Reviewerly/exHarmony huggingface dataset:
Exploring Transfer Learning For End-to-End Spoken Language Understanding

Voice Assistants such as Alexa, Siri, and Google Assistant typically use a two-stage Spoken Language Understanding pipeline; first, an Automatic Speech Recognition (ASR) component to process customer speech and generate text transcriptions, followed by a Natural Language Understanding (NLU) component to map transcriptions to an actionable hypothesis. An end-to-end (E2E) system that goes directly from speech to a hypothesis is a more attractive option. These systems were shown to be smaller, faster, and better optimized. However, they require massive amounts of end-to-end training data and in addition, don't take advantage of the already available ASR and NLU training data. In this work, we propose an E2E system that is designed to jointly train on multiple speech-to-text tasks, such as ASR (speech-transcription) and SLU (speech-hypothesis), and text-to-text tasks, such as NLU (text-hypothesis). We call this the Audio-Text All-Task (AT-AT) Model and we show that it beats the performance of E2E models trained on individual tasks, especially ones trained on limited data. We show this result on an internal music dataset and two public datasets, FluentSpeech and SNIPS Audio, where we achieve state-of-the-art results. Since our model can process both speech and text input sequences and learn to predict a target sequence, it also allows us to do zero-shot E2E SLU by training on only text-hypothesis data (without any speech) from a new domain. We evaluate this ability of our model on the Facebook TOP dataset and set a new benchmark for zeroshot E2E performance. We release the audio data collected for the TOP dataset for future research.
LB-DESPOT: Efficient Online POMDP Planning Considering Lower Bound in Action Selection (Student Abstract)

Partially observable Markov decision process (POMDP) is an extension to MDP. It handles the state uncertainty by specifying the probability of getting a particular observation given the current state. DESPOT is one of the most popular scalable online planning algorithms for POMDPs, which manages to significantly reduce the size of the decision tree while deriving a near-optimal policy by considering only $K$ scenarios. Nevertheless, there is a gap in action selection criteria between planning and execution in DESPOT. During the planning stage, it keeps choosing the action with the highest upper bound, whereas when the planning ends, the action with the highest lower bound is chosen for execution. Here, we propose LB-DESPOT to alleviate this issue, which utilizes the lower bound in selecting an action branch to expand. Empirically, our method has attained better performance than DESPOT and POMCP, which is another state-of-the-art, on several challenging POMDP benchmark tasks.
The Impact of Expertise on Query Formulation Strategies During Complex Learning Task Solving: A Study with Students in Medicine and Computer Science

This study focus on queries formulation strategies when expert users in a medical or computer science domain solved complex tasks. Ten medical students and ten computer science students had to perform four fact-finding search tasks (two simple tasks and two inferential tasks) and six learning tasks (two exploratory, two decision-making and two problem solving tasks) in these two domains. Results showed that non-experts used more terms from task statement to build their queries than experts did. Experts often produced new keywords than non-experts did. Specifically, computer science experts used more keywords not specific to the domain knowledge whereas medical experts used specific domain keywords to formulate queries. These results are a beginning to better understand how users are searching to learn when they are using Internet but further ergonomics studies have to more explore this subject to create search systems adapted to Search as Learning activity.
Interval Solution to Fuzzy Relation Inequality With Application in P2P Educational Information Resource Sharing Systems

Max-min fuzzy relation inequalities have recently been introduced to describe the peer-to-peer (P2P) educational information resource sharing systems.It is well known that the complete solution set of the max-min fuzzy relation system is fully determined by its minimal solutions.However, solving all the minimal solutions has been proven to be equivalent to the set-covering problem, which is NP-hard.Without solving the complete solution set, some specific solutions can be obtained through the corresponding fuzzy relation optimization problems.However, these solutions are usually unstable and fragile.Any minor fluctuations to the components of these specific solutions will cause them to be no longer feasible.In this work, we define and study the widest interval solution of a max-min fuzzy relation inequality system for the first time.The interval solution allows the solution to fluctuate within some range.The fluctuation range is determined by the width of the interval solution.We propose a novel resolution method for searching for the widest interval solution.The resolution method is carried out by detailed procedures and illustrated by a numerical example.
Hybrid lightweight Signcryption scheme for IoT

