Reviewerly/exHarmony huggingface dataset:
NIESR: Nuisance Invariant End-to-End Speech Recognition

Deep neural network models for speech recognition have achieved great success recently, but they can learn incorrect associations between the target and nuisance factors of speech (e.g., speaker identities, background noise, etc.), which can lead to overfitting. While several methods have been proposed to tackle this problem, existing methods incorporate additional information about nuisance factors during training to develop invariant models. However, enumeration of all possible nuisance factors in speech data and the collection of their annotations is difficult and expensive. We present a robust training scheme for end-to-end speech recognition that adopts an unsupervised adversarial invariance induction framework to separate out essential factors for speech-recognition from nuisances without using any supplementary labels besides the transcriptions. Experiments show that the speech recognition model trained with the proposed training scheme achieves relative improvements of 5.48% on WSJ0, 6.16% on CHiME3, and 6.61% on TIMIT dataset over the base model. Additionally, the proposed method achieves a relative improvement of 14.44% on the combined WSJ0+CHiME3 dataset.
Curatr: A Platform for Semantic Analysis and Curation of Historical Literary Texts

The increasing availability of digital collections of historical and contemporary literature presents a wealth of possibilities for new research in the humanities. The scale and diversity of such collections however, presents particular challenges in identifying and extracting relevant content. This paper presents Curatr, an online platform for the exploration and curation of literature with machine learning-supported semantic search, designed within the context of digital humanities scholarship. The platform provides a text mining workflow that combines neural word embeddings with expert domain knowledge to enable the generation of thematic lexicons, allowing researches to curate relevant sub-corpora from a large corpus of 18th and 19th century digitised texts.
A Lightweight Secure IoT Surveillance Framework Based on DCT-DFRT Algorithms

In this paper, we propose an energy-efficient surveillance framework for real-time video processing. The proposed framework guarantees the confidentiality of important frames and transfers them for a real-time decision. First, we extract the keyframes from the surveillance video using a lightweight summarization technique based on a fast histogram-clustering approach. Then, we employ an enhanced discrete cosine transform (DCT) compression technique to reduce the size of the extracted key frames. Finally, the cryptosystem encrypts these keyframes using a lightweight image encryption scheme based on discrete fractional random transform (DFRT) and Chen chaotic system. The proposed framework is fast and ensures real-time processing. Furthermore, this framework has the ability to reduce the transmission cost, and storage required during transmitting the video surveillance.
Application of IndRNN for human activity recognition

We propose a human activity recognition method using Independently Recurrent Neural Network (IndRNN) on the spectrum for the Sussex-Huawei Locomotion-Transportation (SHL) recognition challenge 2019. The proposed method takes advantage of FFT and IndRNN to obtain short-time and long-time features, respectively. To be specific, since the signal obtained by Smartphone-sensors is strongly periodic, it is first processed into spectrum by FFT in a one-second sliding window to obtain short-time features. Then, the spectrum of 21 overlapped window extracted from 5-second data is processed by IndRNN in order to explore the correlation of FFT spectrums of all windows to obtain longtime features for the final action classification. To obtain a phone-position independent model, the training data of three phone locations (bag, hips, torso) is used to train the IndRNN model, which is further fine-tuned with half of the validation data in hand position (a relatively small amount of data which can be obtained in practical applications). The proposed method achieved 82.6% accuracy of validation in hand position, which have been submitted to SHL recognition challenge as "UESTC_IndRNN".
Dimensionality Reduction and Subspace Clustering in Mixed Reality for Condition Monitoring of High-Dimensional Production Data

Visual analytics are becoming increasingly important in the light of big data and related scenarios. Along this trend, the field of immersive analytics has been variously furthered as it is able to provide sophisticated visual data analytics on one hand, while preserving user-friendliness on the other. Furthermore, recent hardware developments such as smart glasses, as well as achievements in virtual-reality applications, have fanned immersive analytic solutions. Notably, such solutions can be very effective when they are applied to high-dimensional datasets. Taking this advantage into account, the work at hand applies immersive analytics to a high-dimensional production dataset to improve the digital support of daily work tasks. More specifically, a mixed-reality implementation is presented that will support manufacturers as well as data scientists to comprehensively analyze machine data. As a particular goal, the prototype will simplify the analysis of manufacturing data through the usage of dimensionality reduction effects. Therefore, five aspects are mainly reported in this paper. First, it is shown how dimensionality reduction effects can be represented by clusters. Second, it is presented how the resulting information loss of the reduction is addressed. Third, the graphical interface of the developed prototype is illustrated as it provides (1) a correlation coefficient graph, (2) a plot for the information loss, and (3) a 3D particle system. In addition, an implemented voice recognition feature of the prototype is shown, which was considered to be being promising to select or deselect data variables users are interested in when analyzing the data. Fourth, based on a machine learning library, it is shown how the prototype reduces computational resources using smart glasses. The main idea is based on a recommendation approach as well as the use of subspace clustering. Fifth, results from a practical setting are presented, in which the prototype was shown to domain experts. The latter reported that such a tool is actually helpful to analyze machine data daily. Moreover, it was reported that such a system can be used to educate machine operators more properly. As a general outcome of this work, the presented approach may constitute a helpful solution for the industry as well as other domains such as medicine.
Hyperdocumentation: origin and evolution of a concept

