Reviewerly/exHarmony huggingface dataset:
Optimal autonomic management of service-based business processes in the cloud

Cloud computing is an emerging paradigm that provides hardware, platform and software resources as services based on pay-as-you-go model. It is being increasingly used for hosting and executing service-based business processes. However, business processes are subject to dynamic evolution during their life-cycle due to the highly dynamic evolution of the cloud environment. Therefore, to efficiently manage them according to the autonomic computing paradigm, service-based business processes can be associated with autonomic managers. Autonomic managers monitor these processes, analyze monitoring data, plan configuration actions, and execute these actions on these processes. The main objective of cloud computing is to improve the performance level while minimizing operating costs. Thus, due to the diversity of business processes requirements and the heterogeneity of cloud resources, discovering the optimal management cost of a business process in the cloud becomes a highly challenging problem. For this purpose, we propose an approach based on an Integer Linear Program to find out the optimal allocation of cloud resources that meet customers’ requirements and resources’ constraints. Besides, to validate our approach under realistic conditions and inputs, we propose an extension of the CloudSim simulator to analyze the cloud resources consumed by an autonomic business process. Experiments conducted on two real datasets highlight the effectiveness and performance of our approach.
Symbol-Level Online Channel Tracking for Deep Receivers

Deep neural networks (DNNs) allow digital receivers to operate in complex environments by learning from data corresponding to the channel input-output relationship. Since communication channels change over time, DNN-aided receivers may be required to retrain periodically, which conventionally involves excessive pilot signaling at the cost of reduced spectral efficiency. In this paper, we study how one can obtain data for retraining deep receivers without sending pilots or relying on specific protocol redundancies, by combining self-supervision with active learning concepts. We focus on the recently proposed ViterbiNet receiver, which integrates into the Viterbi algorithm a DNN for learning the channel. To enable self-supervision, we use the soft-output Viterbi algorithm to evaluate the decision confidence for each of the detected symbols in a given word. Then, to overcome learning with erroneous data, we choose a subset of the recovered symbols to be used for retraining via active learning. The proposed method selects decision-directed data whose confidence is not too low to result in inaccurate labeling, yet not too high to preserve sufficient diversity of the data. We demonstrate that self-supervised symbol-level training yields a performance within a small gap of the Viterbi algorithm with instantaneous channel knowledge.
Suppressing motion-induced phase error by using equal-step phase-shifting algorithms in fringe projection profilometry

Phase-shifting fringe projection profilometry is a widely used and important technique for three-dimensional surface measurement, where N-step fixed-step phase-shifting algorithms are commonly used. With a pressing need to apply this technique for dynamic object/scene measurement, the motion-induced error poses a challenge in achieving high measurement accuracy. A few correction methods have been developed by involving physical markers or complicated algorithms. In this paper, the equal-step phase-shifting algorithms are proposed as a simpler yet more effective solution. By approximating the phase variations as unknown but linear phase shifts, the equal-step algorithms are naturally immune to object motion. In particular, two classical algorithms, including the four-step Carré algorithm and the five-step Stoilov algorithm, are adopted. Furthermore, a novel three-step gradient-based equal-step phase-shifting (GEPS) algorithm is proposed. These equal-step algorithms are studied through comprehensive simulations and experiments, showing that, (i) the equal-step algorithms are all effective in greatly suppressing the motion-induced errors in both ideal and noisy situations; and (ii) among the three algorithms, the Stoilov algorithm is more robust to handle the object motion and the harmonics simultaneously, while the GEPS requires a least number of frames. This study will urge the use of the equal-step algorithms for phase extraction in dynamic profilometry for immediate motion-error suppression by merely implementing a single phase-calculation equation.
LeapASL: A platform for design and implementation of real time algorithms for translation of American Sign Language using personal supervised machine learning models

<h2>Abstract</h2> There is a need for a system that is easy to use, accurate, and portable to translate from American Sign Language (ASL) to English. The proposed innovative system combines Unity's usability with Python's machine learning capabilities to create a platform for real time translation of ASL. The Leap Motion Controller is used to capture hand movements and information to create supervised machine learning models. This system will be able to work in an adaptive way to learn from the signer how they sign individual words to allow for a more robust accuracy than a general model for sign language recognition.
Bayes Synthesis of Linear Nonstationary Stochastic Systems by Wavelet Canonical Expansions

This article is devoted to analysis and optimization problems of stochastic systems based on wavelet canonical expansions. Basic new results: (i) for general Bayes criteria, a method of synthesized methodological support and a software tool for nonstationary normal (Gaussian) linear observable stochastic systems by Haar wavelet canonical expansions are presented; (ii) a method of synthesis of a linear optimal observable system for criterion of the maximal probability that a signal will not exceed a particular value in absolute magnitude is given. Applications: wavelet model building of essentially nonstationary stochastic processes and parameters calibration.
Two-Path GMM-ResNet and GMM-SENet for ASV Spoofing Detection

