Reviewerly/exHarmony huggingface dataset:
e-Health Education Using Automatic Question Generation-Based Natural Language (Case Study: Respiratory Tract Infection)

In the midst of the outbreak, the public is flooded with information that is not necessarily true where hoax messages and fear spread faster than valid information and positive messages. For this reason, it is necessary to have consultation facilities that are accurate, fast and on target. This research creates an educational e-health in the form of a health question and answer system (with an upper respiratory tract infection case study) which can be used to provide answers that are more focused on valid information. Validation of information with answers searched using dynamic neural networks. Documents containing information are extracted to detect the correctness, and document keywords are associated with inquiries from users. The process of clarifying information uses automation of dynamic neural networks which allows the answers given to users to be more focused. The final result of this research is a virtual assistant that provides a corpus in the form of a QA-pair that can be generated automatically to provide accurate information to users working with upper respiratory tract infection with an accuracy of 71.6%.
Multi-dimensional Affect in Poetry (POCA) Dataset: Acquisition, Annotation and Baseline Results

Detecting emotions and affect in text has received enormous attention in recent years, and yet majority of the works in this area reduce the nuanced emotional responses into ‘positive’, ‘negative’ and ‘neutral’. In this paper, we introduce a novel multi-dimensional affect in poetry (POCA) dataset for sentiment analysis annotated using the Geneva Emotion Wheel (GEW), to capture and analyse the multi-dimensional affect evoked in listeners. The POCA dataset is based on poems and their corresponding recitals from an online poetry database where recitals are curated by the website, and performed by the poet or an approved artist. The POCA dataset contains 330 poems (text and audio), from the English language, each of which is annotated across 20 different emotion classes, by 5 listeners. A subset of the dataset (50 poems) have also been annotated by an inlab study by 3 listeners each while their Electrodermal activity (EDA) was being recorded. As a proof of concept, we (i) introduce representative problem formulations to be addressed by machine learning approaches using the POCA dataset, from single emotion recognition (e.g., does this poem evoke joy?) to continuous affect prediction (e.g., what level arousal and valence does this poem evoke?), (ii) provide baseline results for text-based affect recognition using several classification and regression models, and (iii) provide baseline results for EDA-based affect prediction. Our results show that (i) for text-based affect recognition, classical approaches can provide as accurate results as their fine-tuned neural network counterparts, and (ii) in EDA-based affect prediction, in general there is a strong relation between the EDA signals and the self-reported valence and arousal quadrants, while predictions are better for arousal than valence.
Design and Scaffolded Training of an Efficient DNN Operator for Computer Vision on the Edge

Massively parallel systolic arrays and resource-efficient depthwise separable convolutions are two promising hardware and software techniques to accelerate DNN inference on the edge. Interestingly, their combination is inefficient: Computational patterns of depthwise separable convolutions do not exhibit a rhythmic systolic flow and lack sufficient data reuse to saturate systolic arrays. In this article, we formally analyse this inefficiency and propose an efficient operator, an optimal hardware dataflow, and a superior training methodology towards alleviating this. The efficient operator, called Fully-Separable Convolutions (FuSeConv) , 1 is a drop-in replacement for depthwise-separable convolutions. FuSeConv generalizes factorization of convolution fully along their spatial and depth dimensions. The resultant computation is systolic and efficiently maps to systolic arrays. The optimal hardware dataflow, called Spatial-Tiled Output Stationary (ST-OS) , maximizes the efficiency of FuSeConv on systolic arrays. It maps independent convolutions to rows of the systolic array to maximise resource-utilization with negligible VLSI overheads. Neural Operator Scaffolding (NOS) scaffolds the training of FuSeConv operators by distilling knowledge from the more expensive depthwise separable convolution operation. This bridges the accuracy gap between FuSeConv networks and networks with depthwise-separable convolutions. Additionally, NOS can be combined with Neural Architecture Search (NAS) to trade off latency and accuracy. The hardware-software co-design of FuSeConv with ST-OS achieves a significant speedup of 4.1-9.25× with state-of-the-art efficient networks for the ImageNet dataset. The parameter efficiency of FuSeConv and its significant superiority over depthwise-separable convolutions on systolic arrays illustrates their promise as a strong solution on the edge. Training FuSeConv networks with NOS achieves accuracy comparable to the depthwise-separable convolution baselines. Further, by combining NOS with NAS, we design networks that define state-of-the-art models improving on both accuracy and latency for computer vision on systolic arrays.
Designing a Tangible Device for Re-Framing Unproductivity

