Reviewerly/exHarmony huggingface dataset:
Incentive Mechanism Design for Federated Learning with Multi-Dimensional Private Information

As an emerging machine learning technique, federated learning has received significant attention recently due to its promising performance in mitigating privacy risks and costs. In federated learning, the model training is distributed over users and coordinated by a central server. Users only need to send the most updated learning model parameters to the server without revealing their private data. While most of the existing work of federated learning focused on designing the learning algorithm to improve the training performance, the incentive issue for encouraging users’ participation is still under-explored. Such a fundamental issue can significantly affect the training efficiency, effectiveness, and even the practical operability of federated learning. This paper presents an analytical study on the server’s optimal incentive mechanism design, in the presence of users’ multi-dimensional private information including training cost and communication delay. Specifically, we consider a multidimensional contract-theoretic approach, with a key contribution of summarizing users’ multi-dimensional private information into a one-dimensional criterion that allows a complete order of users. We further perform the analysis in three different information scenarios to reveal the impact of the level of information asymmetry on server’s optimal strategy and minimum cost. We show that weakly incomplete information does not increase the server’s cost. However, the optimal mechanism design under strongly incomplete information is much more challenging, and it is not always optimal for the server to incentivize the group of users with the lowest training cost and delay to participate.
SYNTHESIS METHODS OF OPTIMAL DISCRETE CORRECTIVE FUNCTIONS

Adv. Math. Sci. J. Vol. 9, No. 9 Open Access SYNTHESIS METHODS OF OPTIMAL DISCRETE CORRECTIVE FUNCTIONS A. V. Kabulov, E. Urunbaev, I. Normatov and A. Ashurov DOI: https://doi.org/10.37418/amsj.9.9.4 Full Text Abstract Abstract In this work, the task of constructing the optimal corrective function for the control material is solved; classes of functions … page AMSJ9-N9-4 Read More »
Acoustic Modeling Based on Deep Learning for Low-Resource Speech Recognition: An Overview

The polarization of world languages is becoming more and more obvious.Many languages, mainly endangered languages, are of low-resource attribute due to lack of information.Both language conservation and cultural heritage face important challenges.Therefore, speech recognition for low-resource scenario has become a hot topic in the field of speech.Based on the complex network structures and huge model parameters, deep learning has become a powerful science in the process of speech recognition, which has a broad and far-reaching significance for the study of low-resource speech recognition.Aiming at the characteristic of low resource, this article reviews the history and research status of two kinds of acoustic models of deep learning neural networks and acoustic end-to-end structures.We further elaborate on several key techniques for improving performance in the two aspects of data and model training.There are two projects for low-resource languages introduced in this article.The possible future developments are finally pointed out.These works provide some reference for computer speech and language processing.
Graph-based Regularization on Embedding Layers for Recommendation

Neural networks have been extensively used in recommender systems. Embedding layers are not only necessary but also crucial for neural models in recommendation as a typical discrete task. In this article, we argue that the widely used l 2 regularization for normal neural layers (e.g., fully connected layers) is not ideal for embedding layers from the perspective of regularization theory in Reproducing Kernel Hilbert Space. More specifically, the l 2 regularization corresponds to the inner product and the distance in the Euclidean space where correlations between discrete objects (e.g., items) are not well captured. Inspired by this observation, we propose a graph-based regularization approach to serve as a counterpart of the l 2 regularization for embedding layers. The proposed regularization incurs almost no extra computational overhead especially when being trained with mini-batches. We also discuss its relationships to other approaches (namely, data augmentation, graph convolution, and joint learning) theoretically. We conducted extensive experiments on five publicly available datasets from various domains with two state-of-the-art recommendation models. Results show that given a kNN (k-nearest neighbor) graph constructed directly from training data without external information, the proposed approach significantly outperforms the l 2 regularization on all the datasets and achieves more notable improvements for long-tail users and items.
Recognition of Non-Manual Content in Continuous Japanese Sign Language

