Reviewerly/exHarmony huggingface dataset:
ETHICAL REVIEW BOARDS AND PERVASIVE DATA RESEARCH: GAPS AND OPPORTUNITIES

The growing prevalence of data-rich networked information technologies—such as social media platforms, smartphones, wearable devices, and the internet of things —brings an increase in the flow of rich, deep, and often identifiable personal information available for researchers. More than just “big data,” these datasets reflect people’s lives and activities, bridge multiple dimensions of a person’s life, and are often collected, aggregated, exchanged, and mined without them knowing. We call this data “pervasive data,” and the increased scale, scope, speed, and depth of pervasive data available to researchers require that we confront the ethical frameworks that guide such research activities. Multiple stakeholders are embroiled in the challenges of research ethics in pervasive data research: researchers struggle with questions of privacy and consent, user communities may not even be aware of the widespread harvesting of their data for scientific study, platforms are increasingly restricting researcher’s access to data over fears of privacy and security, and ethical review boards face increasing difficulties in properly considering the complexities of research protocols relying on user data collected online. The results presented in this paper expand our understanding of how ethical review board members think about pervasive data research. It provides insights into how IRB professionals make decisions about the use of pervasive data in cases not obviously covered by traditional research ethics guidelines, and points to challenges for IRBs when reviewing research protocols relying on pervasive data.
Using Flask and Jinja

Flask and Jinja are the main two components of a Python system to manage a dynamic website. This system has similarities to other systems like Django. All of these systems consist of a web server and two Python components—a management component and a dynamic web page component. The idea behind this system is that the website should not be constrained to only one way of solving a problem. The designer should be able to choose the components that work for their situation.
FPGA Based Accelerators of Deep Learning Networks for Learning and Classification

A recent trend is to execute computationally intensive algorithms (or work flows) for business analytics using cloud environments which provide machine learning hardware support in the form of GPUs and TPUs. Businesses obtain their data at the sensor level and then perform algorithmic operations on the data via these cloud services. As a result, there can be high input/output data latency which tends to slow down productivity. This thesis work will explore the topic of executing computationally complex algorithms, such as the Convolutional Neural Network (CNN), Recurrent Neural Network(RNN) and Spike Neural Network (SNN), at the sensor level through the use of FPGAs (Field Programmable Gate Arrays) as an alternative to cloud-bound GPU and TPU services.
Latent 3D Volume for Joint Depth Estimation and Semantic Segmentation from a Single Image

This paper proposes a novel 3D representation, namely, a latent 3D volume, for joint depth estimation and semantic segmentation. Most previous studies encoded an input scene (typically given as a 2D image) into a set of feature vectors arranged over a 2D plane. However, considering the real world is three-dimensional, this 2D arrangement reduces one dimension and may limit the capacity of feature representation. In contrast, we examine the idea of arranging the feature vectors in 3D space rather than in a 2D plane. We refer to this 3D volumetric arrangement as a latent 3D volume. We will show that the latent 3D volume is beneficial to the tasks of depth estimation and semantic segmentation because these tasks require an understanding of the 3D structure of the scene. Our network first constructs an initial 3D volume using image features and then generates latent 3D volume by passing the initial 3D volume through several 3D convolutional layers. We apply depth regression and semantic segmentation by projecting the latent 3D volume onto a 2D plane. The evaluation results show that our method outperforms previous approaches on the NYU Depth v2 dataset.
Impact of COVID-19 outbreak on sleep quality of medical staff in Wuhan

Objective: To investigate the sleep quality of medical staff in response to the outbreak of coronavirus disease 2019 (COVID-19) in Wuhan, Hubei Methods: We did a sleep quality survey with Pittsburgh Sleep Quality Index (PSQI) and questionnaire in frontline medical staff who were working in the period of COVID-19 outbreak, and the results were compared with that of non-medical staff Results: The total number of people included in this study was 362, of which 218 were in the first-line medical staff group, 38 were in the COVID-19 diagnosed medical staff group;106 were in the non-medical staff group;and there was no significant difference in genders and ages among the three groups During the outbreak of COVID-19, sleep disturbance symptoms (PSQI7 points) were presented with some degree in these groups The proportions of sleep disturbance in the frontline medical staff group and COVID-19 diagnosed medical staff group were significantly higher than that of non-medical staff (P0 001);The proportion of severe sleep disturbance (PSQI10) in the first-line medical staff group and COVID-19 diagnosed medical staff group were also significantly higher than that of non-medical staff (P0 001) Compared with non-medical staff, frontline medical staff had statistical differences in sleep quality, sleep latency, sleep time and daytime dysfunction (P0 01);The main causes of sleep disorders in first-line medical staff were significantly related to high workload and fear of infection Conclusions: The outbreak of COVID-19 increases the incidence and severity of sleep disorders among frontline medical staff in Wuhan, which is related to high workload and fear of infection © 2020, Editorial Board of Medical Journal of Wuhan University All right reserved
Research on the Application of Augmented Reality Technology in TV Programs