We report on the design of a tangible device for encouraging the acceptance of unproductive time on office workers. We first conducted interviews for a better understanding of the subjective experience of productivity. We found that while the idea of being productive can evoke positive feelings of satisfaction, dealing with unproductive time can be a struggle, negatively affecting people’s moods and self-esteem. These findings guided the design and implementation of RU, a tangible device for reflecting on self-care time. Our prototype offers a physical representation of the mainstream productivity mindset and plays with the idea of connecting and charging energy to encourage the user to experience the time considered unproductive as self-care. In a second study, participants used the device for 5 days and our results suggest that the device motivates reflection on activities beyond work and increases awareness of the importance of taking time for self-care.
Diagnosis of pedagogical and digital strategies used by the teachers in Financial Accounting II subject. Case: Accounting and Auditoring Career

The precise teaching of means that complement the learning. For that reason, the strategies applied in the classroom must be innovative instruments that are proposed based on the fulfillment of the educational objectives. The students of the 21st century are used to using of Information and Communication Technologies (ICT), so using these tools in the educational work which represents a fundamental opportunity to strengthen scientific knowledge supported by the use of technological instruments. With the aim of identifying the pedagogical and digital strategies used by the Accounting teachers of the Accounting and Auditing Program of Técnica del Norte University, located in the Ibarra city, Ecuador. Through a descriptive quantitative approach, it was shown that the use of pedagogical strategies present serious deficiencies in use and application, as well as digital strategies because they are technological means that require to investment time and knowledge. The statistical technique of the Spearman’s correlation coefficient (Rho) was carried out in Minitab whose results show the validity and reliability of the need to implement pedagogical and digital strategies in the educational process by means of technological innovation in the teaching-learning process, it is concluded that teachers must renew their knowledge about the elaboration of digital educational resources in order to create innovation in the development of their classes and therefore improve the learning of the main actors of education.
Feature Map-Aware Activation Quantization for Low-bit Neural Networks

Quantization, the most popular deep neural network (DNN) compression method, can reduce the computational complexity and save a lot of memory resources by converting the existing 32-bit floating point values to low-bit integer point values. However, as DNNs are widely used in mobile and edge devices, which have relatively less hardware resources, there are demands for more aggressive quantization methods. To meet these needs, this paper introduces a dedicated method that divides activation maps of DNNs into several regions according to the activation size and quantizes them to 4-bit by setting scale factors adaptively for each region. As a result of applying the proposed method to the backbone of YOLACT, a representative instance segmentation model, the proposed method achieves approximately 2% increase in both box and mask mAPs compared to the naive 4-bit activation quantization.
A Program Synthesis Approach for Adding Architectural Tactics to An Existing Code Base

Automatically constructing a program based on given specifications has been studied for decades. Despite the advances in the field of Program Synthesis, the current approaches still synthesize a block of code snippet and leave the task of reusing it in an existing code base to program developers. Due to its program-wide effects, synthesizing an architectural tactic and reusing it in a program is even more challenging. Architectural tactics need to be synthesized based on the context of different locations of the program, broken down to smaller pieces, and added to corresponding locations in the code. Moreover, each piece needs to establish correct data- and control-dependencies to its surrounding environment as well as to the other synthesized pieces. This is an error-prone and challenging task, especially for novice program developers. In this paper, we introduce a novel program synthesis approach that synthesizes architectural tactics and adds them to an existing code base.
Co-Saliency Detection Via Unified Hierarchical Graph Neural Network With Geometric Attention

Co-saliency detection aims to identify the common and salient objects from a group of relevant images. The main challenge for co-saliency detection is how to mine and exploit the saliency cues of both intra-image and inter-image. In this paper, we present a novel unified hierarchical neural network (UHGNN). We first construct the graph model by segmenting the images into super-pixels and extracting the intra-image hierarchical saliency cues. Then, the inter-image hierarchical saliency representation is mined to form the unified two-dimensional hierarchical feature setup. We further propose the geometric attention module to make the most of the intra-image and inter-image cues. Our UHGNN model competes or outperforms the state-of-the-art methods on two co-saliency detection benchmark datasets (MSRC, iCoSeg).
An Investigation of Stereoscopic Vision and Real-Scale Body Motions in a Virtual Game

