Reviewerly/exHarmony huggingface dataset:
EXTEND Project: Systematic Approach to Curricula Development

Abstract EXTEND project is aimed at modernizing approaches to teaching engineering disciplines in Russia and Tajikistan, increasing quality of education and possibilities of employment for young engineers, students’ motivation and making engineering education attractive. Modern universities carry out not only professional training of students, but also prepare competitively capable personnel who can survive and thrive in modern free market relations, accompanied by a variety of forms of ownership and competition. International educational projects are relevant as they supply universities with international experience and job possibilities. Therefore, foreign languages competence is of key importance for engineers of future generation, and is a means of forming professional, communicative, linguistic and cultural competences. Success of teaching foreign languages depends not only on teachers’ skills, but on carefully selected and elaborated didactic materials as well. EXTEND project team exchange ideas and experience that result in fruitful discussion of issues concerning elaborating new courses to improve engineering education. International teams, which include EXTEND project participants from European, Russian and Tajik higher educational institutions will carry out the task. Project EXTEND is an open kind of consortium, which implies that members from partner countries subordinate to its leader and share joint responsibility for commitments of the consortium.
Coding on Country

We describe preliminary explorations with a format to engage young indigenous students from remote communities in design and making. Digital technologies are often seen, not without merit, as reflecting and embodying cultural values that are at odds with indigenous ways of learning. To overcome this we organised a 'Coding on Country' workshop. The workshop took place outdoors, at a culturally significant location, was embedded within cultural practices, as well as other mundane activities, and drew participation from young people as well as from Elders. Key insights are that activities that take place on country are also intrinsically about country and culture. A site of cultural significance can, better than a classroom, draw together younger and older community members, and offer a social centre which people can gravitate around while engaging in different activities. This contributes to reconciling technology and tradition, and offers opportunities to participate on one's own terms.
Human body posture recognition algorithm for still images

Aiming at the low accuracy and poor robustness of the current algorithm based on manual features, this study proposed a posture recognition method combining joint point information with convolutional neural network. The deformable convolution is used in the proposed method to improve the stacked hourglass model, so that it can extract the position of the human joint point accurately. At the same time, the convolutional neural network structure is designed to analyse the position information and confidence of the joint point autonomously, and extract the intrinsic link of the joint point of the human body. Finally, the softmax classifier is used to determine the pose category. Experimental verification has been carried out on the Willow data set. Moreover, the recognition accuracy demonstrates the effectiveness and superiority of the improved method.
Towards the development of the Bengali language corpus from public Facebook pages for hate speech research

Online abusive or hateful speech detection in different languages on Social Networking Sites (SNS) have drawn the attention of researchers recently. Hateful comments in public Facebook pages ignite social mishaps in Bangladesh. In this paper, we have discussed the development and annotation of the corpus of hateful speech in the Bengali language on public Facebook pages. We have classified hateful comments into six major classes based on the social aspects of Bangladesh. The corpus (4753 comments) is the maiden contribution as a publicly available data set that can be enhanced and utilized for future hate speech research in SNS.
A Survey for an Automatic Transliteration of Arabic Handwritten Script

This survey is conducted because a large quantity of handwritten Arabic documents are waiting for their computerization since they are very rich and useful for several purposes. The manual computerization of such documents is very expensive in time and cost. Several researcher in Arabic optical character recognition attempted to developed approaches and techniques for the automatic computerization of such documents, unfortunately and despite the diversity of these approaches and techniques the problem remind unsolved yet. Because of the difficulty of the morphological structure of the Arabic written. In this survey, we study and compare in depth some promising approaches and techniques in order to select some complementarity techniques or approaches to exploit the complementarity of these approaches, we present an OCR and Automatic Capitalization of OCR technology for Arabic character recognition and build them as web services for improving the recognition rate through their cooperation. In addition, our approach offers a number of benefits: in the first hand, the recognition of Off-line printed Arabic text using Hidden Markov Models classifiers showed an improvement in terms of recognition rate 95.6%. In the second hand, building these system in the form of web services helped us to benefit from the cooperation methods that have been selected.
Legislation-driven development of a Gift Aid system using Event-B

