Reviewerly/exHarmony huggingface dataset:
CASS-NAT: CTC Alignment-Based Single Step Non-Autoregressive Transformer for Speech Recognition

We propose a CTC alignment-based single step non-autoregressive transformer (CASS-NAT) for speech recognition. Specifically, the CTC alignment contains the information of (a) the number of tokens for decoder input, and (b) the time span of acoustics for each token. The information are used to extract acoustic representation for each token in parallel, referred to as token-level acoustic embedding which substitutes the word embedding in autoregressive transformer (AT) to achieve parallel generation in decoder. During inference, an error-based alignment sampling method is proposed to be applied to the CTC output space, reducing the WER and retaining the parallelism as well. Experimental results show that the proposed method achieves WERs of 3.8%/9.1% on Librispeech test clean/other dataset without an external LM, and a CER of 5.8% on Aishell1 Mandarin corpus, respectively <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . Compared to the AT baseline, the CASS-NAT has a performance reduction on WER, but is 51.2x faster in terms of RTF. When decoding with an oracle CTC alignment, the lower bound of WER without LM reaches 2.3% on the test-clean set, indicating the potential of the proposed method.
A new cryptographic algorithm via a two-dimensional chaotic map

In this paper, a new cryptographic algorithm using a two-dimensional piecewise smooth nonlinear chaotic map is suggested. It depends on the confusion-diffusion model (permutation-substitution model). Firstly, the plain image is shuffled using the logistic map (confusion). Secondly, chaotic sequences are produced by two-dimensional piecewise smooth nonlinear chaotic map. Then, the produced sequences are preprocessed to integers between 0 and 255. Finally, the shuffled image is masked using the chaotic sequences (diffusion). The suggested algorithm is tested by different types of images. Furthermore, the decryption algorithm is implemented to retrieve the plain (original) image using the secret key. Experimental results and security analyses are applied using the suggested algorithm and its performances are validated with recent cryptographic algorithms. The security and performance analyses conclude that the suggested algorithm is secure, fast, and resists various attacks.
An improved Gamma correction model for image dehazing in a multi-exposure fusion framework

A usual problem encountered during bad weather conditions is the degraded image quality due to haze/fog. In basic Gamma correction method there is always an uncertainty regarding the choice of a particular exponential factor, which improves the quality of the input image because of the nonlinearity involved in the process. This issue has been solved in this study by proposing a modified Gamma correction method, in which the exponential correction factor is varied incrementally to generate images. We also propose the implementation of an automatic image selection criterion for fusion which helps chose images with varied and distinct features. The implementation of the multi-exposure fusion framework is done in the hue-saturation-value color space which has close resemblance with the human vision. The intensity channel of the selected images is fused in the gradient domain which captures minute details and takes an edge as compared to other conventional fusion based methods. The fused saturation channel is obtained by averaging fusion followed by enhancement using a non-linear sigmoid function. The hue channel of the input hazy image is left unprocessed to avoid color distortion. The experimental analysis demonstrates that the proposed method outperforms most of the single image dehazing methods.
Evaluating the Interpretability of Generative Models by Interactive Reconstruction

For machine learning models to be most useful in numerous sociotechnical systems, many have argued that they must be human-interpretable. However, despite increasing interest in interpretability, there remains no firm consensus on how to measure it. This is especially true in representation learning, where interpretability research has focused on “disentanglement” measures only applicable to synthetic datasets and not grounded in human factors. We introduce a task to quantify the human-interpretability of generative model representations, where users interactively modify representations to reconstruct target instances. On synthetic datasets, we find performance on this task much more reliably differentiates entangled and disentangled models than baseline approaches. On a real dataset, we find it differentiates between representation learning methods widely believed but never shown to produce more or less interpretable models. In both cases, we ran small-scale think-aloud studies and large-scale experiments on Amazon Mechanical Turk to confirm that our qualitative and quantitative results agreed.
Sensemaking Strategies with Immersive Space to Think

The process of sensemaking involves foraging through and extracting information from large sets of documents, and it can be a cognitively intensive task. A recent approach, the Immersive Space to Think (IST), allows analysts to browse, read, mark up documents, and use immersive 3D space to organize and label collections of documents. In this study, we observed seventeen novice analysts perform a historical analysis task in order to understand how users utilize the features of IST to extract meaning from large text-based datasets. We found three different layout strategies they employed to create meaning with the documents we provided. We further found patterns of interaction and organization that can inform future improvements to the IST approach.
Performance evaluation of secured network traffic classification using a machine learning approach