It is necessary to recognize the virtual area exactly and interact with 3D information intuitively to work smoothly in a virtual space. This study created a virtual reality (VR) game environment that integrated stereoscopic vision based on a binocular parallax by a head mounted display (HMD) with body motion measurement by a range image sensor. Also, to enhance immersion and operation feeling, we presented tactile information using a vibrotactile globe. We verified the usefulness of the operation by real-scale body motions, stereoscopic vision, and tactile information. From the results of experiments, we could confirm that stereoscopic vision improves the game impression, and the operation by real-scale body motions improves game scores in the VR game environment created.
SafeSwim: Overhead vision to help people see better

Traditionally video surveillance consists of overhead cameras to help authorities such as lifeguards or security professionals monitor individuals. This monitoring, if in service of the monitored individuals, serves them only indirectly. More recently, a new kind of sensing called "sousveillance" has emerged, as a form of sensing by, and in direct service to, individuals. In this paper, we explore an overhead camera (e.g. aerial drone-based videography) to help create a form of real-time seeing aid, with specific application to help one or more individual swimmers see better and navigate, e.g. to avoid collisions with other swimmers (or the walls) in pools, as well as navigating in lakes, rivers, and other bodies of open water. Additionally the system can be used to teach swimming (e.g. learn by seeing from a birds-eyeview) as well as to gamify swim-based activities. SafeSwim (and sousveillance in general) is a new form of "seeing" in which those being watched have direct visual access to the live video stream. It is a direct departure from traditional surveillance where authorities such as police or lifeguards watch individuals without giving them direct access to a live video stream.
Smoothing graphons for modelling exchangeable relational data

Modelling exchangeable relational data can be described appropriately in graphon theory. Most Bayesian methods for modelling exchangeable relational data can be attributed to this framework by exploiting different forms of graphons. However, the graphons adopted by existing Bayesian methods are either piecewise-constant functions, which are insufficiently flexible for accurate modelling of the relational data, or are complicated continuous functions, which incur heavy computational costs for inference. In this work, we overcome these two shortcomings by smoothing piecewise-constant graphons, which permits continuous intensity values for describing relations, without impractically increasing computational costs. In particular, we focus on the Bayesian Stochastic Block Model (SBM) and demonstrate how to adapt the piecewise-constant SBM graphon to the smoothed version. We first propose the Integrated Smoothing Graphon (ISG) which introduces one smoothing parameter to the SBM graphon to generate continuous relational intensity values. Then, we further develop the Latent Feature Smoothing Graphon (LFSG), which improves the ISG, by introducing auxiliary hidden labels to decompose the calculation of the ISG intensity and enable efficient inference. Experimental results on real-world data sets validate the advantages of applying smoothing strategies to the Stochastic Block Model, demonstrating that smoothing graphons can greatly improve AUC and precision for link prediction without increasing computational complexity.
Research on Application of Deep Learning Based on Artificial Intelligence in High-noise Image Denoising

Abstract Image denoising is to reduce or eliminate the influence of noise on the image to obtain the original image with less error. In order to remove the noise in the image more effectively, this article builds a convolutional neural network for image denoising based on the artificial intelligence-based deep learning method, and trains it through a high-noise image data set, and analyzes the training data set and model its own influence on the denoising effect, the results show that the increase in the number of layers is beneficial to denoising.
Superconducting Nanowire Photon-Number-Resolving Detectors Integrated with Current Reservoirs and yTron Readouts

We propose and design superconducting nanowire photon-number-resolving detectors integrated with current reservoirs and yTron readouts, which can resolve up to 11 photons with high fidelity.
Research on the Application of Computer Vision Technology in Aquaculture

Abstract At present, domestic aquaculture is still dominated by artificial aquaculture, the automation level of aquaculture process is low, and the basic research on aquaculture is relatively lacking, which makes the intelligent management and monitoring ability of aquaculture weak. Based on this, this paper first analyses the current situation and development trend of computer vision tech, then studies the computer vision recognition scheme design of aquaculture, and finally gives the specific utilization of computer vision tech in aquaculture.
Leadership approach towards Agile, Waterfall and Iterative implementation of the software development products

Abstract The automotive industry is one of the most dynamic markets, gathering around many different sectors from material extractions to robotics and cloud computing. The current research analyses the influences and particularities of leadership and identifies the challenges and advantages of the two principal methodologies in software development projects within the automotive industry, Agile and Waterfall. Some aspects of reducing methodological disadvantages are suggested and presented. The aim is to arrive at a sustainable solution that can ensure the delivery of reliable and flexible software products. The impact of these methodologies on the quality standards and project documentation is analyzed, which is a vital part of automotive software development. By influencing the product development lifecycle, there is an opportunity to develop a structured framework that can be adopted to build software products in a more flexible and agile way.
Expanding Affective Computing Paradigms Through Animistic Design Principles

