Reviewerly/exHarmony huggingface dataset:
Corporate Digital Responsibility at the Dawn of the Digital Service Revolution

Digitization, artificial intelligence (AI), and service robots will revolutionize virtually all service industries and have the potential to bring unprecedented improvements in customer experience, service quality, and productivity. However, these technological advances also carry serious ethical, fairness, and privacy risks. To explore these risks, this article builds on recent work on corporate digital responsibility (CDR). First, it uses a life-cycle stage perspective of data and technologies (i.e., their creation, operation, refinement, and retainment) as an organizing frame to better understand these risks. Second, to provide insights on where these risks originate, it examines an organization’s business model and its flows of money, service, data, insights, and technologies with customers at the front-end and the business partner ecosystem at the back-end. Third, this article identifies why CDR issues occur. It shows that trade-offs between good CDR practices and five organizational objectives and motivations are key underlying causes (e.g., incremental sales can be achieved with better data and analytics but carry a gamut of privacy, fairness, discrimination and psychological risks). Finally, this article proposes a set of strategies, tools and practices managers can use to address these trade-offs and build a CDR-strong service organization.
Robust and accurate pupil detection for head-mounted eye tracking

In head-mounted eye tracking, robust and accurate pupil detection in real and mobile environments is still challenging, as pupil images are contaminated by various interferences, such as eyelids, eyelashes, and illumination. Based on the characteristics of pupil images, i.e., the pupil–iris contrast and the pupil darkness, the pupil region is represented by a horizontal weighted Haar-like feature. Based on this feature, a method called Compact Pupil Region Detection (CPRD) is proposed. Moreover, by adding the initial rectangle of the pupil region as prior knowledge, the CPRD-init method further improves the performance. Within the detected region, ellipse fitting is applied to extract the pupil ellipse. Experimental results on the benchmark database LPW show that the detection success ratio and 5-pixels error ratio increase by 7.1% and 3.9%, respectively, compared with state-of-the-art methods.
A new model for classification of human movements on videos using convolutional neural networks: MA-Net

Classification of human movements is essential for interpreting and describing human activities, such as environment-supported living in smart home environments, elderly nursing homes, visual tracking, object tracking, anomaly detection, medical visualisation and mimic analysis. Also, human movements recognition from videos has become one of the important issues that arise with the developing technology and the processing of big data in computers. In this paper, it is aimed to classify human movements by using a data set including different motion videos. For this aim, a new model MA-Net named by us is proposed. MA-Net have 43 layers. In order to examine MA-Net, data having150 videos and 10 classes in UCF dataset is examined. At first, the frames from videos are extracted. In study, it has been worked to take one frame in 50 frames in videos. After that, dataset is classified using well known models such as Resnet50, Alexnet, Inceptionv3, Densenet201 architectures. After, proposed new model MA-Net are classified too. The highest accuracy rate is obtained from MA-Net model as 91.34%.
Three principles for the progress of immersive technologies in healthcare training and education

The COVID-19 crisis has fundamentally transformed the healthcare training and education landscape. There is now a desperate need for a system-wide exploration of scalable, flexible and resilient solutions that can mitigate the long-term impact on the development of a skilled healthcare workforce. A new generation of immersive technologies (e.g., augmented and virtual reality) have the potential to address many of these issues but their potential is yet to be realised. We recently brought together clinicians, researchers and industry under the banner of the ‘Immersive Healthcare Collaboration’, to create a set of principles to maiximise the utility of these technologies for training and education. Here, we provide a summary of the principles outlined in this Immersive Healthcare Collaboration report. Principle 1 states that the design and development of immersive tools should be driven by learning requirements and informed by the science of human behaviour and cognition. Principle 2 emphasises the need for rigorous evaluation prior to, and during, implementation through an adoption of open science and transparent research practices. In Principle 3 we propose that the first two principles will be best achieved by fostering a culture of collaboration, inclusivity and solidarity between developers, scientists, educationalists and healthcare professionals. From the lessons learned during the pandemic it is clear that the healthcare system is poised for a training and education delivery reform. By following these principles, we are optimistic that the potential of immersive technologies can be harnessed for the benefit of the healthcare community and the delivery of patient care.
FPGA Implementation and Study of Synchronization of Modified Chua’s Circuit-Based Chaotic Oscillator for High-Speed Secure Communications