Network traffic classification is a significant and problematic aspect of network resource management arising from an investigation of network developments, planning, and design for 5G and beyond. Recently, traffic investigation systems for network monitoring and user access restrictions to Virtual Private Networks (VPN) and non-Virtual Private Networks (non-VPN) have gained widespread attention. In this paper, different algorithms for classifying and detecting VPN traffic are considered. A few existing machine learning procedures were tested concerning their performance in network traffic classification and security. The purpose is to improve Precision, Recall, and F1-score in VPN Network Traffic using Ensemble Classifiers. Therefore, the parameters of the ensemble classifier were changed to obtain high Precision, Recall, and F1-score. Bagging Decision Tree and Gradient Boosting algorithms were used for classification which produced promising results when compared to single classifiers like k-Nearest Neighbors (kNN), Multilayer Perceptron (MLP), and Decision Tree. The proposed classifier demonstrates recognition accuracy on a test sample of up to 93.80% which outperforms all other single algorithms used in previous work. The MLP, Random Forest (RF), and Gradient Boosting (GB) algorithms had almost identical performance in all experiments. Furthermore, the proposed classifiers are found to perform better when the network traffic flows are generated using different values of time parameters (timeout). Our results show that the ensemble algorithms (Random Forest and the Gradient Boosting) outperform the single machine learning classifier previously used by other researchers, and we achieved the highest accuracy with the random forest classifier with better results while using non-VPN traffic and VPN traffic. The novelty lies in the application of an ensemble algorithm to secure a network traffic classification performed in comparison with single classifiers to determine Accuracy, Precision, and F1-score of a given dataset, contrary to the known process of selection of features and generation.
The impact of informality on inclusive growth in Sub-Saharan Africa: Does financial inclusion matter?

Rising poverty levels in Sub-Saharan Africa requires a better understanding of inclusive growth determinants to develop effective policy responses. Using panel data from 44 Sub-Saharan African countries for the period 1990–2018, we compute measures of inclusive growth based on gender and the rural–urban divide. We account for endogeneity, cross-sectional dependence, and heteroscedasticity, and estimate an inclusive growth model using the instrumental variable generalized method of moments (IV-GMM) estimator. The empirical evidence indicates that the impact of informality on inclusive growth depends on the measure of informality and inclusiveness. Our results show that financial inclusion exhibits an inverted-U-shaped relationship with inclusive growth. Also, we find that the moderating role of financial inclusion in the informality–inclusive growth nexus is mixed. Our results are robust to alternative model specifications and highlight the importance of financial inclusion and informality in influencing inclusive growth in Sub-Saharan Africa.
Optimized plasmonic reversible logic gate for low loss communication

With the development of plasmonic optical waveguides, numerous nanostructures based on different materials can be fabricated in a controlled way. While doing reversible computing, reversible logic gates are the necessary requirement to reduce the loss of information with less power consumption. The proposed design of the Feynman logic gate is simulated by a cascading metal-insulator-metal optical waveguide based on Mach–Zehnder interferometers. The footprint of the proposed Feynman logic gate is <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>62</mml:mn> </mml:mrow> <mml:mspace width="thickmathspace" /> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mtext>µ<!-- µ --></mml:mtext> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">m</mml:mi> </mml:mrow> </mml:mrow> <mml:mo>×<!-- × --></mml:mo> <mml:mn>9</mml:mn> <mml:mspace width="thickmathspace" /> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mtext>µ<!-- µ --></mml:mtext> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">m</mml:mi> </mml:mrow> </mml:mrow> </mml:math> , the extinction ratio is 10.57 dB, and the insertion loss is <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo>−<!-- − --></mml:mo> </mml:mrow> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>0.969</mml:mn> </mml:mrow> <mml:mspace width="thickmathspace" /> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">d</mml:mi> <mml:mi mathvariant="normal">B</mml:mi> </mml:mrow> </mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo>−<!-- − --></mml:mo> </mml:mrow> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>1.191</mml:mn> </mml:mrow> <mml:mspace width="thickmathspace" /> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">d</mml:mi> <mml:mi mathvariant="normal">B</mml:mi> </mml:mrow> </mml:math> , which is much better compared to an electro-optic-based exiting Feynman logic gate. The results are obtained by simulating the proposed structure using the finite difference time domain method and verified by using mathematical computation in MATLAB.
DeepPayload: Black-box Backdoor Attack on Deep Learning Models through Neural Payload Injection

