Reviewerly/exHarmony huggingface dataset:
VoiceMoji: A Novel On-Device Pipeline for Seamless Emoji Insertion in Dictation

Most of the speech recognition systems recover only words in the speech and fail to capture emotions. Users have to manually add emoji(s) in text for adding tone and making communication fun. Though there is much work done on punctuation addition on transcribed speech, the area of emotion addition is untouched. In this paper, we propose a novel on-device pipeline to enrich the voice input experience. It involves, given a blob of transcribed text, intelligently processing and identifying structure where emoji insertion makes sense. Moreover, it includes semantic text analysis to predict emoji for each of the sub-parts for which we propose a novel architecture Attention-based Char Aware (ACA) LSTM which handles Out-Of-Vocabulary (OOV) words as well. All these tasks are executed completely on-device and hence can aid on-device dictation systems. To the best of our knowledge, this is the first work that shows how to add emoji(s) in the transcribed text. We demonstrate that our components achieve comparable results to previous neural approaches for punctuation addition and emoji prediction with 80% fewer parameters. Overall, our proposed model has a very small memory footprint of a mere 4MB to suit on-device deployment.
Image Copy-Move Forgery Detection Using Deep Convolutional Neural Networks

In this chapter, a new architecture is introduced to address the CMFD issue using deep learning techniques. The extraction of features from an image is achieved through a series of pre-trained VGG16 [1] neural network convolution filters where a technique of back-propagation is used to learn the gradients. RPN was then used for proposing a collection of regions and an object score for each region on the generated feature map. After that, non-maximum suppression (NMS) has been used to select only those regions whose IoU is greater than 0.9 and RoI to find the area of interest in an image.
DIBR Zero-Watermarking Based on Invariant Feature and Geometric Rectification

Despite the success of watermarking technique for protecting depth image-based rendering (DIBR) 3-D videos, existing methods still can hardly ensure the robustness against geometric attacks, lossless video quality, and distinguishability between different videos simultaneously. In this article, we propose a novel zero-watermarking scheme to address this challenge. Specifically, we design CT-SVD features to ensure both distinguishability and robustness against signal processing and DIBR conversion attacks. In addition, a logistic–logistic chaotic system is utilized to encrypt features for the enhanced security. Moreover, a rectification mechanism based on salient map detection and SIFT matching is designed to resist geometric attacks. Finally, we establish an attention-based fusion mechanism to explore the complementary robustness of rectified and unrectified features. Experimental results demonstrate that our proposed method outperforms the existing schemes in terms of losslessness, distinguishability, and robustness against geometric attacks.
JamVis: exploration and visualization of traffic jams

Abstract Traffic jams are a significant problem in urban cities that cause pollution and waste fuel, money, and time. Therefore, there is an urgent need to build tools that enable authorities to monitor and understand traffic dynamics and their causes. However, exploring these large complex data presents a challenge to domain experts. This paper proposes JamVis, a web-based visual analytics framework that leverages Waze’s multi-modal spatio-temporal data to this end. JamVis comprises two main components designed based on requirements elicited from domain experts. The first one supports the exploration of Waze’s traffic jam information through multiple linked views. The second component allows identifying events through alerts reported by Waze users about different problems (e.g., potholes, floods, or heavy traffic). A new algorithm called TST-clustering is introduced to perform event detection, which is an adaptation of the DB-Scan algorithm that allows clustering alerts by space, time, and type. Furthermore, to provide an overview of this algorithm’s spatio-temporal results, we introduce a novel visualization called ST-Heatmap. JamVis is validated through three usage scenarios analyzing different events in Rio de Janeiro.
VOLERE: Leakage Resilient User Authentication Based on Personal Voice Challenges

Voiceprint Authentication as a Service (VAaS) offers great convenience due to ubiquity, generality, and usability. Despite its attractiveness, it suffers from user voiceprint leakage over the air or at the cloud, which intrudes user voice privacy and retards its wide adoption. The literature still lacks an effective solution on this issue. Traditional methods based on cryptography are too complex to be practically deployed while other approaches distort user voiceprints, which hinders accurate user identification. In this article, we propose a leakage resilient user authentication cloud service with privacy preservation based on random personal voice challenges, named VOLERE (VOice LEakage REsilient). It applies a novel voiceprint synthesis method based on a Log Magnitude Approximate (LMA) vocal tract model to fuse original voices of different speaking modes in order to generate a synthesized voiceprint for authentication. Thus, raw voiceprints of users can be well protected. We implement VOLERE and conduct a series of user tests. Experimental results show sound performance of VOLERE regarding authentication accuracy, efficiency, stability, leakage resilience and user acceptance. In particular, its authentication accuracy is reasonably stable regardless user nationality, gender, age, elapsed time, and environment, as well as variance of speaking modes.
SAFSN: A Self-Attention Based Neural Network for Encrypted Mobile Traffic Classification

