Reviewerly/exHarmony huggingface dataset:
InnoDev Workshop: A One Day Introduction to Combining Design Thinking, Lean Startup and Agile Software Development

Software innovation is a core differential factor for organizational survival to deliver faster, better, and more cost effective software solutions. Accordingly, different approaches to innovations have been developed, e.g. Design Thinking from the field of design, Lean Startup from the field of entrepreneurship, and agile development approaches from the field of software engineering. There are increased research efforts to investigate how to combine these approaches. However there is a lack of knowledge on how to train professionals in applying and integrating these approaches. This article describes a one-day training workshop based on the InnoDev model, developed in former research. InnoDev combines Design Thinking, Scrum and Lean Startup and is targeted at software developers, designers, and project managers in companies ranging from startups and SMEs to multinationals. Specifically, this article describes the workshop format and reports on the participants perceptions of the combined approach and the workshop experience. Overall participants had a positive experience in our workshop, became aware of new tools and techniques for software development, and perceived the combination of three innovation approaches as applicable and beneficial within their companies.
Robust and On-the-Fly Dataset Denoising for Image Classification

Memorization in over-parameterized neural networks could severely hurt generalization in the presence of mislabeled examples. However, mislabeled examples are hard to avoid in extremely large datasets collected with weak supervision. We address this problem by reasoning counterfactually about the loss distribution of examples with uniform random labels had they were trained with the real examples, and use this information to remove noisy examples from the training set. First, we observe that examples with uniform random labels have higher losses when trained with stochastic gradient descent under large learning rates. Then, we propose to model the loss distribution of the counterfactual examples using only the network parameters, which is able to model such examples with remarkable success. Finally, we propose to remove examples whose loss exceeds a certain quantile of the modeled loss distribution. This leads to On-the-fly Data Denoising (ODD), a simple yet effective algorithm that is robust to mislabeled examples, while introducing almost zero computational overhead compared to standard training. ODD is able to achieve state-of-the-art results on a wide range of datasets including real-world ones such as WebVision and Clothing1M.
Modeling Wall Tracer Robot Motion Based on Fuzzy Logic Control

In this modern times, robot technology is rapidly having a great progress day by day, especially in robot wall tracer. Robot wall tracer was very useful to lighten human jobs in industry, manufacturing, and infrastructure. Some problems that arises in building the wall tracer robot is how to make a navigation system on the robot so that it always remains in the set point position. Previous study has been done to solve this problem using various methods and approaches such as implemented some intelligence program that used to navigate the robot wall tracer. In this study, we try to design and build a robot wall tracer using an Arduino MEGA microcontroller as the brain for control robot and the HC-SR04 ultrasonic sensor based on fuzzy logic to control the position and DC motor speed. In this study, testing was carried out in terms of hardware and software, as well as overall system testing. Based on hardware testing obtained PWM left motor 110 and PWM right motor 107. Meanwhile, for software testing, it is found that the fuzzy logic has been successfully implemented in the robot. The results of the calculation of fuzzy logic on the robot are compared with the calculation of simulation results and manual calculations. In addition, a performance comparison test of the robot system was conducted, and it was found that robots equipped with fuzzy logic were 2.3 seconds faster than robots without fuzzy logic.
Asynchronous Collaboration: Bridging the Cognitive Distance in Global Software Development Projects

Research problem: The role that physical, temporal, and cultural distances play in global software development projects has been well researched. Culturally diverse teams separated by physical distances across multiple time zones face significant challenges in collaborating effectively with each other. This article examines a fourth dimension-cognitive distance-that relates to the problem-solving style of teams that can also have an impact on their ability to collaborate successfully. Research questions: 1. Does cognitive distance affect communication among global software development teams collaborating with each other? 2. How does cognitive distance affect the sentiment/emotion of global software development teams collaborating with each other? Literature review: Prior research shows that collaboration among teams on global software development projects is impacted by practices to manage collaboration; appropriate use of collaboration technologies; collaboration readiness that relates to individual characteristics such as personality traits, motivation, and trust; and shared understanding in group problem-solving. While shared understanding has looked at the effectiveness of the use of common language and knowledge sharing, it has not examined how differences in problem-solving styles of geographically dispersed teams impact their ability to collaborate successfully. Methodology: We examined project artifacts and email communication among geographically dispersed teams within a global software development project. From the project artifacts, we examined tasks allocated to different teams. From the emails, we established the communication network and volume of communication, and performed a sentiment analysis on email content. This analysis allowed us to observe not only the quality of communication among the teams but also the sentiment/emotion that reflected how well they were working together. Results and discussion: Managing teams that vastly differ in problem-solving styles and tasks requires that project managers be aware of these differences and introduce liaisons that reach across the teams to help bridge the cognitive divide.
Optimization of Fuzzy Logic Controllers with Distributed Bio-Inspired Algorithms