Purpose The purpose of this paper is to provide context and raise interest for the term hyperdocumentation in the context of document theory. Design/methodology/approach The origin of the term is established through etymology and bibliographical research. The term is contextualized with conceptual research on the nature of documents and the construction of social reality through documentation. Prominent uses of the term outside of traditional research on documents is investigated. Findings The authors describe what hyperdocumentation translates to in terms of experience as of today (the different types of hyperdocumentation). Comparison to the initial definition underlines a key issue: the absence of a social contract that would define hyperdocumentality. Originality/value This paper presents novel research on the term hyperdocumentation, including its origin before hypertext, its use outside of traditional document science and its implications for document theory.
Based on DICOM RT Structure and Multiple Loss Function Deep Learning Algorithm in Organ Segmentation of Head and Neck Image

Delineating organs for a long time may cause exhaustion to radiologist’s eyes and mental health, it could lead to results that show different sizes of organs with therapeutic target volume. In this work, we expand on the idea of automatically delineating the organs in Computed Tomography (CT) images of head and neck through the generative adversarial network, which is a deep learning algorithm. In image preprocessing, we generate a bitmap (BMP) image by the combination of CT image and RT structure (RS) file and input it to generator network, which will improve the color and texture quality, last generate a fake Radiation Therapy (RT) image. Finally, the discriminator network takes the fake RT image as an example to compare with the original RT image. To build the predictive model, we continuously train this model to let it learn the rules of delineating organs in CT image, generating more and more images that are similar to the original samples. The approach that proposed in this paper is actually well applied in medicine, and the results of testing are similar to the selected organs or therapeutic targets’ volume that was delineated by the radiologist. We can see that it not only effectively reduces the false positive rate but also promises in applying to other related images.
Generating Optimal Thresholds in a Hysteresis Queue: Application to a Cloud Model

Reducing the energy consumption of a cloud system while guaranteeing a given quality of service level is a crucial problem encountered today by cloud providers. We consider an auto-scaling model where virtual machines are turned on and off depending on the queue's occupation (or thresholds). This model represents the variability of allocated resources (Virtual Machines or VMs) according to user demands. It can be studied using an hysteresis queuing model, which is represented by a multidimensional Markov chain, whose calculation of the stationary distribution becomes complex when the number of VMs grows. We adopt a cost-aware approach and define a mean cost computed as a reward function on the stationary distribution. This cost takes into account both the performance (for Service Level Agreement: SLA) and the use of the resources (for Energy). We propose efficient optimisation methods to find threshold values minimising the global cost. Because this mean cost is a non-convex function, the research of the optimal value is complex. We propose different optimisation methods: the first one, based on heuristics, coupled with aggregation of the Markov Chain to reduce the execution time and the second one which is a meta heuristic: the Simulated Annealing. Finally, we present a real case of a cloud system that we model and set parameter values to test our optimisation algorithms and show their relevance.
A Spoken Dialogue System for the EMPATHIC Virtual Coach

The EMPATHIC project is devoted to the developmentTorres, M. Inés of futureOlaso, Javier Mikel generationsGlackin, Neil of personalised virtual coaches to help elderly people to live independently. In this paper we describe a proposal to deal with the Dialogue Management of the EMPATHICJusto, Raquel VirtualChollet, Gérard Coach. The paper describes a DM system capable of dealing with both long-term goals and well-being plans, that can implement an effective motivational model. The system to be put into practice aims for high level healthy-ageing, utilising expressive multi-modal dialogue tailored for each specific user, working in tandem with short-term goal-oriented dialogue.
Understanding and predicting teachers’ intention to use cloud computing in smart education