Network traffic classification is the foundation of many network security services. As the widespread use of encrypted protocols makes packet payload invisible, it becomes impossible for traditional rule-based methods to classify network traffic correctly. To solve this problem, recent researches use deep learning algorithms to extract features of flows and classify the traffic data. In this paper, we propose the Self-Attention based Flow Sequence Network (SAFSN), an end-to-end encrypted traf-fic classification model. We employ both the elements&#x0027; dependence within a single flow and the dependence between flows, which can be well combined with the self-attention mechanism. We evaluate SAFSN on a real-world dataset covering 40 apps, and the results indicate that SAFSN does well in multi-classification tasks and outperforms all the other methods. Specially SAFSN can achieve an 8.83 &#x0025; higher F -score and an 8.45 &#x0025; higher accuracy than the state-of-the-art machine learning classifier.
Inshore Ship Detection in SAR Images Via an Improved SSD Model with Wavelet Decomposition

Inshore ship detection is a meaningful but difficult application for synthetic aperture radar (SAR), due to the land targets affection and the complex environment near the coast. In order to improve the capability for ship detection in complex environment, a novel method based on wavelet decomposition and improved single shot multi-box detector (SSD) is proposed. A wavelet decomposition is performed firstly to obtain both high- and low-frequency components of SAR images. Then, the two components are used to generate new images containing the texture information of SAR images. After that, new training images are fed into residual network (ResNet) that joins the squeeze-and-excitation (SE) blocks. At last, SSD model uses the feature maps output by ResNet for detection. The effectiveness of the proposed model is testified through numerical experiments, and it is shown that the proposed model has better performance in inshore ship detection using SAR images compared with the traditional SSD model.
Immerse Yourself in ASPIRE - Adding Persuasive Technology Methodology to the ASPIRE Framework

With their ability to captivate learners and simulate real world scenarios it is little wonder that healthcare education is experiencing rapid growth in the use of Immersive technologies such as Augmented Reality (AR), Virtual Reality (VR) and Extended Reality (XR). Traditional learning resources still play an important role, but as demonstrated during this current global pandemic – simulation apps can really help to bridge the training gap for nursing students, particularly in areas such as clinical skills training. To help with the implementation of persuasive technologies into development practices, the HELM team at the University of Nottingham have started the important task of analysing and adapting a set of tools which are capable of combining our renowned ASPIRE development framework with the requirements of this new production methodology. For example, we will examine some of the factors behind the introduction of a Participatory design approach for bespoke tool development and review some of its potential benefits in comparison to using our existing Agile development practices. To this end we will consider the requirements that our international project stakeholders have provided through the creation of a scientific need’s analysis specification tool prototype. A prototype which will aim to observe our ASPIRE methodology whilst working holistically with other suited applications developed in our associated EU Erasmus + funded research projects.
MetaPrep: Data preparation pipelines recommendation via meta-learning

Data preparation is a mandatory phase in the machine learning pipeline. The goal of data preparation is to convert noisy and disordered data into refined data that can be used by the algorithms. However, data preparation is time-consuming and requires specialized knowledge about the data and algorithms. Therefore, automating data preparation is essential to decrease the effort made by data scientists to develop satisfactory models. Despite its relevance, current AutoML platforms disregard or make simple hardcoded data preparation pipelines. Trying to fill this gap, we present a meta-learning-based recommendation system for data preparation. Our system recommends five pipelines, ranked by their relevance, making it useful for users with varying degrees of experience. Using the top-1 pipeline we demonstrated that our proposal allows a better performance of an AutoML system. Furthermore, the accuracy rates of our method were comparable to those achieved by a reinforcement-learning-based algorithm with the same goal, but it was up to two orders of magnitude faster. Moreover, we tested our method in a real-world application and evaluated its benefits and limitations in this scenario.
Feature-aided Multi-Target Tracking Method in Sea Clutter Using Scanning Radar Data

Maritime multi-target tracking (MTT) is an important application scenario of radar target tracking technology, but it still faces huge challenges such as the interference of sea clutter and the fluctuation of target echoes, which leading to the trajectory fragmentation. Additionally, due to radar scanning, targets are extended in azimuth and cannot be regarded as point targets. In this paper, we construct the feature vector by combining the radar echo data with the extension estimation of targets obtained by gamma Gaussian inverse Wishart-probability hypothesis density (GGIW-PHD) filter. Then, the echo features of targets are introduced into MHT framework and trajectory management module, aiming to reduce trajectory fragmentation and improve the integrity of target trajectory. Finally, the effectiveness of the proposed method is verified by measured data.
Augmented Reality in Engineering Education in Austrian Higher Vocational Education from the Students’ Perspective