Augmented reality technology is a technology that integrates real world information and virtual world information. The application of augmented reality technology in TV programs is to simulate and display some video, audio, even taste, environmental atmosphere, etc. that are difficult to experience in the real world through computer technology, so that users can pass visual, auditory, and taste And touch. In the field of television production, from the teletext system in the 1990s to the virtual studio system and real-time virtual implantation system, TV workers have been using technical means to superimpose different virtual information on the video images taken by the camera Above. TV practitioners use ar technology to present the audience with more brilliant pictures, richer information, and more refined TV pictures. At this stage, the augmented reality production technology emphasized in the field of television production is mainly visual augmented reality technology. This is a technology based on real-time tracking the position of the image taken by the camera, and superimposing the corresponding video, audio, graphic information in real time through the computer system. This technology can superimpose virtual information on the TV screen to the real world. The ordinary TV screen not only displays real-world information, but also displays virtual information at the same time. The two kinds of information complement and superimpose each other, and even through elaborate program creative design, a good interaction effect between the real world and the virtual world can be achieved, which ultimately makes it difficult for viewers to distinguish between the virtual world and the real world in front of the TV screen.
Equality of rights vs equality of opportunities in higher education

The paper examines dynamics in consumption of cultural goods in Russia.The sources of empirical data are the Russian Longitudinal Monitoring Survey (RLMS-HSE) for 2000-2018 and Rosstat's Complex Living Conditions Survey (CLCS) for 2011-2018.The analysis indicates that now approximately every second household and approximately every second adult individual consumes some cultural goods during the year.A regular audience is approximately one fifth of the total.The share of expenses on cultural goods reaches 2.5% of average monthly household budget.With the rapid income growth in 2000-2018 demand for cultural goods has been steadily increasing.There is a visible empirical regularity: the higher the income of households or individuals, the more active they are culturally.There appears to be a fairly banal, but effective practical way to stimulate demand for cultural goods in Russia: an acceleration of economic growth.
Need Analysis for Choosing English Teaching Concepts at IPDN

English is a subject that given in the three curriculum systems at IPDN has not been implemented maximally. The three existing curriculum systems provide almost the same material and there are not clear and obvious differences. In order to improve the teaching conditions, need analysis is used to find and determine what English learning materials are appropriate and suitable for teaching and learning at IPDN. A descriptive qualitative approach through document study, interview, and observation was used in this research. From the analysis, teaching English must be developed with the collaborative approach among education personnel (lecturers, trainers, fellows) and students in the three curriculum systems at IPDN. English teaching emphasizes reading skills, where teaching reading focuses on English for Specific Purposes of Government administration as core knowledge.
Evaluating Pre-trained Word Embeddings and Neural Network Architectures for Sentiment Analysis in Spanish Financial Tweets

Sentiment Analysis supports decision making in the financial domain by gaining rapid insights regarding brand image as well as the perceived quality of their services and products in the market. The state of the art regarding Sentiment Analysis consists of using pre-trained word embeddings from large unannotated corpora in order to capture rich and meaningful properties of the words together with their semantic relationships. These rich semantic representations are used to feed a neural network in order to learn to distinguish among positive, negative or neutral texts. However, although pre-trained word embeddings have been applied to different domains and languages, as far as our knowledge goes, there are no studies regarding their reliability applied to the financial domain in Spanish. Consequently, we compiled and labelled a corpus composed of 7,435 tweets from economists and financial news sites and we evaluated the performance of different pre-trained word embeddings, some well-known neural network architectures and linguistic features. Our results indicate that the fastText model, trained with the Spanish Unannoted Corpora and in conjunction with linguistic features, achieved the best accuracy of 58.036% using a Gated Recurrent Unit. As an extra contribution, the compiled corpus was released to the scientific community.
Late Breaking Results: Automated Hardware Generation of CNN Models on FPGAs