The quality of recognition systems for continuous utterances in signed languages could be largely advanced within the last years. However, research efforts often do not address specific linguistic features of signed languages, as e.g., non-manual expressions. In this work, we evaluate the potential of a single video camera-based recognition system with respect to the latter. For this, we introduce a two-stage pipeline based on two-dimensional body joint positions extracted from RGB camera data. The system first separates the data flow of a signed expression into meaningful word segments on the base of a frame-wise binary Random Forest. Next, every segment is transformed into image-like shape and classified with a Convolutional Neural Network. The proposed system is then evaluated on a data set of continuous sentence expressions in Japanese Sign Language with a variation of non-manual expressions. Exploring multiple variations of data representations and network parameters, we are able to distinguish word segments of specific non-manual intonations with 86% accuracy from the underlying body joint movement data. Full sentence predictions achieve a total Word Error Rate of 15.75%. This marks an improvement of 13.22% as compared to ground truth predictions obtained from labeling insensitive towards non-manual content. Consequently, our analysis constitutes an important contribution for a better understanding of mixed manual and non-manual content in signed communication.
Prediction of incident myocardial infarction using machine learning applied to harmonized electronic health record data

Abstract Background With cardiovascular disease increasing, substantial research has focused on the development of prediction tools. We compare deep learning and machine learning models to a baseline logistic regression using only ‘known’ risk factors in predicting incident myocardial infarction (MI) from harmonized EHR data. Methods Large-scale case-control study with outcome of 6-month incident MI, conducted using the top 800, from an initial 52 k procedures, diagnoses, and medications within the UCHealth system, harmonized to the Observational Medical Outcomes Partnership common data model, performed on 2.27 million patients. We compared several over- and under- sampling techniques to address the imbalance in the dataset. We compared regularized logistics regression, random forest, boosted gradient machines, and shallow and deep neural networks. A baseline model for comparison was a logistic regression using a limited set of ‘known’ risk factors for MI. Hyper-parameters were identified using 10-fold cross-validation. Results Twenty thousand Five hundred and ninety-one patients were diagnosed with MI compared with 2.25 million who did not. A deep neural network with random undersampling provided superior classification compared with other methods. However, the benefit of the deep neural network was only moderate, showing an F1 Score of 0.092 and AUC of 0.835, compared to a logistic regression model using only ‘known’ risk factors. Calibration for all models was poor despite adequate discrimination, due to overfitting from low frequency of the event of interest. Conclusions Our study suggests that DNN may not offer substantial benefit when trained on harmonized data, compared to traditional methods using established risk factors for MI.
Dataset of Video Game Development Problems

Different from traditional software development, there is little information about the software-engineering process and techniques in video-game development. One popular way to share knowledge among the video-game developers' community is the publishing of postmortems, which are documents summarizing what happened during the video-game development project. However, these documents are written without formal structure and often providing disparate information. Through this paper, we provide developers and researchers with grounded dataset describing software-engineering problems in video-game development extracted from postmortems. We created the dataset using an iterative method through which we manually coded more than 200 postmortems spanning 20 years (1998 to 2018) and extracted 1,035 problems related to software engineering while maintaining traceability links to the postmortems. We grouped the problems in 20 different types. This dataset is useful to understand the problems faced by developers during video-game development, providing researchers and practitioners a starting point to study video-game development in the context of software engineering.
Influence of navigation parameters on cybersickness in virtual reality

Cybersickness remains a major challenge in the virtual reality community. It occurs mainly when navigating in a 3D immersive virtual environment. Several parameters are known to influence the users' cybersickness level while navigating, that can be either technological or neuro-psychological. This study investigates two of these parameters that are the distance from a virtual barrier and the choice of the navigation interface. An experiment was performed for each of these parameters to evaluate their influence on the variation of cybersickness. For each experiment, participants were asked to navigate in a large virtual room with walls that were textured with a black and white lined pattern to voluntarily exacerbate cybersickness. The level of cybersickness was collected through subjective (Simulator Sickness Questionnaire) and behavioral (evolution of postural sway) measurements. Results allow drawing suggestions for optimal navigation, so that cybersickness can be significantly reduced, thus providing with enhanced user experience.
Traffic Sign Detection Method Based on Improved SSD

Due to changes in illumination, adverse weather conditions, and interference from signs similar to real traffic signs, the false detection of traffic signs is possible. Nevertheless, in order to improve the detection effect of small targets, baseline SSD (single shot multibox detector) adopts a multi-scale feature detection method to improve the detection effect to some extent. The detection effect of small targets is improved, but the number of calculations needed for the baseline SSD network is large. To this end, we propose a lightweight SSD network algorithm. This method uses some 1 × 1 convolution kernels to replace some of the 3 × 3 convolution kernels in the baseline network and deletes some convolutional layers to reduce the calculation load of the baseline SSD network. Then the color detection algorithm based on the phase difference method and the connected component calculation are used to further filter the detection results, and finally, the data enhancement strategy based on the image appearance transformation is used to improve the balance of the dataset. The experimental results show that the proposed method is 3% more accurate than the baseline SSD network, and more importantly, the detection speed is also increased by 1.2 times.
Interactive Dual Attention Network for Text Sentiment Classification