Optimization problems are present in many fields of science, engineering, and industry. In most cases, optimization problems involve a large number of complex decision variables. Often, the application of fuzzy systems to traditional control strategies requires the use of optimization techniques to define the parameters of the membership functions capturing the designer’s experience. Currently, bioinspired algorithms are one of the most widely used methods for fuzzy system optimization. In this paper, we review the literature on the application of different bioinspired techniques such as Genetic Algorithms, Ant Colony Optimization, Particle Swarm Optimization, Galaxy Swarm Optimization, Harmony Search, Firefly, Differential Evolution, to optimize the parameters of the fuzzy controllers. We found particular trends in the current literature, with many methods using dynamic parameter adaptation, hybrids, distributed algorithms, and type-2 fuzzy logic.
Federated Matrix Factorization: Algorithm Design and Application to Data Clustering

Recent demands on data privacy have called for federated learning (FL) as a new distributed learning paradigm in massive and heterogeneous networks. Although many FL algorithms have been proposed, few of them have considered the matrix factorization (MF) model, which is known to have a vast number of signal processing and machine learning applications. Since the MF problem involves two blocks of variables and the variables are usually subject to constraints related to specific solution structure, it requires new FL algorithm designs to achieve communication-efficient MF in heterogeneous data networks. In this paper, we address the challenge by proposing two new federated MF (FedMF) algorithms, namely, FedMAvg and FedMGS, based on the model averaging and gradient sharing principles, respectively. Both FedMAvg and FedMGS adopt multiple steps of local updates per communication round to speed up convergence, and allow only a randomly sampled subset of clients to communicate with the server for reducing the communication cost. Convergence properties for the two algorithms are thoroughly analyzed, which delineate the impacts of heterogeneous data distribution, local update number, and partial client communication on the algorithm performance, and guide the design of proposed algorithms. By focusing on a data clustering task, extensive experiment results are presented to examine the practical performance of proposed algorithms, as well as demonstrating their efficacy over the existing distributed clustering algorithms.
AnAbEL: towards empowering people living with dementia in ambient assisted living

Ambient assisted living aims to support the well-being of people with special needs by offering assistive solutions. Systems focused on dementia increase the autonomy of people living with dementia by monitoring their activities. Topics such as activity recognition and specific solutions such as reminders and tracking users by Global Positioning System offer great advances in user safety and help them preserve a healthier lifestyle. However, these solutions are often addressed to secondary parties, providing them activity logs or alerts, but excluding the main user, the person living with dementia. Although the primary users are taken into consideration at some design stages using user-centred design frameworks, the final products tend to not fully address user needs. This paper presents an ambient intelligent system aimed at reducing this limitation by providing reminders and advice to the person living with dementia in the first instance. The system still involves caregivers if unusual or unhealthy behaviour continues. The solution is deployed in order to be validated by professionals from London city boroughs who work in housing and dementia related services, with an emphasis on enhancing healthy lifestyles by empowering the user in the early stages of dementia with autonomy. Through continued activity monitoring in real-time, the system can provide reminders and warnings to users to keep healthy routines. Continuous monitoring provides user behaviour tracking, and the context-aware logic used involves caregivers through alerts when necessary to ensure user safety. This article describes the process followed in developing the system, and covers previous concerns and practical feedback from health professionals over the deployment of the system in a real environment. Our approach also includes a novel indoor localization system to distinguish users and allows a more specific delivery of services in multi-occupancy scenarios.
Responsible, Automated Data Gathering for Timely Citizen Dashboard Provision During a Global Pandemic (COVID-19)

Creating a public understanding of the dynamics of a pandemic, such as COVID-19, is vital for introducing restrictive regulations. Gathering diverse data responsibly and sharing it with experts and citizens in a timely manner is challenging. This article reviews methodologies of COVID-19 dashboard design and discusses both technical and non-technical challenges associated. Advice and lessons learned from building a citizen-focused, automated county-precision dashboard for Germany are shared. Within four months, the web-based tool had 5 million unique visitors and 70 million sessions. Three developers set up the basic version in less than one week. Early on, data was screen scraped. An iterative process improved timeliness by adding more fine-grained data sources. A collaborative online table editor enabled near real-time corrections. Alerting was setup for errors, and statistics apply for sanity checking. Static site generation and a content delivery network help to serve large user loads in a timely manner. The flexible design allowed to iteratively integrate more complex statistics based on expert knowledge built on top of the collected data and secondary data sources such as ICU beds and citizen movement.
Mobile health intervention and COVID-19 pandemic outbreak: insights from Indian context