In this paper, we propose an automated framework that takes as input a TensorFlow inference graph and generates high-performance accelerators on FPGA by assembling CNN pre-implemented components as a puzzle, based on the graph topology. Using pre-implemented components allows us the only use the minimum of resources necessary, predict the performance and a gain in productivity We adopt a unified representation based on systolic array to perform the computational-hungry operations of the model and provide novel analysis of design trade-offs for FPGA CNN accelerators. Experimental results show the great performance, low latency and flexibility provided by the proposed framework.
KPI FOR STUDENTS SATISFACTION MEASUREMENT AS AN INDICATOR OF UNIVERSITY MANAGEMENT

Currently universities need to be competitive. The current environment forces universities to design strategies where the student becomes the center of their activity. Elements such as student satisfaction become key performance indicators of the strategy, so that universities are required to establish metrics that make it possible to measure the level of performance achieved based on the objectives they had previously set. In order to verify it, through a series of dimensions that measure the satisfaction of the teaching-learning process, a scale has been proposed to measure satisfaction. To confirm the consistency of the proposed scale, the reliability and validity indicators of the same were verified. The selected sample corresponds to university students studying undergraduate and master’s degrees in the online mode. Results obtained show the importance of correctly setting the objectives and contents of the different subjects that make up the curricula of each grade, as well as the outstanding role that the teacher has in said process.
The Derivation of Defect Priorities and Core Defects through Impact Relationship Analysis between Embedded Software Defects

As embedded software is closely related to hardware equipment, any defect in embedded software can lead to major accidents. Thus, all defects must be collected, classified, and tested based on their severity. In the pure software field, a method of deriving core defects already exists, enabling the collection and classification of all possible defects. However, in the embedded software field, studies that have collected and categorized relevant defects into an integrated perspective are scarce, and none of them have identified core defects. Therefore, the present study collected embedded software defects worldwide and identified 12 types of embedded software defect classifications through iterative consensus processes with embedded software experts. The impact relation map of the defects was drawn using the decision-making trial and evaluation laboratory (DEMATEL) method, which analyzes the influence relationship between elements. As a result of analyzing the impact relation map, the following core embedded software defects were derived: hardware interrupt, external interface, timing error, device error, and task management. All defects can be tested using this defect classification. Moreover, knowing the correct test order of all defects can eliminate critical defects and improve the reliability of embedded systems.
Stemming Algorithm for Indonesian Signaling Systems (SIBI)

The Indonesian Language Sign System (SIBI) is a medium used by deaf people to communicate with the wider community. SIBI is Indonesian that is presented by hand. The sentence structure (morphology) of SIBI is the same as the Indonesian language, SIBI also recognizes the prefix, principal and suffix sign word. This research discusses how to make stemming for SIBI, the algorithm used for stemming is Nazief Adiri. Stemming for SIBI separates input sentences, words through the Text Processing process. Then the word in Stemming so that it can be separated into prefix, main, and suffix words. After stemming, the words obtained in indexing can find the right SIBI, by using ID. Good stemming must pay attention to Indonesian Morphology. The number of words used for this stemming trial is 50 words. By using the Confusion Matrix method found 0.94% accuracy, 93% recall, and 84% precision.
Children’s sense-making through exploration

A child’s sense-making is grounded in his or her bodily interactions with the environment and tied to the body’s sensory experience. Digital technologies are being introduced into children’s learning environments and they experience virtual materialities to a greater extent now ever before. This study aimed to uncover how young children make sense of the world through explorative touch interactions with physical and virtual materialities. Children’s sense-making was studied through an explorative inquiry that was supported by video documentation. This article discusses how the combination of materials, digital technologies and experiences of different materialities offers new opportunities for explorative interaction, transforming and shaping children’s experience of the world through joint sensemaking. It also identifies how children’s past experience of material touch is important for them in their process of grasping virtual materiality.&#x0D; Keywords:sense-making, touch interaction, virtual materiality, arts and crafts education, embodied cognition
A Survey on Peripheral Blood Smear Analysis Using Deep Learning

Peripheral Blood Smear (PBS) analysis is a routine test carried out in specialized medical laboratories by specialists to assess some aspects of health status that are measured and assessed through blood. PBS analysis is prone to human errors and the usage of computer-based analysis can greatly enhance this process in terms of accuracy and cost. Despite the challenges, Deep Learning neural networks have shown impressive performance in this context. In this study the recent contributions are summarized along with the main challenges and future directions in this context.
INVITED: New Directions in Distributed Deep Learning: Bringing the Network at Forefront of IoT Design