Animistic and anthropomorphic principles have long been investigated along with affective computing in both HCI and HRI research, to reduce user frustration and create more emotive yet relatable devices, robots, products and artefacts. Yet such artefacts and research have mainly been from user-centric perspectives and the animistic characteristics localised to single objects. In this exploratory paper, we take these principles in a new direction by attempting to invoke animistic characteristics of a room or a space itself. Designing primarily for space itself rather than the user or a single product, allows us to create new interactions and narratives that can induce animism and empathy for the space, in users. This leads to the creation of a prototype space, which we use to investigate how users approach, interact and behave in such a space, yielding several insights and user behaviour, all of which can be used for further studies, capable of generating new interaction perspectives and providing insights into user behaviour. We conclude by discussing the potentiality of such spaces in developing new strategies for behaviour change and HCI.
Evaluation of Face Recognition Systems

WhileEvaluation face recognition research has been perennial and popular since its inception, there has been a marked escalation in this research in recent years due to the confluence of several factors, primarily the development of advanced machine learning algorithms, free and robust software implementations thereof, ever faster GPU processors for running them, vast web-scraped face image databases, open performancePerformance benchmarks, and a vibrant face recognition literature.
MODIFIED DARK CHANNEL PRIOR Modified DCP for Dehazing

The image captured by camera is degraded by various atmospheric parameters for example rain, storm, wind, haze, snow. The removing haze is called dehazing, is naturally done in the physical degradation model, which requires a solution of an ill-posed inverse problem. In this paper we will discuss and do the comparative study of Adaptive Histogram Equalization (AHE), Contrast limited adaptive histogram equalization (CLAHE) and dark channel prior (modified DCP). This article suggest image and video image defogging algorithm working on dark channel prior technique. The modified DCP is derived from the characteristic of natural outdoor images that the intensity value of at least one color channel within a local window is close to zero. The modified DCP system has good haze elimination and color managing potential for the images with various angles of haze. The dehazing is done through four major steps: atmospheric light estimation, transmission map estimation, transmission map refinement, and image reconstruction. This four step solution of modified DCP will give solution to ill-posed inverse problem. This dehazing techniques can be used in advanced driverless assisted systems in autonomous cars, satellite imaging, underwater imaging etc
Logical approach to the analysis of economic development programs

Abstract The present paper proposes a logical model for building an effective economic development programmer. It is built on the basis of existing programs, which are not the best in terms of their strategic implementation, but can improve some economic indicators. As part of our model, these programs will be considered as elements of a logical scheme that should implement a development program with optimal indicators.
Fast Feature Matching Augmented Reality Method on Dynamic Image Towards Multimedia

This paper presents an application algorithm for dynamic image augmented reality rendering based on feature extraction of natural regions of images. For the natural features of template and recognition images, an ORB algorithm based on FAST feature point extraction and BRIEF feature vector operator is used to identify and match image features, which can quickly and accurately obtain image feature matching information. The generating dynamic image keyframe extraction by using the interpolation animation technology which can be applied to the dynamic image augmented reality application rendering of mobile camera equipment. The experimental results show that this algorithm can be effectively applied to the fusion rendering based on natural feature recognition, especially in the field of digital culture publishing, the dynamic augmented reality display of static text image information. It expands the dimension of information and has strong application innovation value.
Generating Long and Coherent Text with Multi-Level Generative Adversarial Networks

In this paper, we study the task of generating long and coherent text. In the literature, Generative Adversarial Nets (GAN) based methods have been one of the mainstream approaches to generic text generation. We aim to improve two aspects of GAN-based methods in generic text generation, namely long sequence optimization and semantic coherence enhancement. For this purpose, we propose a novel Multi-Level Generative Adversarial Networks (MLGAN) for long and coherent text generation. Our approach explicitly models the text generation process at three different levels, namely paragraph-, sentence- and word-level generation. At the top two levels, we generate continuous paragraph vectors and sentence vectors as semantic sketches to plan the entire content. While, at the bottom level we generate discrete word tokens for realizing the sentences. Furthermore, we utilize a Conditional GAN architecture to enhance the inter-sentence coherence by injecting paragraph vectors for sentence vector generation. Extensive experiments results have demonstrated the effectiveness of the proposed model.