The automatic speaker verification system is sometimes vulnerable to various spoofing attacks. The 2-class Gaussian Mixture Model classifier for genuine and spoofed speech is usually used as the baseline for spoofing detection. However, the GMM classifier does not separately consider the scores of feature frames on each Gaussian component. In addition, the GMM accumulates the scores on all frames independently, and does not consider their correlations. We propose the two-path GMM-ResNet and GMM-SENet models for spoofing detection, whose input is the Gaussian probability features based on two GMMs trained on genuine and spoofed speech respectively. The models consider not only the score distribution on GMM components, but also the relationship between adjacent frames. A two-step training scheme is applied to improve the system robustness. Experiments on the ASVspoof 2019 show that the LFCC+GMM-ResNet system can relatively reduce min-tDCF and EER by 76.1% and 76.3% on logical access scenario compared with the GMM, and the LFCC+GMM-SENet system by 94.4% and 95.4% on physical access scenario. After score fusion, the systems give the second-best results on both scenarios.
Enhancing bug localization with bug report decomposition and code hierarchical network

Bug localization, which aims to locate buggy source code files for given bug reports, is a crucial yet challenging software-mining task. Despite remarkable success, the state of the art falls short in handling (1) bug reports with diverse characteristics and (2) programs with wildly different behaviors. In response, this paper proposes a graph-based neural model BLoco for automated bug localization. To be specific, our proposed model decomposes bug reports into several bug clues to capture bug-related information from various perspectives for highly diverse bug reports. To understand the program in depth, we first design a code hierarchical network structure, Code-NoN, based on basic blocks to represent source code files. Correspondingly, a multilayer graph neural network is tailored to capture program behaviors from the Code-NoN structure of each source code file. Finally, BLoco further incorporates a bi-affine classifier to comprehensively predict the relationship between the bug reports and source files. Extensive experiments on five large-scale real-world projects demonstrate that the proposed model significantly outperforms existing techniques.
Optimizing the mitigation of epidemic spreading through targeted adoption of contact tracing apps

The ongoing COVID-19 pandemic is the first epidemic in human history in which digital contact tracing has been deployed at a global scale. Tracking and quarantining all the contacts of individuals who test positive for a virus can help slow down an epidemic, but the impact of contact tracing is severely limited by the generally low adoption of contact-tracing apps in the population. We derive here an analytical expression for the effectiveness of contact-tracing app installation strategies in a susceptible-infected-recovered (SIR) model on a given contact graph. We propose a decentralized heuristic to improve the effectiveness of contact tracing under fixed adoption rates, which targets a set of individuals to install contact-tracing apps and can be easily implemented. Simulations on a large number of real-world contact networks confirm that this heuristic represents a feasible alternative to the current state of the art.
A General Method for Deriving Tight Symbolic Bounds on Causal Effects

A causal query will commonly not be identifiable from observed data, in which case no estimator of the query can be contrived without further assumptions or measured variables, regardless of the amount or precision of the measurements of observed variables. However, it may still be possible to derive symbolic bounds on the query in terms of the distribution of observed variables. Bounds, numeric or symbolic, can often be more valuable than a statistical estimator derived under implausible assumptions. Symbolic bounds, however, provide a measure of uncertainty and information loss due to the lack of an identifiable estimand even in the absence of data. We develop and describe a general approach for computation of symbolic bounds and characterize a class of settings in which our method is guaranteed to provide tight valid bounds. This expands the known settings in which tight causal bounds are solutions to linear programs. We also prove that our method can provide valid and possibly informative symbolic bounds that are not guaranteed to be tight in a larger class of problems. We illustrate the use and interpretation of our algorithms in three examples in which we derive novel symbolic bounds. Supplementary materials for this article are available online.
Hierarchical and Multi-View Dependency Modelling Network for Conversational Emotion Recognition

