction of graph signals as the output, given arbitrary input signals that are agnostic to the graph. In many real-world problems, the graph expands over time as new nodes get introduced. Keeping this premise in mind, we propose a method to recursively obtain the optimal prediction or regression coefficients for the recently proposed Linear Regression over Graphs (LRG), as the graph expands with incoming nodes. This comes as a natural consequence of the structure of the regression problem, and obviates the need to solve a new regression problem each time a new node is added. Experiments with real-world graph signals show that our approach results in a good prediction performance which tends to be close to that obtained from knowing the entire graph apriori.
Systolic inversion algorithms for building cryptographic systems based on security measurement in IoT-based advanced manufacturing

Internet of Things (IoTs) have become one of the most popular techniques and widely used in advanced manufacturing and other engineering areas. As the applications of IoT-based advanced manufacturing rapidly increase, security measurement of data management has become one of the most crucial challenges in IoT-based advanced manufacturing. It is claimed that such systems are vulnerable to quantum computer attacks. Thus, it is very urgent to improve data management based on security measurement. In this paper, we improve multivariate cryptographic systems for IoT-based advanced manufacturing based on employing new inversion algorithms. Compared with related designs, the proposed design has better performance and meets the resource requirement of IoTs and the secure requirement of data management in advanced manufacturing, which is very suitable to build multivariate cryptographic systems for securing data management based on security measurement in IoT-based advanced manufacturing.
Learned Lossless Image Compression with A Hyperprior and Discretized Gaussian Mixture Likelihoods

Lossless image compression is an important task in the field of multimedia communication. Traditional image codecs typically support lossless mode, such as WebP, JPEG2000, FLIF. Recently, deep learning based approaches have started to show the potential at this point. HyperPrior is an effective technique proposed for lossy image compression. This paper generalizes the hyperprior from lossy model to lossless compression, and proposes a L2-norm term into the loss function to speed up training procedure. Besides, this paper also investigated different parameterized models for latent codes, and propose to use Gaussian mixture likelihoods to achieve adaptive and flexible context models. Experimental results validate our method can outperform existing deep learning based lossless compression, and outperform the JPEG2000 and WebP for JPG images.
Optimal linear and quadratic estimators for tracking from distance measurements

We consider the tracking problem of a point moving in a three-dimensional space using only measurements of distance from a set of reference points. The approach followed in this paper is to derive a linear map with multiplicative noise through a quadratic transformation of the distance measurements. A suitable rewriting by means of an output injection term makes the multiplicative noise of the linear map amenable to be processed by recursive estimators. These estimators are guaranteed to be internally stable and the variance of the estimation error is estimated. We compare the performance of the resulting algorithm for the linear and quadratic case with standard alternatives.
Dynamic Facial Expression Recognition Based on ResNet and LSTM

Abstract The face image data collected by ordinary face recognition system is usually static. In real life, to fully capture the state information of human emotion change, we need to collect dynamic face expression change data, which needs to capture a video sequence to reflect the process of emotion change. In this paper, we combine ResNet residual neural network and long-term memory network (LSTM) to extract features from video sequences and then recognize them. In the residual neural network, the residual element has a very good effect on the training and optimization process of deep CNN model, and shortens the convergence time of the model. Combined with the LSTM network, the captured dynamic sequence data is incorporated into the CNN model. Compared with the static single frame expression recognition system, the research of dynamic expression recognition has more application value.
Noise-Robust Key-Phrase Detectors for Automated Classroom Feedback

With the goal of giving teachers automated feedback about their classrooms, we investigate how to train automatic speech detectors of key phrases such as good job, thank you, please, and you’re welcome. This kind of language conveys support and respect from teacher to student and is one of the behavioral markers used in the established CLASS [1] classroom observation protocol. School classrooms are noisy and contain overlapping speech, presenting a highly challenging environment for automatic speech recognition (ASR), even for state-of-the-art approaches. We train deep neural networks using hierarchical multitask learning (MTL) on a modest-sized but highly-tailored dataset of classroom speech. Compared to 2 state-of-the-art ASR systems for general-purpose speech recognition (Google [2] and Deep-Speech [3]), our system delivers a substantially improved recall rate (50.4% versus 20.5%) while matching their precision (30%). Moreover, our system’s predictions correlate with several dimensions of the CLASS.
Video Question Generation via Semantic Rich Cross-Modal Self-Attention Networks Learning