Due to rapid changes in industrial production, more and more companies introduce elements of Industry 4.0. Augmented Reality (AR) as an emerging technology is a crucial component in this field. AR can be applied in different areas of the product life cycle and manufacturing processes alike. Consequently, AR-technologies and their industrial application have to be integrated into the curriculum of Higher Vocational Education because graduates are the workforce of tomorrow. In the meantime, AR-technologies have been rolled out in selected departments of Higher Vocational Colleges in Austria. Prior to this rollout, a seminar series for teachers was developed to provide in-depth knowledge and training materials. The central aim of this study is to explore the perception of the students regarding AR-education. Based on hypotheses, a questionnaire was developed to collect empirical data analyzed with statistical methods. Overall, 172 students took part in this survey. The results show that students generally like AR as part of their vocational education. In some aspects, the analysis revealed differences between students from different departments. Students are also interested in writing their diploma thesis in this field. Based on the results, a further investigation of the differences between departments is required. The insights can further be used to adapt and optimize the contents and teaching material for teachers.
Kalman filtering with finite-step autocorrelated measurement noise

This paper is concerned with the Kalman filtering problem for discrete-time linear systems corrupted by finite-step autocorrelated measurement noise which is a linear function of several mutually uncorrelated random vectors. An optimal Kalman filter is presented using state augment approach. Then, by new techniques developed in this paper, the convergence conditions of the optimal Kalman filter are established by equivalently considering the convergence of the prediction state error covariance of an augmented system where, different from the existing results, the matrix difference equation of the prediction augmented-state error covariance (PASEC) has a unique structure, that is, the matrix difference equation of the PASEC does not contain the measurement noise covariance and the process noise covariance of the augmented system in the equation is not positive definite. The main novelty of this paper is the theoretical analysis of the asymptotic convergence behavior of the PASEC whose matrix difference equation has the unique structure mentioned above. An example is presented to illustrate the effectiveness and advantages of the proposed new design strategy.
A Practical and Secure Stateless Order Preserving Encryption for Outsourced Databases

Order-preserving encryption (OPE) plays an important role in securing outsourced databases. OPE schemes can be either Stateless or Stateful. Stateful schemes can achieve the ideal security of order-preserving encryption, i.e., “reveal no information about the plaintexts besides order.” However, comparing to stateless schemes, stateful schemes require maintaining some state information locally besides encryption keys and the ciphertexts are mutable. On the other hand, stateless schemes only require remembering encryption keys and thus is more efficient. It is a common belief that stateless schemes cannot provide the same level of security as stateful ones because stateless schemes reveal the relative distance among their corresponding plaintext. In real world applications, such security defects may lead to the leakage of statistical and sensitive information, e.g., the data distribution, or even negates the whole encryption. In this paper, we propose a practical and secure stateless order-preserving encryption scheme. With prior knowledge of the data to be encrypted, our scheme can achieve IND-CCPA (INDistinguishability under Committed ordered Chosen Plaintext Attacks) security for static data set. Though the IND-CCPA security can't be met for dynamic data set, our new scheme can still significantly improve the security in real world applications. Along with the encryption scheme, in this paper we also provide methods to eliminate access pattern leakage in communications and thus prevents some common attacks to OPE schemes in practice.
A Study on Chaos Engineering for improving Cloud Software Quality and Reliability

Software availability on the cloud is an essential aspect in every domain of software development. The growing demand for zero downtime of websites is a requirement for any successful development organization. Organizations like Netflix, Amazon, Flipkart, etc., have the challenge of serving their websites to millions of users at the same time. In peak hours of sale, like in a Big Billion Day sale, the load balancing capabilities are at risk of server failure, leading to growing dissatisfaction among customers. Platform-as-a-Service (PaaS) vendors provide various solutions to ensure that websites are scalable and have a higher uptime but still require comprehensive testing by all development organizations. This paper introduces a concept called Chaos Engineering, which looks at breaking a system or website in possible ways to test each resource and tool available to the end-user. We explore various scenarios where Chaos Engineering has been used to ensure higher uptime and fulfill the requirements of cloud products. This paper also highlights the usage and advantages of using various Chaos Engineering tools available in the market.
Breaking the Curse of Class Imbalance: Bangla Text Classification