Purpose Applying cloud computing (CC) in education is a great opportunity to globalize knowledge with the minimum costs and maximum accessibility. This study aims to understand and predict teacher intention to use cloud commuting as infrastructure in Smart education. Design/methodology/approach This was a cross-sectional study in which faculty members’ perception concerning CC services adoption in education were assessed based on the extended model of theory of planned behavior, by researcher-developed questionnaire ( a = 0.9). Collected data were analyzed by regression analysis and the final model was tested by structural equation modeling. Findings Attitude towards the behavior, perceived behavior control and privacy; had direct and significant associations with faculty members’ intention to use CC. However, subjective norms ( p = 0.311) and security ( p = 0.505 ) were not significant predictors of intention to use CC. Originality/value The results of this study elucidate the critical factors associated with teacher’s behavioral intentions toward CC services and also serve as a valuable reference for education sector to plan for the better use of these services. The presented model can be considered as best practice framework for adapting cloud commuting as infrastructure in education. Applying CC services in education is great opportunity and should be subsequently the major concern of educational organizations. This study clearly identified significant and non-significant factors that should be considered when successful implementation on could computing services is in progress.
Mysterious Murder - MCTS-driven Murder Mystery Generation

We present an approach to procedurally generate the narrative of a simple murder mystery. As a basis for the simulation, we use a rule evaluation system inspired by Ceptre, which employs linear logic to resolve valid actions during each step of the simulation. We extend Ceptre’s system with a concept of believable agents to make consecutive actions appear to have a causal connection so that players can comprehend the flow of events. The parts of the generated narratives are then presented to a player whose task it is to figure out who the murderer in this story could have been. Rather than aiming to replace highly authored narratives, this project generates puzzles, which may contain emerging arcs of a story as perceived by the player. While we found that even a simple rule set can create stories that are interesting to reason about, we expect that this type of system is flexible enough to create considerably more engaging stories if enough time is invested in authoring more complex rule sets.
ROI Segmentation and Detection of Neoplasm Based on Morphology Using Segmentation Operators

Modern medical imaging studies have a defiance problem to detect the abnormalities that lead to early diagnosis of the disease. Medical image processing deals with the detailed study of human organs and extracting the discernible information called ROI. In medical images even a minute portion of the image has a great concern in diagnosis and also has chances for wrong prophesy. Image segmentation is highly referred technique for exact separation of image for the accurate diagnosis. In this paper segmentation of brain image is implemented using morphology and segmentation operators to simplify image description, distinguishing the quality, perceptibility and cognizability of the image. The image segmentation operators like Sobel, Prewitt, Gaussian, Average, Laplacian, LoG and Unsharp are applied on DICOM brain image having tumor. The results are evaluated considering the ROI pertaining to tumor. This paper submits a comprehensive report of the techniques to perceive brain image segmentation and find out the abnormality. These results exemplify the segmented image and the best suitable operator for brain tumor segmentation and the ROI. Exact identification and separation of tumor helps for quality of appearance and classification of malignancy for the possible treatment.
Amniotic Fluid Segmentation by Pixel Classification in B-Mode Ultrasound Image for Computer Assisted Diagnosis

B-mode ultrasound imaging segmentation is facing a challenge in the artifacts such as speckle noise, blurry edges, low contrast, and unexpected shadow. This study proposed a model segmentation considering the local information from each pixel based upon its neighborhood information. The features used are a statistical texture (mean intensity, deviation standard, skewness, entropy, and property) taken based upon the 3 × 3 and 5 × 5 window. Random forest was used to classify each pixel into three regions: the amniotic fluid, uterus, and fetal body. An evaluation was carried out by calculating the comparison between the ground truth area and the segmentation results of the proposed model. The experimental results showed that the proposed model has an average accuracy of 81.45% in the 3 × 3 window and 85.86% in the 5 × 5 window on 50 tested images.
Monotonic Trends in Deep Neural Networks

The importance of domain knowledge in enhancing model performance and making reliable predictions in the real-world is unparalleled. We focus on incorporating monotonic trends (increase in input implies increase/decrease in output), and propose a novel gradient-based point-wise loss function for enforcing partial monotonicity with deep neural networks. While recent developments have focused on structural changes to the model, our approach aims at enhancing the learning process. Our point-wise loss function acts as a plug-in to the standard loss and penalizes non-monotonic gradients. We demonstrate that the point-wise loss produces comparable (and sometimes better) results with respect to both AUC and monotonicity metric, compared to state-of-the-art deep lattice networks that enforce monotonicity. Moreover, it is able to learn customized individual trends and produces smoother conditional curves - important for personalized decisions, while preserving the flexibility of deep networks.
Hybrid K-Means and Improved Self-Adaptive Particle Swarm Optimization for Data Clustering

