Reviewerly/exHarmony huggingface dataset:
Inferring Tasks and Fluents in Videos by Learning Causal Relations

Recognizing time-varying object states in complex tasks is an important and challenging issue. In this paper, we propose a novel model to jointly infer object fluents and complex tasks in videos. A task is a complex human activity with specific goals and a fluent is defined as a time-varying object state. A hierarchical graph represents a task as a human action stream and multiple concurrent object fluents which vary as the human performs the actions. In this process, the human actions serve as the causes of object state changes which conversely reflect the effects of human actions. For a given input video, a causal sampling search algorithm is proposed to jointly infer the task category and the states of objects in each video frame. For model learning, a structural SVM framework is adopted to jointly train the task, fluent, cause, and effect parameters. We test the proposed method on a task and fluent dataset. Experimental results demonstrate the effectiveness of the proposed method.
Show Why the Answer is Correct! Towards Explainable AI using Compositional Temporal Attention

Visual Question Answering (VQA) models have achieved significant success in recent times. Despite the success of VQA models, they are mostly black-box models providing no reasoning about the predicted answer, thus raising questions for their applicability in safety-critical such as autonomous systems and cyber-security. Current state of the art fail to better complex questions and thus are unable to exploit compositionality. To minimize the black-box effect of these models and also to make them better exploit compositionality, we propose a Dynamic Neural Network (DMN), which can understand a particular question and then dynamically assemble various relatively shallow deep learning modules from a pool of modules to form a network. We incorporate compositional temporal attention to these deep learning based modules to increase compositionality exploitation. This results in achieving better understanding of complex questions and also provides reasoning as to why the module predicts a particular answer. Experimental analysis on the two benchmark datasets, VQA2.0 and CLEVR, depicts that our model outperforms the previous approaches for Visual Question Answering task as well as provides better reasoning, thus making it reliable for mission critical applications like safety and security.
Convolutional STN for Weakly Supervised Object Localization

Weakly-supervised object localization is a challenging task in which the object of interest should be localized while learning its appearance. State-of-the-art methods recycle the architecture of a standard CNN by using the activation maps of the last layer for localizing the object. While this approach is simple and works relatively well, object localization relies on different features than classification, thus, a specialized localization mechanism is required during training to improve performance. In this paper, we propose a convolutional, multi-scale spatial localization network that provides accurate localization for the object of interest. Experimental results on CUB-200-2011 and ImageNet datasets show that our proposed approach provides competitive performance for weakly supervised localization.
Virtual Reality System for Invasive Therapy

In invasive therapies such as invasive ventilation and deep brain stimulation, doctors face the challenge of planning, performing, and learning complex surgical procedures. VR systems were built to help doctors plan surgeries. However, the previous VR designs focused on navigation and visualization but not risk estimation. In this paper, we introduced a novel VR system for invasive treatment. Our approach supports 1) 3D navigation of anatomical models by a bi-manual miniature-world design; 2) simulation of probe trajectory and calculation of the corresponding risks; 3) visualization of different layers; 4) visualization of cross-sectional cutting by a plane. These functions allow doctors to easily manipulate the anatomical model and plan probe trajectory based on the estimation of risks.
Deep Gaussian Process Models for Integrating Multifidelity Experiments with Non-stationary Relationships

The problem of integrating multifidelity data has been studied extensively, due to integrated analyses being able to provide better results than separately analyzing various data types. One popular approach is to use linear autoregressive models with location- and scale-adjustment parameters. Such parameters are typically modeled using stationary Gaussian processes. However, the stationarity assumption may not be appropriate in real-world applications. To introduce nonstationarity for enhanced flexibility, we propose a novel integration model based on deep Gaussian processes that can capture nonstationarity via successive warping of latent variables through multiple layers of Gaussian processes. For inference of the proposed model, we use a doubly stochastic variational inference algorithm. We validate the proposed model using simulated and real-data examples.
Concert and performance activity in professional training of future teacher of music art