Abstract The information transmitted in IoT is susceptible to affect the user’s privacy, and hence the information ought to be transmitted securely. The conventional method to assure integrity, confidentiality, and non-repudiation is to first sign the message and then encrypt it. Signcryption is a technique where the signature and the encryption are performed in a single round. The current Signcryption system uses traditional cryptographic approaches that are overloaded for IoT, as it consists of resource-constrained devices and uses the weak session key to encrypt the data. We propose a hybrid Signcryption scheme that employs PRESENT, a lightweight block cipher algorithm to encrypt the data, and the session key is encrypted by ECC. The time taken to signcrypt the proposed Signcryption is better when compared to current Signcryption techniques, as it deploys lightweight cryptography techniques that are devoted to resource-constrained devices.
Self-correcting Q-learning

The Q-learning algorithm is known to be affected by the maximization bias, i.e. the systematic overestimation of action values, an important issue that has recently received renewed attention. Double Q-learning has been proposed as an efficient algorithm to mitigate this bias. However, this comes at the price of an underestimation of action values, in addition to increased memory requirements and a slower convergence. In this paper, we introduce a new way to address the maximization bias in the form of a "self-correcting algorithm" for approximating the maximum of an expected value. Our method balances the overestimation of the single estimator used in conventional Q-learning and the underestimation of the double estimator used in Double Q-learning. Applying this strategy to Q-learning results in Self-correcting Q-learning. We show theoretically that this new algorithm enjoys the same convergence guarantees as Q-learning while being more accurate. Empirically, it performs better than Double Q-learning in domains with rewards of high variance, and it even attains faster convergence than Q-learning in domains with rewards of zero or low variance. These advantages transfer to a Deep Q Network implementation that we call Self-correcting DQN and which outperforms regular DQN and Double DQN on several tasks in the Atari 2600 domain.
Comparison of statistical properties for various morphological filters based on mosaic image shape models

We consider the statistical properties of different mosaic filters. We demonstrate that in Pitiev's morphology, the measure of shape complexity is directly related to the shape simplicity measure based on morphological correlation coefficient (MCC). Based on MCC, we introduce the normalized morphological simplification index (NMSI). Using NMSI, we show that the simpler the mosaic shape, the more shape simplification is provided by the corresponding Pyt'ev projector. For the examples of mean and median mosaic filters, we address the problem of different operator comparison. In this context we introduce the concept of statistically simplifying morphological operators. Morphological correlation of mosaic shape and diffusion mosaic operator is considered. We prove that the NMSI for the diffusion mosaic operator is not related to the complexity for the corresponding diffusion shape kernel. Thus, a principal qualitative difference in the relationship between relational and operator models for diffuse and projective mosaic linear filters is demonstrated.
Statistical Query Algorithms and Low Degree Tests Are Almost Equivalent

Researchers currently use a number of approaches to predict and substantiate information-computation gaps in high-dimensional statistical estimation problems. A prominent approach is to characterize the limits of restricted models of computation, which on the one hand yields strong computational lower bounds for powerful classes of algorithms and on the other hand helps guide the development of efficient algorithms. In this paper, we study two of the most popular restricted computational models, the statistical query framework and low-degree polynomials, in the context of high-dimensional hypothesis testing. Our main result is that under mild conditions on the testing problem, the two classes of algorithms are essentially equivalent in power. As corollaries, we obtain new statistical query lower bounds for sparse PCA, tensor PCA and several variants of the planted clique problem.
Bhāṣācitra: Visualising the dialect geography of South Asia

We present Bhās .ācitra, 1 a dialect mapping system for South Asia built on a database of linguistic studies of languages of the region annotated for topic and location data.We analyse language coverage and look towards applications to typology by visualising example datasets.The application is not only meant to be useful for feature mapping, but also serves as a new kind of interactive bibliography for linguists of South Asian languages.
Knowledge-Guided Object Discovery with Acquired Deep Impressions

We present a framework called Acquired Deep Impressions (ADI) which continuously learns knowledge of objects as ``impressions'' for compositional scene understanding. In this framework, the model first acquires knowledge from scene images containing a single object in a supervised manner, and then continues to learn from novel multi-object scene images which may contain objects that have not been seen before without any further supervision, under the guidance of the learned knowledge as humans do. By memorizing impressions of objects into parameters of neural networks and applying the generative replay strategy, the learned knowledge can be reused to generate images with pseudo-annotations and in turn assist the learning of novel scenes. The proposed ADI framework focuses on the acquisition and utilization of knowledge, and is complementary to existing deep generative models proposed for compositional scene representation. We adapt a base model to make it fall within the ADI framework and conduct experiments on two types of datasets. Empirical results suggest that the proposed framework is able to effectively utilize the acquired impressions and improve the scene decomposition performance.
Best practices for supervised machine learning when examining biomarkers in clinical populations