Purpose This paper discusses the need for government and healthcare organization to implement mobile phone-based solutions for healthcare during the Coronavirus (Covid-19) pandemic. It also highlights the challenges and/or barriers to the rapid introduction, implementation and management of these and other innovative solutions to health service delivery during the current situation Design/methodology/approach The data include both qualitative and quantitative, collected from the primary interview-based case study and questionnaire survey. It also uses insights from the general populations, healthcare professionals and health information technology developers to understand the role of a mobile health intervention in the COVID-19 pandemic outbreak. Findings Healthcare professionals and health information technology developers are confident that the use of mobile health technology and applications has the ability to assist in monitoring and controlling the COVID-19 outbreak. The key advantages of using mobile phone technology are: increased awareness, improved assistance in tracking and testing casualties, improved assistance in seeking and scheduling health information and medical appointments, increased social distancing, improved overall productivity and quality of life. However, data demonstrated that lack of awareness and accessibility or unwillingness to use the technology, complex healthcare needs, application infrastructure, policies and a dearth of training and support are all barriers to successful implementation of this useful tool. Practical implications This research has the potential to make a significant impact on government and healthcare policy through presenting a coherent argument for the importance of designing and deploying mobile health technology and applications for the general population. Originality/value prior literature in this domain is inadequate in explaining the importance of mobile phone-based healthcare solutions for health service and during serious disease outbreaks and, in particular, within the Indian context. The findings of this study can be used by government and healthcare organizations to improve health governance during the current global pandemic.
Newspaper Navigator: Open Faceted Search for 1.5 Million Images

Despite the marked improvements in image classification and recognition tasks over the past decade, affordances and interfaces for searching images have remained largely unchanged on the web. In this demo, we present Newspaper Navigator, an open faceted search system for 1.5 million historic newspaper photographs. In contrast to standard faceted search, which requires facets to be pre-selected and applied to images, Newspaper Navigator empowers users to specify their own facets in an open-domain fashion during the search process by selecting relevant examples and iteratively training a machine learner. With Newspaper Navigator, users can quickly sort 1.5 million images according to dynamically-specified facets such as "baseball player'' and "oval-shaped portrait.'' Newspaper Navigator also drives facet exploration by suggesting related keyword search queries for a user to perform. Our demo walks through examples of searching with Newspaper Navigator and highlights the facet learning and exploration affordances.
Now Wash Your Hands: Understanding Food Legislation Compliance in a Virtual Reality Restaurant Kitchen

The Corrupt Kitchen is a room-scale virtual-reality game in which players act as a chef servicing a queue of customers. Tasked with making burgers, players must prepare the food while ensuring it is safe to eat, engaging explicitly and implicitly with challenges related to regulatory compliance and derived from UK legislation, but also efficient and ethical decision making; washing hands, placing rat traps, hiring appropriate help, time saving and money making. Interviewing nineteen players with professional involvement in food preparation reveals a diversity of perceived alignment with participants' everyday real-world practice that ranges from rules to be gamed to serious concerns. We contribute an examination of how the game, combined with a study protocol that further prompted debriefing and reflection, demonstrates opportunities for training, reflection and engagement with the subject matter. We consider how fidelity and immersion allow comparisons between gameplay and real world compliance.
Age Classification Using Motif and Statistical Features Derived On Gradient Facial Images

Background: Age estimation using face images has become increasingly significant in the recent years, due to diversity of potentially useful applications. Age group feature extraction, the local features, has received a great deal of attention. Objective: This paper derived a new age estimation operator called “Gradient Dual-Complete Motif Matrix (GD-CMM)” on the 3 x 3 neighborhood of gradient image. The GD-CMM divides the 3 x 3 neighborhood in to dual grids of size 2 x 2 each and on each 2 x 2 grid complete motif matrices are derived. Methods: The local features are extracted by using Motif Co-occurrence Matrix (MCM) and it is derived on 2 x 2 grid and the main disadvantage of this Motifs or Peano Scan Motifs (PSM) is they are static i.e. the initial position on a 2 x2 grid is fixed in deriving motifs, resulting with six different motifs. The advantage 3 x 3 neighborhood approaches over 2x 2 grids is the 3x3 grid identify the spatial relations among the pixels more precisely. The gradient images represent facial features more efficiently and human beings are more sensitive to gradient changes than original grey level intensities. Results: The proposed method is compared with other existing methods on FGNET, Google and scanned facial image databases. The experimental outcomes exhibited the superiority of proposed method than existing methods. Conclusion: On the GD-CMM, this paper derived co-occurrence features and machine learning classifiers are used for age group classification.
Digital Competence in Schools: A Bibliometric Study