Concert and performance activity is an important component for future music teachers. It encourages them to analyse their needs, interests, inclinations. It contributes to the formation of personal significance, emotional confidence, ensures the independence of the performer in the process of professional training. Thus, the insufficient elaboration of the problem of preparation of a future music teacher for concert and performance activity becomes an actual one in the art educational process. The article is devoted to the problem of concert and performance activity of a future music teacher in the process of professional training. It reveals the principles of forming the readiness of a future teacher of music for concert and performance practice, analyses the research on this issue. The purpose of the article is to reveal the influence of the concert and performance activity as a kind of artistic creativity on the process of professional training of a future music teacher. The scope of research on this issue includes the definition of the most effective ways and methods of preparing future music teachers for concert and performance activity, as well as numerous aspects of the creative process in which the future musician-performer is involved. The methods and techniques of diagnosing the characteristics of an individual and his performance qualities are of particular importance. This allows identifying “problem issues” that require active pedagogical influence to overcome existing shortcomings. Universal approaches (systemic, axiological, competence) must be constantly combined with individual-personal, which takes into account the features (physiological, mental, ideological, etc.) that are specific to this person and that distinguish him from everyone else. The individual-personal approach is one of the most important tasks of studying the professional and spiritual formation of the person for the future music teacher. It defines the ability to create his / her own original musical world embodying the maintenance and values of a mussician-performer as the basic criterion of the readiness for professional concert and performance activity.
Modelling web component quality using Delphi study

• Internet developers use an implicit quality model for web components. • Web components can be endowed with an explicit quality model based on ISO 25010. • The relationship between implicit / explicit models can be validated by end-users. • Quality of web components can be predicted based in explicit metrics. This article presents the application of a mixed method research in software engineering, developing an innovative quality model for web components. The research applies the qualitative analysis of open surveys in order to determine a set of internal/static and external/dynamic metrics of an empirical quality model. Subsequently, Delphi Method quantitatively analyses, by consensus amongst the members of a panel of experts, the level of coverage that these metrics offer of the product quality and quality in use characteristics included in standard ISO/IEC 25,000. Aiming a better understanding of the industry, the method applies to the software quality domain of web components developed in open repositories, where the fragmentation in the development processes has led to organic quality models of an implicit and collaborative nature. As a result of the research, a mixed methods methodology, exploratory sequential design, has been obtained that allows not only the assessment of web components hosted on public repositories, but also the systematisation of the coverage analysis amongst characteristics and metrics, essential for the development of software quality models in heterogeneous development environments.
Systemic Functional Linguistics in Translation

This article is concerned with my personal account of the process of translating into Vietnamese a world famous grammar book: An Introduction to Functional Grammar, Second Edition, written by world renowned scholar M. A. K. Halliday. The account of the translation process is placed within the compass of systemic functional linguistics. It is clear from my account that in translating An Introduction to Functional Grammar, Second Edition, the translator may experience many daunting problems, among which the problems of translating technical terms and long and heavily loaded nominal groups seem to be the toughest. It is also clear from my account that systemic functional linguistics is highly relevant to translation theory and translation practice. It can stand to benefit the translator from analysis of the source text, to discussion of translation problems, to explanation for establishment of points of equivalence between the choices in the target text and those in the source text, and to synthesis of the target text.
Space-Time Domain Tensor Neural Networks: An Application on Human Pose Classification

Recent advances in sensing technologies require the design and development of pattern recognition models capable of processing spatiotemporal data efficiently. In this study, we propose a spatially and temporally aware tensor-based neural network for human pose classification using three-dimensional skeleton data. Our model employs three novel components. First, an input layer capable of constructing highly discriminative spatiotemporal features. Second, a tensor fusion operation that produces compact yet rich representations of the data, and third, a tensor-based neural network that processes data representations in their original tensor form. Our model is end-to-end trainable and characterized by a small number of trainable parameters making it suitable for problems where the annotated data is limited. Experimental evaluation of the proposed model indicates that it can achieve state-of-the-art performance.
SlowFast Convolution LSTM Networks for Dynamic Gesture Recognition

Computer vision-based gesture recognition is gradually becoming a popular research direction in the field of human-computer interaction (HCI). However, there are various challenges in the extraction of gesture features, such as complex backgrounds, light changes and shadows. Dynamic gesture recognition aims to identify ongoing gestures from a continuous sequence of gestures, which makes it difficult to accurately extract features about continuous gestures due to not knowing the start frame and stop frame of each gesture instance. In order to overcome the various challenges in the dynamic gesture recognition task, we propose a deep architecture for the recognition of dynamic gestures by applying the SlowFast pathways and convolution LSTM to gesture recognition. End-to-end feature extraction of dynamic gestures is performed through the SlowFast pathways, avoiding the complex feature extraction process. Due to the long time span of dynamic gestures, the motion feature of gestures also play an important role in the specific connotations of gestures, hence the introduction of convolution LSTM to capture the movement information of gestures. The proposed architecture is verified on the ChaLearn LAP large-scale isolated gesture dataset (IsoGD). The results show the validity of our proposed architecture.
A deep-learning classifier for object tracking from through-the-wall radar data