This paper presents an implementation of modified Chua's circuit-based chaos generator in a field-programmable gate array (FPGA) and explores the synchronization of such generators for use in secure communication systems. The chaos generator design employs a fixed-point numeric format and the Forward Euler differential equation numerical solution method. The implementation of chaotic generator on Intel Cyclone V FPGA demonstrates the stable generation of sampled chaotic signal with up to 76 MHz clock frequency. The correctness of the realization is verified using a comparative analysis of the Simulink functional model and FPGA implementation.
Binocular stereo vision calibration based on accurate ellipse detection algorithm of direct calculation and grating conversion check

With the rapid development of science and technology, binocular vision detection technology has become one of the most important technologies because of its high precision and stability. In order to achieve high-precision three-dimensional measurement of high-speed rail train, a binocular stereo vision system is built in this paper. The visual image is gray-scale processed, and then the integral image is used to realize the edge preservation processing of local mean square deviation, and then the denoised image is enhanced. At the same time, FAST algorithm based on accelerated segmentation detection is proposed to extract feature points. In order to solve the problem of camera calibration, this paper proposes a high-precision binocular stereo vision calibration algorithm based on direct calculation and grating conversion calibration. Firstly, the reliable information points are determined from the two-dimensional image, and then the edge image is obtained by effectively detecting the ellipse contour according to the set of information points. Finally, the ellipse parameters are extracted by using the ellipse contour information, and finally the camera parameters are solved by using the ring point. Compared with the traditional calibration method, the experimental results show that the average reprojection error of the two cameras is about 0.1 pixel, and the data accuracy is high, which proves the feasibility and theoretical correctness of the experimental method.
Automatic Prostate Cancer Detection On Multi-Parametric Mri With Hierarchical Weakly Supervised Learning

Multi-parametric MRI (mp-MRI) is one of the most commonly used non-invasive methods for prostate cancer (PCa) diagnosis. In recent years, computer aided diagnosis (CAD) for PCa on mp-MRI based on deep learning techniques has gained much attention and shown promising progress. The key for the success of deep learning based PCa diagnosis is to obtain a large amount of high quality PCa region annotation on mp-MRI such that the network can accurately learn the large variation of PCa lesions. In order to precisely annotate the PCa region on mp-MRI, the pathological whole mount data of the patient is normally required as reference, which is often difficult to obtain in real world clinical situations. Therefore, we are motivated to propose a new deep learning based method to integrate different levels of information available in the PCa screening workflow through a multitask hierarchical weakly supervised framework for PCa detection on mp-MRI. Experimental results show that our method achieves promising PCa detection and segmentation results.
Chinese Lexical Simplification

Lexical simplification has attracted much attention in many languages, which is the process of replacing complex words in a given sentence with simpler alternatives of equivalent meaning. Although the richness of vocabulary in Chinese makes the text very difficult to read for children and non-native speakers, there is no research work for the Chinese lexical simplification (CLS) task. To circumvent difficulties in acquiring annotations, we manually create the first benchmark dataset for CLS, which can be used for evaluating the lexical simplification systems automatically. To acquire a more thorough comparison, we present five different types of methods as baselines to generate substitute candidates for the complex word that includes synonym-based approach, word embedding-based approach, BERT-based approach, sememe-based approach, and a hybrid approach. Finally, we design the experimental evaluation of these baselines and discuss their advantages and disadvantages. To our best knowledge, this is the first study for CLS task.
1,2,3,4 tell me how to grow more: A position paper on children, design ethics and biowearables

Driven by the rapid pace of technical innovation in biosensing, artificial intelligence, the Internet of Things and wearable computing, the next generation of smart devices will be worn on-body, eventually becoming implanted. The increasing presence of these new forms of interactive technologies, known as biowearables, in children's lives poses critical ethical concerns. In this position paper, we take a design ethics perspective to identify and describe four cases of ethical importance associated with biowearables, children, and long-term use. The cases concern potential negative impacts of specific aspects of biowearables on children's identity formation, the development of autonomy and agency, and what sources of information children turn to for authority about themselves. Drawing on ethical discourse related to emerging technologies and biowearable computing, we present prospective guidance for designers, where it is available. Where guidance is nascent or missing, we propose future research areas that could be addressed. In particular, we propose the importance of teaching children about computer ethics through hands-on critical reflection during design and technology activities. Our results will be of interest to the human–computer interaction community as well as to technology developers, educators, parents and those involved in policy formation around emerging technologies.
Verifiable Random Functions with Optimal Tightness