We introduce a novel task, Video Question Generation (Video QG). A Video QG model automatically generates questions given a video clip and its corresponding dialogues. Video QG requires a range of skills - sentence comprehension, temporal relation, the interplay between vision and language, and the ability to ask meaningful questions. To address this, we propose a novel semantic rich cross-modal self-attention (SR-CMSA) network to aggregate the multi-modal and diverse features. To be more precise, we enhance the video frames semantic by integrating the object-level information, and we jointly consider the cross-modal attention for the video question generation task. Excitingly, our proposed model remarkably improves the baseline from 7.58 to 14.48 in the BLEU-4 score on the TVQA dataset. Most of all, we arguably pave a novel path toward understanding the challenging video input and we provide detailed analysis in terms of diversity, which ushers the avenues for future investigations.
Self-Paced Probabilistic Principal Component Analysis For Data With Outliers

Principal Component Analysis (PCA) is a popular tool for dimension reduction and feature extraction in data analysis. Probabilistic PCA (PPCA) extends the standard PCA by using a probabilistic model. However, both standard PCA and PPCA are not robust, as they are sensitive to outliers. To alleviate this problem, we propose a novel method called Self-Paced Probabilistic Principal Component Analysis (SP-PPCA) by introducing the Self-Paced Learning mechanism into PPCA. Furthermore, we design the corresponding optimization algorithm based on an alternative search strategy and an expectation-maximization algorithm, so that SP-PPCA uses an iterative procedure to find the optimal projection vectors and filter out outliers. Experiments on both synthetic data and real data demonstrate that SP-PPCA is more robust than the baselines.
A Comparison among Different Levels of Abstraction in Genetic Programming

In this paper we compare the performance of variants of Genetic Programming (GP) typically used for high dimensional machine learning problems. First we propose a taxonomy based on GP primitives that allow us to clearly differentiate GP variants found in literature; then we implement and test three GP variants in a set of image denoising tasks. Results show a clear advantage of the variant most commonly used for those kind of problems. We then compare our results with those reported in other GP works as well as those obtained by a Deep Neural Network (DNN). Comparisons suggest that GP cannot compete with deep learning unless it is embedded with expert's knowledge of the problem domain.
Saliency-Based Image Contrast Enhancement with Reversible Data Hiding

Reversible data hiding (RDH) has become a hot research area in the recent years due to its wide applications such as authentication. Among all the RDH methods proposed, contrast enhancement based reversible data hiding is one that was recently proposed. However, most of the existing schemes proposed focus on image itself without taking human visual system (HVS) into account. Consequently, the full range of the redundancy from HVS cannot be fully exploited, and the resulting image may be visually unpleasing. In this paper, a new approach is proposed by introducing the visual saliency into the process of reversible data hiding. Specifically, a saliency fixation map is generated by using an existing saliency prediction model, followed by a saliency guided segmentation to better enhance the salient regions in the input image. The evaluation results show that the proposed method yields better visual results and outperforms the existing methods in statistical metrics.
Orthogonal Training for Text-Independent Speaker Verification

In this paper we propose orthogonal training schemes to improve the effectiveness of cosine similarity measurements in text-independent speaker verification (SV) tasks. Compared to the PLDA backend, cosine similarity is simple to compute, and it does not require extra data or time to build a separate model. The use of cosine similarity measurement is also highly desirable for building end-to-end SV systems. However, the cosine similarity has an underlying assumption that the dimensions of the speaker embeddings are orthogonal, which is usually not satisfied in current SV systems. The training scheme applies singular vector decomposition (SVD) to the weight matrix of the speaker embedding extraction layer in our time delay neural network (TDNN)-based SV system, and replaces the original weight matrix by the matrix constructed from the left unitary matrix and the singular value matrix. Then the reconstructed matrix in the extraction layer is held constant and the remaining network is fine-tuned with an orthogonality regularizer. We further investigate orthogonal training from scratch, with orthogonality regularization incorporated throughout the network training. Experimental results show that our orthogonal training methods can significantly improve the system performance with a cosine similarity backend.
Development of a Vision Aided Automated Ball Retrieving Robot for Tennis Training Sessions

A single training session of tennis requires 30-40 balls and these scattered balls have to be collected at the end of each session which costs time and induces unnecessary physical stress on players. This paper proposes a method to retrieve the balls which are scattered in the Tennis court back to the user. The proposed method comprises image processing techniques for object localization and the adaptation of Dijkstra's algorithm for optimum path planning for efficient navigation. Both localization and the path planning algorithms are developed on a Python platform and robot's differential drive system is controlled by a microcontroller. Communication between robot and the computer is established through Wi-Fi. Novel development of this research is the implementation of the global view method for a vast range of image acquisition and verifying its suitability in Tennis field. In order to collect balls efficiently, an adaption of an existing mechanism is proposed. Results show that the mechanism is 80% efficient in collecting balls and 100% efficient in object identification for a given environmental condition.
Enhancement in S-Box of BRADG Algorithm