This paper proposes a new model, called hierarchical and multi-view dependency modelling network (HMVDM), for the task of emotion recognition in conversations (ERC). The modelling of conversational context plays an important role in ERC, especially for the multi-turn and multi-speaker conversations which hold complex dependency between different speakers. In our proposed HMVDM <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , we model the dependency between different speakers at both tokenlevel and utterance-level. Specifically, the HMVDM model has a hierarchical structure with two main modules: 1) token-level dependency modelling module (TDM), which aims to learn the long-range token-level dependency between different utterances in a speaker-aware manner and output the utterance representation; 2) utterance-level dependency modelling module (UDM), which accepts the utterance representation from TDM as inputs and aims to learn the utterance-level dependency from intra-, inter-, and global-speaker(s) view simultaneously. Extensive experiments are conducted on four ERC benchmark datasets with state-of-the-art models employed as baselines for comparison. The empirical results demonstrate the superiority of our proposed HMVDM model and confirm the importance of hierarchical and multi-view context dependency modelling for ERC.
Class of Bell-diagonal entanglement witnesses in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msup><mml:mi mathvariant="double-struck">C</mml:mi><mml:mn>4</mml:mn></mml:msup><mml:mo>⊗</mml:mo><mml:msup><mml:mi mathvariant="double-struck">C</mml:mi><mml:mn>4</mml:mn></mml:msup></mml:mrow></mml:math> : Optimization and the spanning property

Two classes of Bell diagonal indecomposable entanglement witnesses in $\mathbb{C}^4 \otimes \mathbb{C}^4$ are considered. Within the first class, we find a generalization of the well-known Choi witness from $\mathbb{C}^3 \otimes \mathbb{C}^3$, while the second one contains the reduction map. Interestingly, contrary to $\mathbb{C}^3 \otimes \mathbb{C}^3$ case, the generalized Choi witnesses are no longer optimal. We perform an optimization procedure of finding spanning vectors, that eventually gives rise to optimal witnesses. Operators from the second class turn out to be optimal, however, without the spanning property. This analysis sheds a new light into the intricate structure of optimal entanglement witnesses.
A Robust Deep Audio Splicing Detection Method via Singularity Detection Feature

There are many methods for detecting forged audio produced by conversion and synthesis. However, as a simpler method of forgery, splicing has not attracted widespread attention. Based on the characteristic that the tampering operation will cause singularities at high-frequency components, we propose a high-frequency singularity detection feature obtained by wavelet transform. The proposed feature can explicitly show the location of the tampering operation on the waveform. Moreover, the long short-term memory (LSTM) is introduced to the CNN-architecture LCNN to ensure that the sequence information can be fully learned. The proposed feature is sent to the improved RNN-architecture LCNN together with the widely used linear frequency cepstral coefficients (LFCC) to learn forgery characteristics where the LFCC is used as a supplement. Systematic evaluation and comparison show that the proposed method has greatly improved the accuracy and generalization.
ISDA: Position-Aware Instance Segmentation with Deformable Attention

Most instance segmentation models are not end-to-end trainable due to either the incorporation of proposal estimation (RPN) as a pre-processing or non-maximum suppression (NMS) as a post-processing. Here we propose a novel end-to-end instance segmentation method termed ISDA. It reshapes the task into predicting a set of object masks, which are generated via traditional convolution operation with learned position-aware kernels and features of objects. Such kernels and features are learned by leveraging a deformable attention network with multi-scale representation. Thanks to the introduced set-prediction mechanism, the proposed method is NMS-free. Empirically, ISDA outperforms Mask R-CNN (the strong baseline) by 2.6 points on MS-COCO, and achieves leading performance compared with recent models. Code will be available soon.
Transformer-Based Streaming ASR with Cumulative Attention

In this paper, we propose an online attention mechanism, known as cumulative attention (CA), for streaming Transformer-based automatic speech recognition (ASR). Inspired by monotonic chunk-wise attention (MoChA) and head-synchronous decoder-end adaptive computation steps (HS-DACS) algorithms, CA triggers the ASR outputs based on the acoustic information accumulated at each encoding timestep, where the decisions are made using a trainable device, referred to as halting selector. In CA, all the attention heads of the same decoder layer are synchronised to have a unified halting position. This feature effectively alleviates the problem caused by the distinct behaviour of individual heads, which may otherwise give rise to severe latency issues as encountered by MoChA. The ASR experiments conducted on AIShell-1 and Librispeech datasets demonstrate that the proposed CA-based Transformer system can achieve on par or better performance with significant reduction in latency during inference, when compared to other streaming Transformer systems in literature.
Co-Attention-Guided Bilinear Model for Echo-Based Depth Estimation

Echoes reflect a geometric structure of a scene surrounding a sound source. In this paper, we address the problem of estimating depth maps of indoor scenes based on echoes. First, we experimentally show that fusing multiple acoustic features, especially spectrogram and angular spectrum, can improve estimation accuracy. We then propose a novel bilinear model that incorporates dense co-attention for effective feature fusion. Our model is able to obtain a compact fused feature while capturing the second-order correlations of intra-and inter-features. Thorough evaluations on two datasets demonstrate the superiority of the proposed method over the state-of-the-art echo-based depth estimation and feature fusion methods.
ADIMA: Abuse Detection In Multilingual Audio