Machine learning approaches are increasingly used in health research. Applications range from the identification of disease onset, classification of disease severity, to predicting epileptic seizures. Although machine learning can be a powerful tool, there is potential for misuse; model performance can be inflated through overfitting and, consequently, will not generalize to the greater population. The risk of misuse increases when the number of variables extracted from continuous data is almost unlimited, as is the case for neural, movement, and acoustic (e.g., speech and music) data. Given that health research may contain small sample sizes, and outcome variables can be noisier for clinical populations, there are important points that should be considered before using machine learning. We suggest best practices in machine learning including data formatting, reducing data dimensionality, model selection and evaluation, and other steps within the machine learning process. We further discuss some common pitfalls in applying machine learning to small sample sizes and high-dimensional data (e.g., speech biomarkers, neural and imaging data). We advocate for parsimonious approaches that include selecting the simplest machine learning method that best describes the data, preventing redundancy and overfitting through variable elimination, and ensuring that certain variables or approaches do not inflate machine learning outcomes. We further consider approaches that can identify the best predictors (or combinations thereof), as well as “black box” machine learning methods (e.g., deep learning). Finally, we discuss the limitations of current machine learning methods and pose future directions to broaden the applicability of machine learning tools and ensure the outcomes are robust against random factors.
Two-Tier LSTM Model for Image Caption Generation

Image captioning is systematically generating the caption of the image with a sentence description.In the past few years, the automatic process of creating image caption has fascinated the great interest in Artificial Intelligence (AI) field.Image captioning defines as the basic process of building the conjunction of image processing and natural language processing at input and output position.All image processing tasks, such as the segmentation of image, object tracking, object detection, image recognition, and many others, are mostly performed using Convolutional Neural Networks (CNNs).To perform the natural language processing tasks, just as semantic role labelling, neural machine translation, speech recognition, question and answering, and many others, Recurrent Neural Networks (RNNs) and long-term memory networks (LSTMs) are essential for some of the biggest breakthroughs.This paper proposes the efficient encoder-decoder framework-based image captioning model, namely Two-Tier LSTM (TT-LSTM) Model.The TT-LSTM model is designedly implemented upon the encoder-decoder framework with two LSTM layers.This research is implemented on the MSCOCO, Flickr30k, and Flickr8k datasets; and evaluated with standard evaluation matrixes such as ROUGE-L, CIDEr, and four BLEU scores.The outcomes of the experiments on the typical datasets reveal that the proposed model generates meaningful natural language sentences.The proposed model also improves the sentence generation efficiency and can achieve better performance for image caption generation.
Cross-language Sentence Selection via Data Augmentation and Rationale Training

This paper proposes an approach to crosslanguage sentence selection in a low-resource setting.It uses data augmentation and negative sampling techniques on noisy parallel sentence data to directly learn a cross-lingual embedding-based query relevance model.Results show that this approach performs as well as or better than multiple state-of-theart machine translation + monolingual retrieval systems trained on the same parallel data.Moreover, when a rationale training secondary objective is applied to encourage the model to match word alignment hints from a phrase-based statistical machine translation model, consistent improvements are seen across three language pairs (English-Somali, English-Swahili and English-Tagalog) over a variety of state-of-the-art baselines.
Methods for Improving Image Quality for Contour and Textures Analysis Using New Wavelet Methods

The fidelity of an image subjected to digital processing, such as a contour/texture highlighting process or a noise reduction algorithm, can be evaluated based on two types of criteria: objective and subjective, sometimes the two types of criteria being considered together. Subjective criteria are the best tool for evaluating an image when the image obtained at the end of the processing is interpreted by man. The objective criteria are based on the difference, pixel by pixel, between the original and the reconstructed image and ensure a good approximation of the image quality perceived by a human observer. There is also the possibility that in evaluating the fidelity of a remade (reconstructed) image, the pixel-by-pixel differences will be weighted according to the sensitivity of the human visual system. The problem of improving medical images is particularly important in assisted diagnosis, with the aim of providing physicians with information as useful as possible in diagnosing diseases. Given that this information must be available in real time, we proposed a solution for reconstructing the contours in the images that uses a modified Wiener filter in the wavelet domain and a nonlinear cellular network and that is useful both to improve the contrast of its contours and to eliminate noise. In addition to the need to improve imaging, medical applications also need these applications to run in real time, and this need has been the basis for the design of the method described below, based on the modified Wiener filter and nonlinear cellular networks.
Electronic Documents in Archives’ Information Exchange System