Text sentiment classification is an essential research field of natural language processing. Recently, numerous deep learning-based methods for sentiment classification have been proposed and achieved better performances compared with conventional machine learning methods. However, most of the proposed methods ignore the interactive relationship between contextual semantics and sentimental tendency while modeling their text representation. In this paper, we propose a novel Interactive Dual Attention Network (IDAN) model that aims to interactively learn the representation between contextual semantics and sentimental tendency information. Firstly, we design an algorithm that utilizes linguistic resources to obtain sentimental tendency information from text and then extract word embeddings from the BERT (Bidirectional Encoder Representations from Transformers) pretraining model as the embedding layer of IDAN. Next, we use two Bidirectional LSTM (BiLSTM) networks to learn the long-range dependencies of contextual semantics and sentimental tendency information, respectively. Finally, two types of attention mechanisms are implemented in IDAN. One is multihead attention, which is the next layer of BiLSTM and is used to learn the interactive relationship between contextual semantics and sentimental tendency information. The other is global attention that aims to make the model focus on the important parts of the sequence and generate the final representation for classification. These two attention mechanisms enable IDAN to interactively learn the relationship between semantics and sentimental tendency information and improve the classification performance. A large number of experiments on four benchmark datasets show that our IDAN model is superior to competitive methods. Moreover, both the result analysis and the attention weight visualization further demonstrate the effectiveness of our proposed method.
Hide and Speak: Towards Deep Neural Networks for Speech Steganography

Steganography is the science of hiding a secret message within an ordinary public message, which is referred to as Carrier. Traditionally, digital signal processing techniques, such as least significant bit encoding, were used for hiding messages. In this paper, we explore the use of deep neural networks as steganographic functions for speech data. We showed that steganography models proposed for vision are less suitable for speech, and propose a new model that includes the short-time Fourier transform and inverse-short-time Fourier transform as differentiable layers within the network, thus imposing a vital constraint on the network outputs. We empirically demonstrated the effectiveness of the proposed method comparing to deep learning based on several speech datasets and analyzed the results quantitatively and qualitatively. Moreover, we showed that the proposed approach could be applied to conceal multiple messages in a single carrier using multiple decoders or a single conditional decoder. Lastly, we evaluated our model under different channel distortions. Qualitative experiments suggest that modifications to the carrier are unnoticeable by human listeners and that the decoded messages are highly intelligible.
MEGA RST Discourse Treebanks with Structure and Nuclearity from Scalable Distant Sentiment Supervision

The lack of large and diverse discourse treebanks hinders the application of data-driven approaches, such as deep-learning, to RSTstyle discourse parsing.In this work, we present a novel scalable methodology to automatically generate discourse treebanks using distant supervision from sentiment-annotated datasets, creating and publishing MEGA-DT, a new large-scale discourse-annotated corpus.Our approach generates discourse trees incorporating structure and nuclearity for documents of arbitrary length by relying on an efficient heuristic beam-search strategy, extended with a stochastic component.Experiments on multiple datasets indicate that a discourse parser trained on our MEGA-DT treebank delivers promising inter-domain performance gains when compared to parsers trained on human-annotated discourse corpora.
A Little Bit Is Worse Than None: Ranking with Limited Training Data

Researchers have proposed simple yet effective techniques for the retrieval problem based on using BERT as a relevance classifier to rerank initial candidates from keyword search.In this work, we tackle the challenge of finetuning these models for specific domains in a data and computationally efficient manner.Typically, researchers fine-tune models using corpus-specific labeled data from sources such as TREC.We first answer the question: How much data of this type do we need?Recognizing that the most computationally efficient training is no training, we explore zero-shot ranking using BERT models that have already been fine-tuned with the large MS MARCO passage retrieval dataset.We arrive at the surprising and novel finding that "some" labeled in-domain data can be worse than none at all.
Accelerated sampling Kaczmarz Motzkin algorithm for the linear feasibility problem