Abstract This work presents our approach to formally model the Swiftaid system design, a digital platform that enables donors to automatically add Gift Aid to donations made via card payments. Following principles of Behaviour-Driven Development, we use Gherkin to capture requirements specified in legislation, specifically the UK Charity (Gift Aid Declarations) Regulations 2016. The Gherkin scenarios provide a basis for subsequent formal modelling and analysis using Event-B, Rodin and ProB. Interactive model simulations assist communication between domain experts, software architects and other stakeholders during requirements capture and system design, enabling the emergent system behaviour to be validated. Our approach was employed within the development of the real Swiftaid product, launched by Streeva in February 2019. Our analysis helped conclude that there was not a strong enough business case for one of the features, whichwas shown to provide nominal user convenience at the expense of increased complexity. This work provides a case study in allying formal and agile software development to enable rapid development of robust software.
Fireworks Algorithm for Frequency Assignment Problem

In this paper, a new approach to resolve the problem of frequency assignment problem is proposed. (FAP) is well known in the NP-complete problems and can be modeled as an optimization problem, which the objective is to minimize the costs due to interference generated by a solution. The fireworks algorithm (FWA) is proposed for solving FAP. FWA is a recently developed swarm intelligence algorithm. FWA is a meta-heuristic method and has a good convergence property and can always find the global optimal solutions.
DUST Initializr — Graph-based platform for designing modules and applications in the revised DUST framework

The DUST framework is a middleware platform developed to create software components that are distributable across heterogeneous networks. The development and maintenance of a component-based system can become a challenge. In this paper, we present a web-based tool that allows engineers to visually draw a graph to model an application in DUST. This graph is used by the back-end template generators to create code templates and associated configuration files in order to focus on the business logic. The latest version of the tool is compatible with the revised DUST version. It generates fully functional message objects and builder classes for transparent communication over DUST channels. Engineers are now able to manage and update DUST application through a GUI interface instead of error-prone coding and debugging.
Improving Batch Normalization with Skewness Reduction for Deep Neural Networks

Batch Normalization (BN) is a well-known technique used in training deep neural networks. The main idea behind batch normalization is to normalize the features of the layers (i.e., transforming them to have a mean equal to zero and a variance equal to one). Such a procedure encourages the optimization landscape of the loss function to be smoother, and improves the learning of the networks for both speed and performance. In this paper, we demonstrate that the performance of the network can be improved, if the distributions of the features of the output in the same layer are similar. As normalizing based on mean and variance does not necessarily make the features to have the same distribution, we propose a new normalization scheme: Batch Normalization with Skewness Reduction (BNSR). Comparing with other normalization approaches, BNSR transforms not just only the mean and variance, but also the skewness of the data. By tackling this property of a distribution, we are able to make the output distributions of the layers to be further similar. The nonlinearity of BNSR may further improve the expressiveness of the underlying network. Comparisons with other normalization schemes are tested on the CIFAR-100 and ImageNet datasets. Experimental results show that the proposed approach can outperform other state-of-the-arts that are not equipped with BNSR.
Design and Implementation of Fall Detection System Based on Video

To improve the accuracy and stability, a doublealgorithm fall detection system is designed. OpenPose and threshold determination are used as algorithm #1 to detect falls, while two neural networks splicing are used independently as algorithm #2 for cross-check. Compared to OpenPose or 3DCNN model alone, the result shows higher F1 and more accurate and pragmatically valuable. A real-time fall detection system is designed based on the study of algorithm #1 and #2. The history data analysis module is added to store and analyze the keyframes of falls, the images of which would be desensitized to protect users' privacy. A valuable reference model is hereby provided for future products in the potential market.
Giving Form to Temporality: Extending Design Practices and Methodologies for 'Slow' Interaction

With an aim to extend and enlarge the slow technology concept, this overview describes my dissertation goal of exploring the diversity of temporal design within HCI. My motivation started from the real-world need to better support people to live with their vast and still growing digital possessions. I adopt Research through Design methodology and plan to propose two quality design cases and following empirical studies in order to understand how 'slow' everyday technologies should be to provide alternative thinking in temporality and curious interactive experience with digital possessions. Building on my emerging body of published research, the research outcome will be anticipated to support the creation of methodological practice-based insights and to provoke different perspectives on designing future technologies as contributions to HCI communities.
A panorama of applications of blockchain technology to energy

This chapter provides a panorama of potential applications of the blockchain technology to the energy sector. The chapter provides an introduction to the blockchain technology covering the main technological characteristics and providing some context to what is a rapidly emerging technology. Some limitations of the technology are also discussed along with key issues relating to the regulation of the technology. The chapter then highlights how the blockchain technology could impact the energy sector in five distinct subsectors, these being energy trading, environmental attribute management, electric mobility, and financing considerations. The chapter then concludes by providing an up-to-date list of all nearly 150 companies working on the blockchain in energy space.
Inventory and Order System Development at PT.X