In this work, a novel data driven object tracking scheme from through-the-wall radar data is presented. The localization task is firstly rewritten in terms of a classification problem which is addressed by using a deep learning approach. This localization and tracking task is assisted by standard inverse problems techniques which are used in order to recover the stationary background and to enhance the performance of the learning method. Together it yields a combined classifier network which is able to estimate very efficiently an almost arbitrary trajectory described by a target moving inside a building. No prior assumption on the dynamical model of the object is needed, which is a big advantage of the proposed scheme compared to standard Bayesian tracking filters. A 2D proof-of-concept study is discussed here which prepares more realistic 3D extensions to be addressed in our future research.
Boosting Low-Resource Intent Detection with in-Scope Prototypical Networks

Identifying intentions from users can help improve the response quality of task-oriented dialogue systems. How to use only limited labeled in-domain (ID) examples for zero-shot unknown intent detection and few-shot ID classification is a more challenging task in spoken language understanding. Existing related methods heavily rely upon the multi-domain datasets containing large-scale independent source domains for meta-training. In this paper, we propose a universal In-scope Prototypical Networks for low-resource intent detection to be general to dialogue meta-train datasets lacking widely-varying domains, which focuses on the scope of episodic intent classes to construct meta-task dynamically. Also, we introduce loss with margin principle to better distinguish samples. Experiments on two benchmark datasets show that our model consistently outperforms other baselines on zero-shot unknown intent detection without deteriorating the competitive performance on few-shot ID classification.
Integrating Feedforward Design into a Generative Network to Synthesize Supplementary Training Data for Object Classification

Training a neural network typically requires large amounts of data. Yet, gathering such amounts of data may pose a problem. This can be addressed by augmenting a data set, i. e., by adding artificial data to the original data set. One method of augmentation is generating additional images, for example with a generative adversarial network. However, this technique still requires a lot of data. This work tackles this problem by implementing the so-called feedforward design as an alternative neural network training method which supposedly requires fewer training data than regular backpropagation. We apply feedforward design to the task of image generation. The results are evaluated by augmenting original data sets to form a new training set for a neural network classifier.
Non-Parallel Many-To-Many Voice Conversion by Knowledge Transfer from a Text-To-Speech Model

In this paper, we present a simple but novel framework to train a nonparallel many-to-many voice conversion (VC) model based on the encoder-decoder architecture.It is observed that an encoder-decoder text-to-speech (TTS) model and an encoder-decoder VC model have the same structure.Thus, we propose to pre-train a multi-speaker encoder-decoder TTS model and transfer knowledge from the TTS model to a VC model by (1) adopting the TTS acoustic decoder as the VC acoustic decoder, and (2) forcing the VC speech encoder to learn the same speaker-agnostic linguistic features from the TTS text encoder so as to achieve speaker disentanglement in the VC encoder output.We further control the conversion of the pitch contour from source speech to target speech, and condition the VC decoder on the converted pitch contour during inference.Subjective evaluation shows that our proposed model is able to handle VC between any speaker pairs in the training speech corpus of over 200 speakers with high naturalness and speaker similarity.
Designing and Implementation of Mobile Application for Healthcare Monitoring

With the Corona virus, mobile applications are becoming more important especially these days because all people stay at home and cannot visit the hospital, and the clinic abroad becomes a danger. Mobile technology has the potential to impact healthcare. In the paper we use the mobile application as a platform Remote monitoring to measure various biological variables such as the ECG signal and heart rate of patients. The main contribution of this paper is to use the fuzzy algorithm to analyze patient body parameters, transmit them to a smart cell phone, and send an alarm in case of danger. This helps save heart patients before the worst cases happen. Finally, the results obtained from this project are displayed on a smartphone or PC as well. The accuracy of the results is 90% was proven by comparing the project readings with the medical devices used by doctors.
Computing Defeasible Meta-logic

The use of meta-rules, i.e., rules whose content includes other rules, has been advocated to model policies and the notion of power in legal reasoning, where an agent has the power to create new norms affecting other agents. The use of Defeasible Logic (DL) to model meta-rules in the application area we just alluded to has been investigated, but not from a computational viewpoint. Our aim is to fill this gap by introducing a variant of DL, Defeasible Meta-Logic, to represent defeasible meta-theories, by proposing efficient algorithms to compute the (meta-)extensions of such theories, and by proving their computational complexity.
Blockchain-based security &amp; privacy for biomedical and healthcare information exchange systems