Verifiable random functions (VRFs), introduced by Micali, Rabin and Vadhan (FOCS'99), are the public-key equivalent of pseudorandom functions. A public verification key and proofs accompanying the output enable all parties to verify the correctness of the output. However, all known standard model VRFs have a reduction loss that is much worse than what one would expect from known optimal constructions of closely related primitives like unique signatures. We show that: 1. Every security proof for a VRF that relies on a non-interactive assumption has to lose a factor of Q, where Q is the number of adversarial queries. To that end, we extend the meta-reduction technique of Bader et al. (EUROCRYPT'16) to also cover VRFs. 2. This raises the question: Is this bound optimal? We answer this question in the affirmative by presenting the first VRF with a reduction from the non-interactive qDBDHI assumption to the security of VRF that achieves this optimal loss.
University Teaching Planning in Times of COVID-19: Analysis of the Catalan Context and Proposal for a Future Model from ESIC Business and Marketing School Experience

On 14 March 2020, Spain came to a standstill and the movement of people was restricted with the publication of Royal Decree (RD) 463/2020 and the education sector had to reinvent itself with new rules and procedures. The purpose of this paper is to analyse the impact of the different government regulations and their effect on university teaching planning. Since the approval of RD 555/2020, educational competences were returned to the autonomous communities and each of them implemented different public policies. We will analyse the specific impact on teaching planning and models applied in Catalonia and their development, which vary according to the evolution of the number of COVID-19 infections and which has been marking the political, economic and social agenda since the beginning of the pandemic. The university has moved from teaching in a face-to-face environment to a virtual or semi-virtual or blended learning environment. This change implies a paradigmatic transformation in communication, education, organisation, evaluation and planning, among others. At ESIC Business &amp; Marketing School (ESIC) these pedagogical paradigm shifts have contributed to transform the learning processes in a context of pedagogical innovation.
Revitalizing Optimization for 3D Human Pose and Shape Estimation: A Sparse Constrained Formulation

We propose a novel sparse constrained formulation and from it derive a real-time optimization method for 3D human pose and shape estimation. Our optimization method, SCOPE (Sparse Constrained Optimization for 3D human Pose and shapE estimation), is orders of magnitude faster (avg. 4ms convergence) than existing optimization methods, while being mathematically equivalent to their dense unconstrained formulation under mild assumptions. We achieve this by exploiting the underlying sparsity and constraints of our formulation to efficiently compute the Gauss-Newton direction. We show that this computation scales linearly with the number of joints and measurements of a complex 3D human model, in contrast to prior work where it scales cubically due to their dense unconstrained formulation. Based on our optimization method, we present a real-time motion capture framework that estimates 3D human poses and shapes from a single image at over 30 FPS. In benchmarks against state-of-the-art methods on multiple public datasets, our framework outperforms other optimization methods and achieves competitive accuracy against regression methods. Project page with code and videos: https://sites.google.com/view/scope-human/.
Oriented Object Detection by Searching Corner Points in Remote Sensing Imagery

Oriented object detection in remote sensing images has drawn great attention since it can provide more accurate bounding boxes. We propose a one-stage anchor-free network based on searching four corner points of an object, which can yield an arbitrary quadrilateral to fit objects with different shapes and orientations. We detect the corners by combining two strategies, where one regresses to the relative corner positions with respect to their corresponding center and the other directly detects the absolute corner positions from the corner heatmaps. By defining a candidate corner region based on the regressed results, we check whether corner points from the corner heatmaps are included in the region. If so, the closest one relative to the regressed corner is selected as the final position; otherwise, the regressed corner position is utilized. Experiments were conducted on two aerial remote sensing datasets, and the results demonstrated that the proposed method achieves superior performance to both the anchor-based and anchor-free methods.
QoS‐aware big service composition using distributed co‐evolutionary algorithm

Abstract Big services are collections of interrelated web services across virtual and physical domains, processing Big Data. Existing service selection and composition algorithms fail to achieve the global optimum solution in a reasonable time. In this paper, we design an efficient quality of service‐aware big service composition methodology using a distributed co‐evolutionary algorithm. In our proposed model, we develop a distributed NSGA‐III for finding the optimal Pareto front and a distributed multi‐objective Jaya algorithm for enhancing the diversity of solutions. The distributed co‐evolutionary algorithm finds the near‐optimal solution in a fast and scalable way.
Determining Differences in Reading Behavior Between Experts and Novices by Investigating Eye Movement on Source Code Constructs During a Bug Fixing Task