Abusive content detection in spoken text can be addressed by performing Automatic Speech Recognition (ASR) and leveraging advancements in natural language processing. However, ASR models introduce latency and often perform sub-optimally for abusive words as they are underrepresented in training corpora and not spoken clearly or completely. Exploration of this problem entirely in the audio domain has largely been limited by the lack of audio datasets. Building on these challenges, we propose ADIMA, a novel, linguistically diverse, ethically sourced, expert annotated and well- balanced multilingual abuse detection audio dataset comprising of 11,775 audio samples in 10 Indic languages spanning 65 hours and spoken by 6,446 unique users. Through quantitative experiments across monolingual and cross-lingual zeroshot settings, we take the first step in democratizing audio based content moderation in Indic languages and set forth our dataset to pave future work. Dataset and code are available at: https://github.com/ShareChatAI/Adima
Comparison of Transfer Learning and Traditional Machine Learning Approach for Text Classification

In the era of big data where data consist of images, text, audio etc., manually analysing text is a big challenge. Over a long time, research in Natural Language Processing (NLP) has relied upon rule based approach and machine learning approach but these approaches need domain expert to build only task specific NLP systems. However, the recent research in NLP has given rise to pre-trained language models like ELMo, GPT, BERT, DistilBERT etc. which have made transfer learning as state-of-the-art technique in NLP. In this paper, we present the comparison of transfer learning and traditional machine learning approach for text classification. To achieve our goal, we have fine-tuned BERT and DistilBERT for text classification to compare transfer learning approach against the traditional machine learning approach based on Term Frequency-Inverse Document Frequency (TF-IDF) algorithm for feature extraction. The performance of all models is empirically tested on suite of different datasets. The results obtained by fine-tuning pre-trained language models show significant improvement over traditional machine learning approach for text classification. This research study also promotes the use of DistilBERT over BERT as default transfer learning model for various text classification applications because it can be trained in less time with less number of parameters to give similar performance like BERT.
Learning Deep Pathological Features for WSI-Level Cervical Cancer Grading

Fully automated cervical cancer grading on the level of Whole Slide Images (WSI) is a challenge task. As WSIs are in gigapixel resolution, it is impossible to train a deep classification neural network with the entire WSIs as inputs. To bypass this problem, we propose a two-stage learning framework. In detail, we propose to first learn patch-level deep pathological features for smear patches via a patch-level feature learning module, which is trained via leveraging the cell instance detection task. Then, we propose to learn WSI-level pathological features from patch-level features for cervical cancer grading. We conduct extensive experiments on our private dataset and make comparisons with rule-based cervical cancer grading methods. Experimental results demonstrate that our proposed deep feature-based WSI-level cervical cancer grading method achieves state-of-the-art performance.
Improving Biomedical Named Entity Recognition with a Unified Multi-Task MRC Framework

The prior knowledge, such as expert rules and knowledge base, has been proven effective in the traditional Biomedical Named Entity Recognition (BioNER). Most current neural BioNER systems use this external knowledge for pre-processing or post-editing instead of incorporate it into the training process, which cannot be learned by the model. To encode prior knowledge into the model, we present a unified multi-task Machine Reading Comprehension (MRC) framework for BioNER. Specifically, in the MRC task, the question sequences are derived from the standard BioNER dataset. We introduce three kinds of prior knowledge at query sequences, including Wikipedia, annotation scheme, entity dictionary. Then, our model adopts a multi-task learning strategy to joint training the main task BioNER and the auxiliary task MRC. Finally, experimental results on three benchmark datasets validate the superiority of our BioNER model compared with various state-of-the-art baselines.
Rangeinet: Fast Lidar Point Cloud Temporal Interpolation

Due to the low scan rate of LiDAR sensors, LiDAR point cloud streams usually have a low frame rate, which is far below that of other sensors such as cameras. This could incur frame rate mismatch while conducting multi-sensor data fusion. LiDAR point cloud temporal interpolation aims to synthesize the non-existing intermediate frame between input frames to improve the frame rate of point clouds. However, the existing methods heavily depend on 3D scene flow or 2D flow estimation, which yield huge computational complexity and obstacles in real-time applications. To resolve this issue, we propose a fast and non-flow involved method, which analyzes the LiDAR point cloud by exploiting its corresponding 2D range images (RIs). Specifically, we develop a Siamese context extractor containing asymmetrical convolution kernels to learn the shape context and spatial feature of RIs, and the 3D space-time convolutions are introduced to precisely capture the temporal characteristics. Experimental results have clearly shown that our method is much faster than the state-of-the-art LiDAR point cloud temporal interpolation methods on various datasets, while delivering either comparable or superior frame interpolation performance.