Deep learning models are increasingly used in mobile applications as critical components. Unlike the program bytecode whose vulnerabilities and threats have been widely-discussed, whether and how the deep learning models deployed in the applications can be compromised are not well-understood since Neural Networks are usually viewed as a black box. In this paper, we introduce a highly practical backdoor attack achieved with a set of reverse-engineering techniques over compiled deep learning models. The core of the attack is a neural conditional branch constructed with a trigger detector and several operators and injected into the victim model as a malicious payload. The attack is effective as the conditional logic can be flexibly customized by the attacker, and scalable as it does not require any prior knowledge from the original model. We evaluated the attack effectiveness using 5 state-of-the-art deep learning models and real-world samples collected from 30 users. The results demonstrated that the injected backdoor can be triggered with a success rate of 93.5%, while only brought less than 2ms latency overhead and no more than 1.4% accuracy decrease. We further conducted an empirical study on real-world mobile deep learning apps collected from Google Play. We found 54 apps that were vulnerable to our attack, including popular and security-critical ones. The results call for the awareness of deep learning application developers and auditors to enhance the protection of deployed models.
Top-DB-Net: Top DropBlock for Activation Enhancement in Person Re-Identification

Person Re-Identification is a challenging task that aims to retrieve all instances of a query image across a system of non-overlapping cameras. Due to the various extreme changes of view, it is common that local regions that could be used to match people are suppressed, which leads to a scenario where approaches have to evaluate the similarity of images based on less informative regions. In this work, we introduce the Top-DB-Net, a method based on Top DropBlock that pushes the network to learn to focus on the scene foreground, with special emphasis on the most task-relevant regions and, at the same time, encodes low informative regions to provide high discriminability. The Top-DB-Net is composed of three streams: (i) a global stream encodes rich image information from a backbone, (ii) the Top DropBlock stream encourages the backbone to encode low informative regions with high discriminative features, and (iii) a regularization stream helps to deal with the noise created by the dropping process of the second stream, when testing the first two streams are used. Vast experiments on three challenging datasets show the capabilities of our approach against state-of-the-art methods. Qualitative results demonstrate that our method exhibits better activation maps focusing on reliable parts of the input images. The source code is available at: https://github.com/RQuispeC/top-dropblock.
Leveraging Text-Chart Links to Support Authoring of Data-Driven Articles with VizFlow

Data-driven articles—i.e., articles featuring text and supporting charts—play a key role in communicating information to the public. New storytelling formats like scrollytelling apply compelling dynamics to these articles to help walk readers through complex insights, but are challenging to craft. In this work, we investigate ways to support authors of data-driven articles using such storytelling forms via a text-chart linking strategy. From formative interviews with 6 authors and an assessment of 43 scrollytelling stories, we built VizFlow, a prototype system that uses text-chart links to support a range of dynamic layouts. We validate our text-chart linking approach via an authoring study with 12 participants using VizFlow, and a reading study with 24 participants comparing versions of the same article with different VizFlow intervention levels. Assessments showed our approach enabled a rapid and expressive authoring experience, and informed key design recommendations for future efforts in the space.
Improved Hybrid Bag-Boost Ensemble With K-Means-SMOTE–ENN Technique for Handling Noisy Class Imbalanced Data