Abstract PT X is Indonesian cutting tools importer, which sells various kinds of cutting tools, such as Carbide End Mill and Reamer. This company is facing two problems. The first is inefficient order process. The second problem is inefficient inventory update, which takes about eight minutes. Logistic Manager double checks the product quantity by counting physical product, to make sure that the quantity is updated. This process affects both order and procurement process. The objective of this study is to propose a new system, which will be able to perform simpler business process and reduce completion time. Modified Waterfall Approach of System Development Life Cycle (SDLC) was chosen, as it is suitable for well – understood processes, where doing overlapping phases is permitted. This study develops UML Diagrams, also medium and high-fidelity prototypes. User Interface Testing, (consists of learnability, error, memorability, and satisfaction measurement) was done to examine system performance. Result shows that the proposed system performs better than current system. The completion time of create order is reduced 27.51% to 3.11 minutes, availability check is reduced 75.99% to 2.3 minutes, incoming inventory update is reduced 99.67% to 7.69 seconds, and outgoing inventory update is reduced 94.95% to 12.46 seconds.
So You Think You Can't Draw? A Hands-on Introductory Course on Sketching in HCI Techniques

Hand-drawn sketching is a practice as old as our ancestors. From cave painting to picture-books, we have explored the world with our visual senses. Within Human-Computer Interaction, sketches can be used to document, ideate, and describe concepts between researcher, user, or client. Attendees will leave the course with the confidence to engage actively with sketching on an everyday basis in their research practice.
Sparse Binary Optimization for Text Classification via Frank Wolfe Algorithm

Text classification is a fundamental problem and has attracted significant research attention, especially in large scale search with high dimensions. We propose a novel framework for text classification, combining with an efficient sparse binary optimization algorithm called Frank Wolfe based optimization algorithm. Specifically, we project the input data into a higher-dimensional space while ensuring the sparsity of the data in the output space, and the objective function is preserving pairwise similarities between the input sample. Experimental results on benchmark datasets demonstrate the effectiveness of the proposed framework with the optimization algorithm.
MobileAid: A Fast and Effective Cognitive Aid System on Mobile Devices

Elderly people generally suffer from diseases with cognitive decline, which seriously affect their daily lives. To assist these patients with cognitive decline, numerous cognitive aid devices have been researched and designed. Although these devices can achieve the aid tasks, they have to face two difficulties: low accuracy and long latency. In this paper, we present MobileAid, an aid system implemented on the mobile device, which assists cognitive decline patients to recognize objects. The key idea of this system is a two-step lightweight neural network design for the target recognition: context recognition and object recognition, and this design achieves low time delay. Considering the great success of convolutional neural networks in object recognition, we design a lightweight convolutional neural network by the combination of pooling layers and activation functions selection, which achieves high accuracy. Furthermore, we apply the depthwise separable convolution to reduce the resource consumption for deploying the system on mobile devices. The results of the extensive experiments we have conducted show that MobileAid can achieve 95% high accuracy and 90ms low time delay with few resource consumption.
Feature Selection Methods in Sentiment Analysis

The development of digital tecnnologies nowadays assists people by suggesting opinion, choices, preferences and feelings. This opinion is useful for company's engagement to make certain analysis to know their potential users and personalized their need. However, the information needs extraction to make further analysis. Thus, sentiment analysis is used to extract opinion and others and transform it into meaningful data. During the process of analysis, feature selection method is required to select a subset which consists of relevant features to construct a predictive model. This process requires some conditions during the selection of feature subset. The required conditions for feature selection are that the selected feature subset must be small and relevant for a high dimensional dataset which considers the presence of noise plus there are no redundant features. However, some of the feature selection methods unable to fulfill all conditions. In this research, 40 papers were collected, classified and reviewed. We discussed on the feature selection methods in sentiment analysis based on its level of analysis and make comparison between these methods to know its limitation and advantages. The comparison made between methods are based on its accuracy and CPU performance. Finally, suggest the best/benchmark method for feature selection. The findings obtained from this research shows that hybrid methods obtain the best accuracy and CPU performance compared to the other methods.
Prostate TRUS Image Segmentation Without Shape Prior