This article addresses the class imbalance issue in a low-resource language called Bengali. As a use-case, we choose one of the most fundamental NLP tasks, i.e., text classification, where we utilize three benchmark text corpora: fake-news dataset, sentiment analysis dataset, and song lyrics dataset. Each of them contains a critical class imbalance. We attempt to tackle the problem by applying several strategies that include data augmentation with synthetic samples via text and embedding generation in order to augment the proportion of the minority samples. Moreover, we apply ensembling of deep learning models by subsetting the majority samples. Additionally, we enforce the focal loss function for class-imbalanced data classification. We also apply the outlier detection technique, data resampling, and hidden feature extraction to improve the minority-f1 score. All of our experimentations are entirely focused on textual content analysis, which results in a more than 90% minority f1 score for each of the three tasks. It is an excellent outcome on such highly class-imbalanced datasets.
Audio Embeddings Help to Learn Better Dialogue Policies

Neural transformer architectures have gained a lot of interest for text-based dialogue management in the last few years. They have shown high learning capabilities for open domain dialogue with huge amounts of data and also for domain adaptation in task-oriented setups. But the potential benefits of exploiting the users' audio signal have rarely been explored in such frameworks. In this work, we combine text dialogue history representations generated by a GPT-2 model with audio embeddings obtained by the recently released Wav2Vec2 transformer model. We jointly fine-tune these models to learn dialogue policies via supervised learning and two policy gradient-based reinforcement learning algorithms. Our experimental results, using the DSTC2 dataset and a simulated user model capable of sampling audio turns, reveal that audio embeddings lead to overall higher task success (than without using audio embeddings) with statistically significant results across evaluation metrics and training algorithms.
Semantic methods of intelligent assistant developing

Human-computer interaction with people whose visual perception is limited is possible only with tactile and voice interfaces, the latter are being used more and more recently. The aim of the work is to create an intelligent assistant for an indoor navigation system designed for blind and visually impaired people. The development of an intelligent assistant is based on a semantic user model and a four-level ontological model of the subject domain. To build a dialogue between an intelligent assistant and a user, we use the theory of speech acts, argumentation theory and case-based reasoning. The developed software system is aimed at identifying the desires and user needs and proposing possible user actions aimed at achieving them. The system allows for the decomposition of user tasks and the formation of a sequence of their execution based on semantic models of the user and the subject domain.
Robust Thresholding Strategies for Highly Imbalanced and Noisy Data

Many studies have shown that non-default decision thresholds are required to maximize classification performance on highly imbalanced data sets. Thresholding strategies include using a threshold equal to the prior probability of the positive class or identifying an optimal threshold on training data. It is not clear, however, how these thresholding strategies will generalize to imbalanced data sets that contain class label noise. When class noise is present, the positive class prior is influenced by the class label noise, and a threshold that is optimized on noisy training data may not generalize to test data. We employ four thresholding strategies: two thresholds that are optimized on training data and two thresholds that depend on the positive class prior. Threshold strategies are evaluated on a range of noise levels and noise distributions using the Random Forest, Multilayer Perceptron, and XGBoost learners. While all four thresholding strategies significantly outperform the default threshold with respect to the Geometric Mean (G-Mean), three of the four thresholds yield unstable true positive rates (TPR) and true negative rates (TNR) in the presence of class noise. Results show that setting the threshold equal to the prior probability of the noisy positive class consistently performs best according to G-Mean, TPR, and TNR. This is the first evaluation of thresholding strategies for imbalanced and noisy data, to the best of our knowledge, and our results contradict related works that have suggested optimizing thresholds on training data as the best approach.
T-Counter: Trustworthy and Efficient CPU Resource Measurement Using SGX in the Cloud

As cloud services have become popular, and their adoption is growing, consumers are becoming more concerned about the cost of cloud services. Cloud Service Providers (CSPs) generally use a pay-per-use billing scheme in the cloud services model: consumers use resources as they needed and are billed for their resource usage. However, CSPs are untrusted and privileged; they have full control of the entire operating system (OS) and may tamper with bills to cheat consumers. So, how to provide a trusted solution that can keep track of and verify the consumers’ resource usage has been a challenging problem. In this article, we propose a T-Counter framework based on Intel SGX. The T-Counter allows applications to construct a trusted solution to measure its CPU usage by itself in cloud computing. These constructed applications are instrumented with counters in basic blocks and added three components in trusted parts to count instructions and defend against malicious CSPs’ manipulations. We propose two algorithms which selectively instrument counters in the CFG. T-Counter is implemented as an extension of the LLVM framework and integrated with the SGX SDK. Theoretical analyses and evaluations show that T-Counter can effectively measure CPU usage and defend against malicious CSPs’ manipulations.