Abstract A class imbalance problem plays a vital role while dealing with classes with rare number of instances. Noisy class imbalanced datasets create considerable effect on the machine learning classification of classes. Data resampling techniques commonly used for handling class imbalance problem show insignificant behavior in noisy imbalanced datasets. To cure curse of data resampling technique in noisy class imbalanced data, we have proposed improved hybrid bag-boost with proposed resampling technique model. This model contains proposed resampling technique used for handling noisy imbalanced datasets. Proposed resampling technique comprises K-Means SMOTE (Synthetic Minority Oversampling TEchnique) as an oversampling technique and edited nearest neighbor (ENN) undersampling technique used as noise removal. This resampling technique is used to mitigate noise in imbalanced datasets at three levels, i.e. first clusters datasets using K-Means clustering technique, SMOTE inside clusters for handling imbalance by inducing synthetic instances of class in minority and lastly, using ENN technique to remove instances that create noise afterwards. Experiments were performed using 11 binary imbalanced datasets by varying attribute noise percentages, and by using area under receiver operating curve as performance metrics. Experimental results confirmed that proposed model shows better results than the rest. Moreover, it is also confirmed that proposed technique performs better with an increased noise percentage in binary imbalanced datasets.
The Embodiment of Photorealistic Avatars Influences Female Body Weight Perception in Virtual Reality

Embodiment and body perception have become important research topics in the field of virtual reality (VR). VR is considered a particularly promising tool to support research and therapy in regard to distorted body weight perception. However, the influence of embodiment on body weight perception has yet to be clarified. To address this gap, we compared body weight perception of 56 female participants of normal weight using a VR application. They either (a) self-embodied a photorealistic, non-personalized virtual human and performed body movements in front of a virtual mirror or (b) only observed the virtual human as other's avatar (or agent) performing the same movements in front of them. Afterward, participants had to estimate the virtual human's body weight. Additionally, we considered the influence of the participants' body mass index (BMI) on the estimations and captured the participants' feelings of presence and embodiment. Participants estimated the body weight of the virtual human as their embodied self-avatars significantly lower compared to participants rating the virtual human as other's avatar. Furthermore, the estimations of body weight were significantly predicted by the participant's BMI with embodiment, but not without. Our results clearly highlight embodiment as an important factor influencing the perception of virtual humans' body weights in VR.
Triggered emission of indistinguishable photons from an organic dye molecule

Single molecules in solid state matrices have been proposed as sources of single photon Fock states back 20 years ago. Their success in quantum optics and in many other research fields stems from the simple recipes used in the preparation of samples, with hundreds of nominally identical and isolated molecules. Main challenges as of today for their application in photonic quantum technologies are the optimization of light extraction and the on-demand emission of indistinguishable photons. We here present Hong–Ou–Mandel (HOM) experiments with photons emitted by a single molecule of dibenzoterrylene in an anthracene nanocrystal at 3 K, under continuous wave and also pulsed excitation. A detailed theoretical model is applied, which relies on independent measurements for most experimental parameters, hence allowing for an analysis of the different contributions to the two-photon interference visibility, from residual dephasing to spectral filtering. A HOM interference visibility of more than 75% is reported, which, according to the model, is limited by the residual dephasing present at the operating temperature.
A Novel Machine Learning based Method for Deepfake Video Detection in Social Media

With the advent of deepfake videos, video forgery has become a serious threat.Videos in social media are the most common and serious targets.There are some existing works for detecting deepfake videos but very few attempts have been made for videos in social media.This paper presents a neural network based method to detect fake videos.A model, consisting of a convolutional neural network (CNN) and a classifier network is proposed.Three different structures, XceptionNet, InceptionV3 and Resnet50 have been considered as the CNN modules and a comparative study has been made.Xception Net has been chosen in the proposed model and paired with the proposed classifier for classification.We used the FaceForensics++ dataset to reach the best model.Our model integrated in the algorithm detects compressed videos in social media.
Denoispeech: Denoising Text to Speech with Frame-Level Noise Modeling

While neural-based text to speech (TTS) models can synthesize natural and intelligible voice, they usually require high-quality speech data, which is costly to collect. In many scenarios, only noisy speech of a target speaker is available, which presents challenges for TTS model training for this speaker. Previous works usually address the challenge using two methods: 1) training the TTS model using the speech denoised with an enhancement model; 2) taking a single noise embedding as input when training with noisy speech. However, they usually cannot handle speech with real-world complicated noise such as those with high variations along time. In this paper, we develop DenoiSpeech, a TTS system that can synthesize clean speech for a speaker with noisy speech data. In DenoiSpeech, we handle real-world noisy speech by modeling the fine-grained frame-level noise with a noise condition module, which is jointly trained with the TTS model. Experimental results on real-world data show that DenoiSpeech outperforms the previous two methods by 0.31 and 0.66 MOS respectively. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>