Due to the low Signal Noise Ratio (SNR) in prostate TRUS images and the missing boundaries in shadow areas caused by calcifications or hyperdense prostate tissues, the automatic TRUS image segmentation has been a rather difficult task. To address the segmentation problem, many researchers provide many edge detection operators specific to TRUS images and especially shape prior to the missing boundaries. In this paper, we propose a novel TRUS segmentation method which completely discards the shape prior. Our method consists of two steps, firstly, we can get the rough segmented curve through the revised prostate boundary feature operator used by weighted normal vector profile which can detect the boundary points more precisely than the conventional normal vector profile. Secondly, we can result the final segmentation curve through removing the abnormal points over the rough curve by image neighbouring relativity, which make the shape prior not required. Meanwhile, our segmentation method has no iterative process which widely used by many TRUS image segmentation algorithm. Promising experimental results in real prostate TRUS images show the robustness and low time-consumption of this method.
Exploring traditional handicraft learning mode using WebAR technology

The purpose of paper is to improve the limitations of current augmented reality technology in traditional handicraft learning. We propose a traditional handcraft mobile learning mode based on WebAR technology. Firstly, we analyzed that the current situation of traditional handicraft learning based on augmented reality technology and summarized the characteristics and application advantages of Web AR. Secondly, we develop a framework of traditional handcraft mobile learning system based on Web AR technology in traditional handicraft learning. Finally developed a mobile interactive prototype with Cantonese Porcelain as a practical case for the user interaction experience test. Interactive prototype with Cantonese Porcelain as a practical case for the user interaction experience test. The results show that the traditional handicraft learning model based on WebAR is more effective than the application limitations of traditional augmented reality devices. This contribution is highly relevant for users that aim for learning and creativity in different contexts that bring to new forms of traditional handicraft learning.
Development and Validation of Virtual Reality Application in Mining Education

Productivity, safety and its improvement is also an integral part of a good mining operation. In recent times, due to constraints on time and cost, it has become increasingly harder to conduct training and safety inductions at mine sites. For the purpose of overcoming these limitations, the use of virtual reality (VR) is proposed for mining education and training. VR has already been introduced in the education and training of miners overseas, and quantitative studies on the effects of using VR for miner's education and training have been made. However, Japan has only one such application of VR for mining education, namely, “Virtual Mining Practice System” which was produced by Akita University, and there are relatively few cases where VR has been introduced in the Japanese mining industry. Furthermore, there has been no quantitative study to date on the effects of education using VR for mining education. Therefore, the objective of this study is to investigate the effects of a class that utilizes a VR application developed for mining education (Mining VR), as well as evaluate its learning outcomes. In this study, a method called randomized controlled trial (RCT) is used for evaluating Mining VR's effectiveness. Study participants are divided two groups randomly where one class makes use of Mining VR and another class using other non-VR material. After the classes are completed, a test is conducted and the average results of each group are compared by T-test. The results of this experiment showed that there were no statistically significant differences in skill of “understanding” and “knowledge retention” comparing two groups. On the other hand, results suggested that Mining VR has improved students'“ motivation” for class when using Mining VR.
Designing Mobile Based User Interface for Promotion of Lakeur Craft Art Application at Balaputra Dewa State Museum

Abstract Applications that use mobile devices are growing rapidly in accordance with the needs and fashion of a society that continues to grow. Many conveniences and benefits are felt if an agency or institution has a mobile-based application, among others is the ease of obtaining information and the latest data from the agency or institution. Museum Balaputra Dewa Palembang is one of the museums that must develop themselves to follow the development of mobile technology that is growing rapidly, which must be able to present information that is more easily accessible by the community not only in Palembang but throughout Indonesia. The User Interface for the promotion of lakeur arts and crafts at the Balaputra Dewa Museum was created using the Task Centered System Design (TCSD) method, which has several stages, namely creating planning flow, storyboard analysis, conceptual analysis of models, paper prototyping analysis and mock-up design. This stage is the basis of a user interface design in an application.
Data Association with Gaussian Processes

The data association problem is concerned with separating data coming from different generating processes, for example when data comes from different data sources, contain significant noise, or exhibit multimodality. We present a fully Bayesian approach to this problem. Our model is capable of simultaneously solving the data association problem and the induced supervised learning problem. Underpinning our approach is the use of Gaussian process priors to encode the structure of both the data and the data associations. We present an efficient learning scheme based on doubly stochastic variational inference and discuss how it can be applied to deep Gaussian process priors.