This document presents the results of an analysis of scientific production on digital competence in the educational field from a scientometric perspective, by analysing 150 documents available in Scopus. It also includes a complementary content analysis. The findings show the rise of the subject area in the European sphere. Regarding content, the terminological diversity used and the educational levels studied stand out. The conclusion is that teachers need more training in the didactic use of technologies to improve the development of digital competence in the educational system.
Personalized Item Recommendation for Second-hand Trading Platform

With rising awareness of environment protection and recycling, second-hand trading platforms have attracted increasing attention in recent years. The interaction data on second-hand trading platforms, consisting of sufficient interactions per user but rare interactions per item, is different from what they are on traditional platforms. Therefore, building successful recommendation systems in the second-hand trading platforms requires balancing modeling items? and users? preference, and mitigating the adverse effects of the sparsity, which makes recommendation especially challenging. Accordingly, we proposed a method to simultaneously learn representations of items and users from coarse-grained and fine-grained features, and a multi-task learning strategy is designed to address the issue of data sparsity. Experiments conducted on a real-world second-hand trading platform dataset demonstrate the effectiveness of our proposed model.
A Novel Clothing Attribute Representation Network-Based Self-Attention Mechanism

As highly increasing of on-line fashions retail industry, automatic recognition and representation of clothing items have huge potentials.With the help of deep learning methods, many clothing attribute representation models have been proposed.However, these models are mainly suitable for coarse-grained classification which are not suitable for clothing attribute representation.To address such a problem, in this article, we propose a novel network structure named SAC, which is a combination of CNNs and Self-attention mechanism and can represent clothing attributes more fine-grained.Besides, we use Grad-CAM to visualize which part of the clothing attributes is more concerned by customers.Finally, a new labeled clothing dataset is introduced in this article, which is expected to be helpful to the researchers who are working in fashion domains for image representation.
ARCAR: On-Road Driving in Mixed Reality by Volvo Cars

ARCAR is a proof-of-concept headset-based mixed reality experience, for use by a driver in full control of a moving vehicle. The functional prototype has been implemented using a high end video pass-through mixed reality headset, and tested on roads (closed to external traffic) at conventional driving speeds. Our implementation embodies acceptable solutions to a number of current challenges that have been in the way of in-car XR applications, and enables a wealth of research. ARCAR is intended to serve as a research platform in the near future, enabling investigations on topics which include safety, design, perceived quality, and consumer applications.
Hypergraph network model for nested entity mention recognition

We propose a hypergraph network (HGN) model to recognize the nested entity mentions in texts. This model can learn the representations for the sequence structures of natural languages and the representations for the hypergraph structures of nested entity mentions. Mainstream methods recognize an entity mention by separately tagging the words or the gaps between words, which may complicate the problem and not be favorable for capturing the overall features of the mention. To solve these issues, the HGN model treats each entity mention as a whole and tags it with one label. We represent each sentence as a hypergraph, in which nodes represent words and hyperedges represent entity mentions. Thus, entity mention recognition (EMR) is transformed into a task of classifying the hyperedges. The HGN model firstly uses encoders to extract the features and learn a hypergraph representation, and then recognizes entity mentions by tagging every hyperedge. The experiments on three standard datasets demonstrate our model outperforms the previous models for nested EMR. We openly release the source code at https://github.com/nlplab-ie/HGN.
Bangla Compound Character Recognition using Support Vector Machine (SVM) on Advanced Feature Sets

A systematic approach for handwritten character detection and recognition using SVM has many significant applications. The proposed technique extracts features to classify the complex structural variations of Bangla compound characters. The three features used for classification are - Longest Run Feature (LRF) based on CG based Partitioning, Histogram of Oriented Gradients (HOG) Feature and Diagonal Feature. This enhanced, powerful combination of features result in a 180-length variable feature vector for each character, which is found to be adequate enough to uniquely represent and identify each character. Prior, Bangla handwritten character recognition problem has not been addressed with the proposed feature extraction techniques. The extracted feature vectors are used during the training phase for building a SVM classifier, testing phase for classifying characters which are then compared with their respective labels during evaluation. The results obtained show higher efficiency regarding classifier accuracy as compared to other existing approaches.