This research compares the eye movement of expert and novice programmers working on a bug fixing task. This comparison aims at investigating which source code elements programmers focus on when they review Java source code. Programmer code reading behaviors at the line and term levels are used to characterize the differences between experts and novices. The study analyzes programmers’ eye movements over identified source code areas using an existing eye tracking dataset of 12 experts and 10 novices. The results show that the difference between experts and novices is significant in source code element coverage. Specifically, novices read more method signatures, variable declarations, identifiers, and keywords compared to experts. However, experts are better at finishing the task using fewer source code elements when compared to novices. Moreover, programmers tend to focus on the method signatures the most while reading the code.
Fine-tuning Pretrained Multilingual BERT Model for Indonesian Aspect-based Sentiment Analysis

Although previous research on Aspect-based Sentiment Analysis (ABSA) for Indonesian reviews in hotel domain has been conducted using CNN and XGBoost, its model did not generalize well in test data and high number of OOV words contributed to misclassification cases. Nowadays, most state-of-the-art results for wide array of NLP tasks are achieved by utilizing pretrained language representation. In this paper, we intend to incorporate one of the foremost language representation model, BERT, to perform ABSA in Indonesian reviews dataset. By combining multilingual BERT (m-BERT) with task transformation method, we manage to achieve significant improvement by 8% on the F1-score compared to the result from our previous study.
Face Mask Detection Using Deep Learning During COVID-19

With the onset of the COVID-19 pandemic, the entire world is in chaos and is talking about novel ways to prevent virus spread. People around the world are wearing masks as a precautionary measure to prevent catching this infection. While some are following and taking this measure, some are not still following despite official advice from the government and public health agencies. In this paper, a face mask detection model that can accurately detect whether a person is wearing a mask or not is proposed and implemented. The model architecture uses MobileNetV2, which is a lightweight convolutional neural network, therefore requires less computational power and can be easily embedded in computer vision systems and mobile. As a result, it can create a low-cost mask detector system that can help to identify whether a person is wearing a mask or not and act as a surveillance system as it works for both real-time images and videos. The face detector model achieved high accuracy of 99.98% on training data, 99.56% on validation data, and 99.75% on testing data.
TICNet: A Target-Insight Correlation Network for Object Tracking

Recently, the correlation filter (CF) and Siamese network have become the two most popular frameworks in object tracking. Existing CF trackers, however, are limited by feature learning and context usage, making them sensitive to boundary effects. In contrast, Siamese trackers can easily suffer from the interference of semantic distractors. To address the above problems, we propose an end-to-end target-insight correlation network (TICNet) for object tracking, which aims at breaking the above limitations on top of a unified network. TICNet is an asymmetric dual-branch network involving a target-background awareness model (TBAM), a spatial-channel attention network (SCAN), and a distractor-aware filter (DAF) for end-to-end learning. Specifically, TBAM aims to distinguish a target from the background in the pixel level, yielding a target likelihood map based on color statistics to mine distractors for DAF learning. SCAN consists of a basic convolutional network, a channel-attention network, and a spatial-attention network, aiming to generate attentive weights to enhance the representation learning of the tracker. Especially, we formulate a differentiable DAF and employ it as a learnable layer in the network, thus helping suppress distracting regions in the background. During testing, DAF, together with TBAM, yields a response map for the final target estimation. Extensive experiments on seven benchmarks demonstrate that TICNet outperforms the state-of-the-art methods while running at real-time speed.
Multimode Approach of Data Encryption in Images Through Quantum Steganography

The information technology era or the third industrial revolution began around the 1960s; has changed the ways we live, work, and play; and brought substantial challenges that include loss of privacy, fake news, digital divides, and significant information security risks. With billions of connections and systems, security vulnerabilities are abundant including the opportunity for criminals to exploit any gaps that present themselves. Eventually, we'll need a groundbreaking technology to gain the upper hand against these threats. Protecting data, systems, and networks assumed a more specific term: cybersecurity. The goal of cybersecurity today is to protect information while it's at rest and in motion. One of the most interesting ways to deliver hidden information is through steganographic technique.
Unseen to Seen by Digital Steganography

In this digital era, most of the daily works of users are captured in digital forms like image, audio, video, or even text. These may contain sensitive, confidential, or private information as well. To keep these, there are different techniques exercised over the years like chest, coffer, repository, treasury, etc. Sometimes such ways of hiding information attract unintended users. Therefore, steganography has been applied to digital objects in a new form called digital steganography. This chapter enforces identifying methods in digital steganography. The basic model of any type of digital steganography is composed of three types of objects: cover object, text to be hidden, and stego object. Depending on the type of cover object, different types of digital steganography methods have been defined. Only data hiding in text is discussed.