Blockchain has become one of the most developing technologies in our country. Blockchain technology is a decentralized, distributed ledger that records the sources of digital assets. Electronic Medical Record (EMR) accommodates patient’s medical history, which has been carried out by a Physician or Doctor in a particular clinic or hospital or Healthcare center. Whereas, Electronic Health Record (EHR) holds patient’s entire health history, including immunization plans, diagnosis, lab reports, inoculation schedule, etc.,. This feature empowers the usage of Health IT in a more secure way with the amendment of blockchain technology. These records are maintained smart contract between the patient and the healthcare sector. In both EMR and EHR, the patient is the owner of his own data. This paper incorporates the features of blockchain technology to provide a collaborative work on Electronic Medical Records (EMRs) and Electronic Health Records (EHRs) and it concentrates highly in diabetes as it has a sharp magnification in current world. Though it is tedious to implement this EHR all over the country, this paper works for its implementation especially in the domain of diabetes.
An efficient signed digit montgomery modular multiplication algorithm

In this paper, we present a novel radix-2 Montgomery modular multi+d its corresponding architecture for high speed and low ATP implementation. The proposed multiplier, which is based on the Signed Digit Adder(SDA), requires binary input and also generates a modular product in binary form. To speed up radix-2 Montgomery modular multiplication process, we first design a new Simplified SDA(SSDA), dedicated to multipliers based on Non-Adjacent-Form(NAF) methods, which can not only avoid the carry propagation at each addition operation of add-shift loop, but also reduce the addition rounds efficiently. Second, a detecting and skipping mechanism based on the proposed SSDA has been studied to bypass some unnecessary addition rounds which would further reduce the addition rounds. In addition, our proposed Montgomery modular multiplier uses only one-level SDA architecture, and this architecture is also used to both the operand pre-computation and the final format conversion, which lead to a low hardware cost and short critical path delay. At last, we compare the area, the critical path and the cycle number of the proposed multiplier with those of other multipliers based on CSAs and SDAs. Experimental results show that the proposed Montgomery modular multiplier can achieve higher speed(6.9%) and area–time product improvement(19.4%) when compared with previous designs.
SSDL: Self-Supervised Domain Learning for Improved Face Recognition

Face recognition in unconstrained environments is challenging due to variations in illumination, quality of sensing, motion blur and etc. An individual's face appearance can vary drastically under different conditions creating a gap between train (source) and varying test (target) data. The domain gap could cause decreased performance levels in direct knowledge transfer from source to target. Despite fine-tuning with domain specific data could be an effective solution, collecting and annotating data for all domains is extremely expensive. To this end, we propose a self-supervised domain learning (SSDL) scheme that trains on triplets mined from unlabelled data. A key factor in effective discriminative learning, is selecting informative triplets. Building on most confident predictions, we follow an “easy-to-hard” scheme of alternate triplet mining and self-learning. Comprehensive experiments on four different benchmarks show that SSDL generalizes well on different domains.
Fusion Technology on the Condition of Information Deficiency Based on Improved IOWA-D-S Evidence Theory

Aiming at the problem of information fusion in deficient data condition on airborne platform, we propose a fusion technology based on improved IOWA operator and D-S evidence theory in this paper. Firstly, aiming at the incomplete information of the enemy's radiator that is likely to occur in actual situation, we propose that predicting deficient data by introducing IOWA operator and form a complete information system. Then, to fuse the information system by making use of the improved D-S evidence theory. Simulation experiment results show that the algorithm proposed in this paper is much more effective in fusing information.
Better Prior Knowledge Improves Human-Pose-Based Extrinsic Camera Calibration

Accurate extrinsic calibration of wide baseline multi-camera systems enables better understanding of 3D scenes for many applications and is of great practical importance. Classical Structure-from-Motion calibration methods require special calibration equipment so that accurate point correspondences can be detected between different views. In addition, an operator with some training is usually needed to ensure that data is collected in a way that leads to good calibration accuracy. This limits the ease of adoption of such technologies. Recently, methods have been proposed to use human pose estimation models to establish point correspondences, thus removing the need for any special equipment. The challenge with this approach is that human pose estimation algorithms typically produce much less accurate feature points compared to classical patch-based methods. Another problem is that ambient human motion might not be optimal for calibration. We build upon prior works and introduce several novel ideas to improve the accuracy of human-pose-based extrinsic calibration. Our first contribution is a robust reprojection loss based on a better understanding of the sources of pose estimation error. Our second contribution is a 3D human pose likelihood model learned from motion capture data. We demonstrate significant improvements in calibration accuracy by evaluating our method on four publicly available datasets.