In this paper, we first highlight three major challenges to large-scale adoption of deep learning at the edge: (i) Hardware-constrained IoT devices, (ii) Data security and privacy in the IoT era, and (iii) Lack of network-aware deep learning algorithms for distributed inference across multiple IoT devices. We then provide a unified view targeting three research directions that naturally emerge from the above challenges: (1) Federated learning for training deep networks, (2) Data-independent deployment of learning algorithms, and (3) Communication-aware distributed inference. We believe that the above research directions need a network-centric approach to enable the edge intelligence and, therefore, fully exploit the true potential of IoT.
SVM based column-level approach for crosswalk detection in low-resolution images

Development of automated assistance systems for the visually impaired is still relatively uncommon research topic. However, the use of the cameras and image processing is widely spread and can be a good source of useful information for the visually impaired user. In this work, we are considering usage of SVM (Support Vector Machine) for detecting the crosswalk region in low-resolution images. The proposed approach uses SVM to make decisions on column level thereby providing the horizontal position of the crosswalk in image. Such information can potentially help the visually impaired person to cross the road safely. Preliminary testing was conducted and satisfying results were obtained especially in terms of performance.
TUNING OUT HATE SPEECH ON REDDIT: AUTOMATING MODERATION AND DETECTING TOXICITY IN THE MANOSPHERE

Over the past two years social media platforms have been struggling to moderate at scale. At the same time, they have come under fire for failing to mitigate the risks of perceived ‘toxic’ content or behaviour on their platforms. In effort to better cope with content moderation, to combat hate speech, ‘dangerous organisations’ and other bad actors present on platforms, discussion has turned to the role that automated machine-learning (ML) tools might play. This paper contributes to thinking about the role and suitability of ML for content moderation on community platforms such as Reddit and Facebook. In particular, it looks at how ML tools operate (or fail to operate) effectively at the intersection between online sentiment within communities and social and platform expectations of acceptable discourse. Through an examination of the r/MGTOW subreddit we problematise current understandings of the notion of ‘tox¬icity’ as applied to cultural or social sub-communities online and explain how this interacts with Google’s Perspective tool.
Impact of Website Qualities on E-satisfaction among Consumers on Saudi Online Retailers

Impact of Website Qualities on E-satisfaction amongConsumers on Saudi Online Retailers 1. Introduction E-business has progressively turn out to be a vital aspect of a corporate strategy (Davis & Comeau, 2020).Internet and information technology have made unbelievable aids to business revolution (Smith & Friedman, 2019).The indications exposed that the Internet becomes an important driver of change in numerous businesses in the former years (Soto-Acosta, Popa, & Palacios-Marqués, 2016).The online businesses deliver more contented, quicker, and wellorganized professional contract in comparison with other traditional business.One business that has been dramatically affected by the Internet is the retail industry (Bi, Davison, & Smyrnios, 2017).The Internet creates a brand-new relationship with the sellers and provides enormous opportunities for cost reduction and boost potential sales from the retail sector.It helps to customers, looking for information about products information (Alos-Simo, Verdu-Jover, & Gomez-Gras, 2017), and also compare different prices easily (Shulga et al., 2016).Customer Satisfaction has been essential in marketing due to its influence on customer retention and behavioural intention (Phuong & Dai Trang, 2018).Pandey, (2016) defined customer satisfaction as a psychological condition resulting when the emotion surrounding disconfirmed expectations is tight with the consumer's prior feelings about the consumption experience.Additionally, customer satisfaction is a sign as a generator of repeated purchasing behaviour reduces customer complaints and makes obstacles to changing to others.The other factor in a company is the quality of the site.Quality of the website helps firms acquire a loyal customer and achieve a competitive advantage (Thomas, 2016).The Importance of website quality has researched in many types of research and from other viewpoints.For example Alos-Simo et al., (2017)researched hotel website and reveal a set of features like hotel facilities, reservation info, contact info, site management are the most important in developing successful hotel websites.Additionally, the research by Ho et al., (2017) signalled information quality, security, site performance, customer relationships, responsiveness are the vital factors for an online shopping site.Those factors have a robust predictive capability concerning online customer satisfaction and loyalty (Chatzoglou & Chatzoudes, 2016).Well-designed website influences the customers' satisfaction, consumers' attitudes and expectations and motivates them to continue browsing the site.The thoughtful layout which carefully contained the customers' requirements and desires make the customers also satisfy.Satisfied customers have a substantial impact on customer loyalty (Shulga et al., 2016).