There are many ways to improve the BRADG algorithm, one of which is to detect and manipulate the weakness point to become stronger against attacks. The weakness here is S-box. In the DES algorithm, there are eight S-boxes integrated in two S-boxes, in the BRADG algorithm every four S-boxes in one S-box. The BRADG algorithm has two S-boxes and this is the main weakness point; to improve this point, we have expanded the S-box to 16 of the S-boxes, and in each cycle dealing with two S-boxes depending on address, initially, an address named “sold” is assumed to be used as the title of the first S-box. According to the enhancement algorithm, a new address is found that is the choice address of the second S-box in the first cycle. At the second cycle, the s new become is sold and repeated in each cycle. In each cycle 2 of S-box is selected depending on the title.
Study on the Effect of Emotional Speech on Language Identification

Identifying language information from speech utterance is referred to as spoken language identification. Language Identification (LID) is essential in multilingual speech systems. The performance of LID systems have been studied for various adverse conditions such as background noise, telephonic channel, short utterances, so on. In contrast to these studies, for the first time in the literature, the present work investigated the impact of emotional speech on language identification. In this work, different emotional speech databases have been pooled to create the experimental setup. Additionally, state-of-art i-vectors, time-delay neural networks, long short term memory, and deep neural network x-vector systems have been considered to build the LID systems. Performance of the LID system has been evaluated for speech utterances of different emotions in terms of equal error rate and C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">avg</sub> . The results of the study indicate that the speech utterances of anger and happy emotions degrades performance of LID systems more compared to the neutral and sad emotions.
DeepCap: A Deep Learning Model to Caption Black and White Images

Captioning of colored images has been around for quite some time now, it uses object detection and the spatial relation between the objects to generate captions. There have been numerous approaches to caption colorized images in the past, but there have been a very few. In this paper we present an approach to caption Black and white images without any attempt of colorization. We have used transfer learning to implement Inception V3, a CNN model developed by Google and a runner up in the ImageNet image classification challenge, to generate captions from Black and white images achieving an accuracy of 45.77% on the validation set.
Voice Authentication Model for One-time Password Using Deep Learning Models

This paper explores the possibility of implementing a voice authentication system consisting of speech recognition and speaker verication model for the one-time password (OTP) system. The speech recognition model is responsible for classifying user utterances of random OTP digits in Bahasa Indonesia and the speaker verification model is used to verify the identity of the speaker. The long short-term memory network and siamese network with convolutional neural networks are employed as the model, where they aim to recognize and verify human voices represented by MFCC feature vectors. From the experiments, it is found that the validation accuracy of the speech recognition model is reliable, yet the speaker verication model cannot achieve satisfactory result.
EPI-Neighborhood Distribution Based Light Field Depth Estimation

In this paper, a novel depth estimation algorithm tackling foreground occlusion is proposed based on the neighborhood distribution in the sheared epipolar images (EPIs). First, the EPI is sheared to perform refocusing. Next a series of sheared EPI's neighboring pixels in a local window are selected and the corresponding histogram distributions are analyzed by the proposed novel tensor, Kullback-Leibler Divergence (KLD). Then, depths calculated from vertical and horizontal EPIs' tensors are fused according to the tensors' variation scale for a high quality depth map. Finally, confident depth points are propagated to the whole image by global optimization. Experimental results show that the proposed algorithm achieves better performance relative to state-of-the-art algorithms.
Improving Fast Adaptation for Newcomers in Multi-Robot Reinforcement Learning System

Multi-robot system has been adopted as a kind of ubiquitous intelligent systems to perform critical tasks in various fields. In multi-robot systems, multi-agent reinforcement learning (MARL) is regarded as a promising technology to support decision-making. However, existing MARL approaches assume either a predefined system configuration or a unified model for agents with identical roles, and thus cannot effectively deal with the dynamic change in the number of robots, which is very common in the real world. This kind of "adaptation" problem seriously hinders the development of intelligence in multi-robot systems. In this paper, we propose a novel meta-MADDPG approach to enable new robots to integrate into an existing multi-robot system quickly. We build on the MADDPG (Multi-Agent Deep Deterministic Policy Gradient) algorithm and distill the meta-knowledge of a specific robot team by training a meta-actor and a meta-critic simultaneously. The meta-actor can learn an experienced policy net for new robots to perform reasonable actions directly if the situation is urgent, while the meta-critic trains a value net to criticize the current situation for better evolution of new robots. Our experiments on a typical application case (multi-robot collision avoidance) indicate that the meta-knowledge can significantly improve the fast adaptation for the newcomers. Our source code is available at https://github.com/liyiying/meta-MADDPG.