The functioning of various types of documents in the information space is determined by new technologies: social networks, blogs, forums, Internet exhibitions, electronic periodicals and non-periodical editions; they directly affect the formation of ethical, political, socio-cultural perceptions of modern users. There is a tendency to diversify these technologies as applicable to various types of electronic documents and for purposes of their use. Nowadays, the archives face a number of important tasks concerning study, analysis, admission for storage, and further use of documents. Among the issues requiring consideration and clarification in relation to electronic documents there is definition of the original, distinction between the original and electronic copy, the original and falsified document, definition of its material carrier, broadcasting of information, as well as some legal issues, primarily, questions of ownership, copyright, etc. The accumulation of information in the virtual sphere proceeds in various forms: there are photographs, videos from digital cameras, all kinds of electronic documents; it necessitates a clarification of some provisions concerning acquisition, storage, and usage of the documents and the information they contain. The article uses a research metaphor of “digital cultural layer” to express the changes in the archival sphere, where there emerges a new layer (complex) of electronic documents that has been created and is functioning in the virtual sphere. Electronic digital document doesn’t fit the usual paradigm, according to which a document always has a visualized material medium. The notion of the “original” becomes a subject for clarification. Acceptance of electronic documents for storage leads to adjustment of such traditional notions as “storage item” and “record-keeping item.” Identification of electronic document also requires clarification, since it does not lend itself to “identification” in the usual sense and we can only speak of conditional identification. The issues of acquisition, storage, and usage of electronic documents are considered in the article on the example of personal collections. As a significant part of citizens participate in various forms of social activity on the Internet (blogs, forums, electronic correspondence, etc.), thus expanding the social base of creators and holders of personal information, the near future will see a replenishment of personal provenance collections by electronic documents of personal origin.
Semi-Supervised Learning for Multi-Task Scene Understanding by Neural Graph Consensus

We address the challenging problem of semi-supervised learning in the context of multiple visual interpretations of the world by finding consensus in a graph of neural networks. Each graph node is a scene interpretation layer, while each edge is a deep net that transforms one layer at one node into another from a different node. During the supervised phase edge networks are trained independently. During the next unsupervised stage edge nets are trained on the pseudo-ground truth provided by consensus among multiple paths that reach the nets' start and end nodes. These paths act as ensemble for any given edge and strong consensus is used for high-confidence supervisory signal. The unsupervised learning process is repeated over several generations, in which each edge becomes a student and also part of different ensemble teachers for training other students. By optimizing such consensus between different paths, the graph reaches consistency and robustness over multiple interpretations and generations, in the face of unknown labels. We give theoretical justifications of the proposed idea and validate it on a large dataset. We show how prediction of different representations such as depth, semantic segmentation, surface normals and pose from RGB input could be effectively learned through self-supervised consensus in our graph. We also compare to state-of-the-art methods for multi-task and semi-supervised learning and show superior performance.
A New Double Rank-based Multi-workflow Scheduling with Multi-objective Optimization in Cloud Environments

Workflow scheduling in clouds has been extensively researched. Many workflows from different users could be submitted to clouds at the same time and cloud providers should handle them simultaneously. So, it is necessary to consider the problem of scheduling multi-workflow. In addition, cloud computing systems can offer some special features, like Pay-Per-Use and Quality of Service (QoS) over the Internet. The scheduler has to consider the tradeoffs between different QoS parameters in order to satisfy the QoS requirements. Hence, how to schedule multiple heterogeneous workflows in the meanwhile to balance multiple objectives is a big challenge. The majority of the existing multi-workflow scheduling algorithms are based on QoS constrained approaches and attempt to optimize one objective while taking other QoS factors as constraints. Meanwhile, most of the multi-objective optimization scheduling works aim to deal with single-workflow. Conversely, this paper focuses on QoS optimization approaches by finding trade-off schedules to execute multi-workflow on cloud computing resources so as to balance multi-objective. To this end, a new double rank-based task sequencing method is proposed and integrated with a multi-objective heuristic algorithm for multi-workflow scheduling. Different algorithms are evaluated using various well-known real-world workflows and simulated workflows. The performance evaluation results demonstrate that the proposed approach is capable of generating efficient schedules with high quality in terms of meeting multi-objective for multiple workflows.