Data Clustering has become an important mechanism for data exploration and understanding. K-Means algorithm is currently one of the most popular clustering techniques, due to its simplicity and scalability. However, K-Means performance is highly influenced by the choice of initial cluster centers, which may lead to suboptimal solutions. In this paper, a novel hybrid partitional clustering algorithm is proposed, named IDKPSOC-k-means, based on an improved self-adaptive Particle Swarm Optimization (PSO) and K-Means, which uses a crossover operator to improve PSO capability to escape from local minima points from the problem space. To evaluate the performance of the proposed approach, experiments have been performed on sixteen benchmark data sets obtained from UCI Machine Learning Repository. The experimental evaluation, conducted by the use of Friedman hypothesis tests in relation to four clustering metrics, has shown the effectivity of the proposed model in relation to the comparison algorithms.
Deep Evidential Regression

Deterministic neural networks (NNs) are increasingly being deployed in safety critical domains, where calibrated, robust, and efficient measures of uncertainty are crucial. In this paper, we propose a novel method for training non-Bayesian NNs to estimate a continuous target as well as its associated evidence in order to learn both aleatoric and epistemic uncertainty. We accomplish this by placing evidential priors over the original Gaussian likelihood function and training the NN to infer the hyperparameters of the evidential distribution. We additionally impose priors during training such that the model is regularized when its predicted evidence is not aligned with the correct output. Our method does not rely on sampling during inference or on out-of-distribution (OOD) examples for training, thus enabling efficient and scalable uncertainty learning. We demonstrate learning well-calibrated measures of uncertainty on various benchmarks, scaling to complex computer vision tasks, as well as robustness to adversarial and OOD test samples.
Ontology-driven Text Feature Modeling for Disease Prediction using Unstructured Radiological Notes

Clinical Decision Support Systems (CDSSs) support medical personnel by offering aid in decision making and timely interventions in patient care. Typically such systems are built on structured Electronic Health Records (EHRs), which, unfortunately have a very low adoption rate in developing countries at present. In such situations, clinical notes recorded by medical personnel, though unstructured, can be a significant source for rich patient related information. However, conversion of unstructured clinical notes to a structured EHR form is a manual and time consuming task, underscoring a critical need for more efficient, automated methods. In this paper, a generic disease prediction CDSS built on unstructured radiology text reports is proposed. We incorporate word embeddings and clinical ontologies to model the textual features of the patient data for training a feed-forward neural network for ICD9 disease group prediction. The proposed model built on unstructured text outperformed the state-of-the-art model built on structured data by 9% in terms of AUROC and 23% interms of AUPRC, thus eliminating the dependency on the availability of structured clinical data.
Sentence Similarity Techniques for Short vs Variable Length Text using Word Embeddings

In goal-oriented conversational agents like Chatbots, finding the similarity between user input and representative text result is a big challenge. Generally, the conversational agent developers tend to provide a minimal number of utterances per intent, which makes the classification task difficult. The problem becomes more complex when the length of the representative text per action is short and the length of the user input is long. We propose a methodology that derives Sentence Similarity score based on N-gram and Sliding Window and uses the FastText Word Embeddings technique which outperforms the current state-of-the-art Sentence Similarity results. We are also publishing a dataset on the shopping domain, to build conversational agents. And the extensive experiments done on the dataset fetched better results in accuracy, precision and recall by 6%, 2% and 80% respectively. It also evinces that our solution generalizes well on the low corpus and requires no training.
Bias Disparity in Collaborative Recommendation: Algorithmic Evaluation and Comparison

Research on fairness in machine learning has been recently extended to recommender systems. One of the factors that may impact fairness is bias disparity, the degree to which a group’s preferences on various item categories fail to be reflected in the recommendations they receive. In some cases biases in the original data may be amplified or reversed by the underlying recommendation algorithm. In this paper, we explore how different recommendation algorithms reflect the tradeoff between ranking quality and bias disparity. Our experiments include neighborhood-based, model-based, and trust-aware recommendation algorithms.
Bayesian model discrimination for partially-observed epidemic models

An efficient method for Bayesian model selection is presented for a broad class of continuous-time Markov chain models and is subsequently applied to two important problems in epidemiology. The first problem is to identify the shape of the infectious period distribution; the second problem is to determine whether individuals display symptoms before, at the same time, or after they become infectious. In both cases we show that the correct model can be identified, in the majority of cases, from symptom onset data generated from multiple outbreaks in small populations. The method works by evaluating the likelihood using a particle filter that incorporates a novel importance sampling algorithm designed for partially-observed continuous-time Markov chains. This is combined with another importance sampling method to unbiasedly estimate the model evidence. These come with estimates of precision, which allow for stopping criterion to be employed. Our method is general and can be applied to a wide range of model selection problems in biological and epidemiological systems with intractable likelihood functions.