The Sampling Kaczmarz Motzkin (SKM) algorithm is a generalized method for solving large-scale linear systems of inequalities. Having its root in the relaxation method of Agmon, Schoenberg, and Motzkin and the randomized Kaczmarz method, SKM outperforms the state-of-the-art methods in solving large-scale Linear Feasibility (LF) problems. Motivated by SKM's success, in this work, we propose an Accelerated Sampling Kaczmarz Motzkin (ASKM) algorithm which achieves better convergence compared to the standard SKM algorithm on ill-conditioned problems. We provide a thorough convergence analysis for the proposed accelerated algorithm and validate the results with various numerical experiments. We compare the performance and effectiveness of ASKM algorithm with SKM, Interior Point Method (IPM) and Active Set Method (ASM) on randomly generated instances as well as Netlib LPs. In most of the test instances, the proposed ASKM algorithm outperforms the other state-of-the-art methods.
CMU-MOSEAS: A Multimodal Language Dataset for Spanish, Portuguese, German and French

Figure 1: Overview of in-the-wild monologue videos and sentence utterances in the CMU-MOSEAS dataset.Each sentence is annotated for 20 labels including sentiment, subjectivity, emotions and attributes."L" denotes Likert (intensity) and "B" denotes Binary for the type of the labels.The example above is a Portuguese video.
Making Information Seeking Easier: An Improved Pipeline for Conversational Search

This paper presents a highly effective pipeline for passage retrieval in a conversational search setting.The pipeline comprises of two components: Conversational Term Selection (CTS) and Multi-View Reranking (MVR).CTS is responsible for performing the first-stage of passage retrieval.Given an input question, it uses a BERT-based classifier (trained with weak supervision) to de-contextualize the input by selecting relevant terms from the dialog history.Using the question and the selected terms, it issues a query to a search engine to perform the first-stage of passage retrieval.On the other hand, MVR is responsible for contextualized passage reranking.It first constructs multiple views of the information need embedded within an input question.The views are based on the dialog history and the top documents obtained in the first-stage of retrieval.It then uses each view to rerank passages using BERT (fine-tuned for passage ranking).Finally, MVR performs a fusion over the rankings produced by the individual views.Experiments show that the above combination improves first-state retrieval as well as the overall accuracy in a reranking pipeline.On the key metric of NDCG@3, the proposed combination achieves a relative performance improvement of 14.8% over the state-of-the-art baseline and is also able to surpass the Oracle.
Domain adaptation challenges of BERT in tokenization and sub-word representations of Out-of-Vocabulary words

BERT model (Devlin et al., 2019) has achieved significant progress in several Natural Language Processing (NLP) tasks by leveraging the multi-head self-attention mechanism (Vaswani et al., 2017) in its architecture.However, it still has several research challenges which are not tackled well for domain specific corpus found in industries.In this paper, we have highlighted these problems through detailed experiments involving analysis of the attention scores and dynamic word embeddings with the BERT-Base-Uncased model.Our experiments have lead to interesting findings that showed: 1) Largest substring from the left that is found in the vocabulary (in-vocab) is always chosen at every sub-word unit that can lead to suboptimal tokenization choices, 2) Semantic meaning of a vocabulary word deteriorates when found as a substring in an Out-Of-Vocabulary (OOV) word, and 3) Minor misspellings in words are inadequately handled.We believe that if these challenges are tackled, it will significantly help the domain adaptation aspect of BERT.
Quantitative argument summarization and beyond: Cross-domain key point analysis

When summarizing a collection of views, arguments or opinions on some topic, it is often desirable not only to extract the most salient points, but also to quantify their prevalence.Work on multi-document summarization has traditionally focused on creating textual summaries, which lack this quantitative aspect.Recent work has proposed to summarize arguments by mapping them to a small set of expert-generated key points, where the salience of each key point corresponds to the number of its matching arguments.The current work advances key point analysis in two important respects: first, we develop a method for automatic extraction of key points, which enables fully automatic analysis, and is shown to achieve performance comparable to a human expert.Second, we demonstrate that the applicability of key point analysis goes well beyond argumentation data.Using models trained on publicly available argumentation datasets, we achieve promising results in two additional domains: municipal surveys and user reviews.An additional contribution is an in-depth evaluation of argument-to-key point matching models, where we substantially outperform previous results.
Image Forgery Detection and Localization via a Reliability Fusion Map

Moving away from hand-crafted feature extraction, the use of data-driven convolution neural network (CNN)-based algorithms facilitates the realization of end-to-end automated forgery detection in multimedia forensics. On the basis of fingerprints acquired by images from different camera models, the goal of this paper is to design an effective detector capable of completing image forgery detection and localization. Specifically, relying on the designed constant high-pass filter, we first establish a well-performing CNN architecture to adaptively and automatically extract characteristics, and design a reliability fusion map (RFM) to improve localization resolution, and tamper detection accuracy. The extensive results from our empirical experiments demonstrate the effectiveness of our proposed RFM-based detector, and its better performance than other competing approaches.