Reviewerly/exHarmony huggingface dataset:
UX Researchers: Framing Brazilian’s Perspectives

This paper explores the professional paradigms of integrating skills from Exact and Social Sciences, considering the phenomenon of datafication. It provides a comparative overview of UX workers’ educational and professional backgrounds in the ICT industry in Brazil. The critical research hypothesis is that agile methodologies, primarily from the Exact Sciences, quantify these professionals’ qualitative work. It applies a survey using a non-probability convenience sampling with professionals identified as UX Researcher on LinkedIn. The financial, e-commerce, and ICT services sectors are the ones that showed the highest adherence to the survey. Qualitative methods popularized in the discovery and validation stages of agile methods are primarily developed at short notice throughout the recruitment, planning, and analysis stages, underscoring the assumption of quantitative dominance. There is a clear dispersion about valuing time and empathy with agile methods among professionals with academic research experience. Cultural aspects and regional contexts continue to be methodological challenges for quantifying users’ experiences, even if they sound like qualitative demands.
The Prediction of E-Money Circulation: Backpropagation with Genetic Algorithm Adoption

Digital transformation forces the utilization of e-money during the economic transaction. Behind its advantages, e-money has been influenced by the inflation rate, thus accelerating the country’s money circulation. Moreover, the fragile Covid-19 economy triggers each country’s need to anticipate the circulation of e-money to deter future inflation. Therefore, this paper deployed the Backpropagation approach integrated with the Genetic Algorithm to forecast the dissemination of e-money in Indonesia by exploiting time-series Bank Indonesia (BI) data from January 2009 to December 2019. Here, 120 data with 12 variables are considered to thoroughly predict the Year 2020 circulation focusing on the previous 12 months. This study reveals that e-money circulation in Indonesia is increasing monthly in 2020. The testing result shows that the lowest mean square error (MSE) is found at 0.000035 for data training division at 90%:10%, learning rate parameter at 0.8, the combination of crossover probability and mutation at 0.4:0.6, and the total generation and population at 350 and 200, respectively. In a nutshell, Backpropagation with a Genetic Algorithm has been expected to a successful outcome for e-money circulation and provides large values compared with actual data and original BPNN.
A Design for Private Data Protection Combining with Data Perturbation and Data Reconstruction

With the rapid development of various hardware equipment and saving technology, multiple data with different types are uploaded to saving space. There are some private data can not be ignored. For provider, in order to use and deliver these private data to the third party, data anonymization, such as K-anonymity [1] should be applied to cover the explicit information. For receiver, there are still some way to transform these “fake” data to a new data set which obtain the same statistical properties with the original one while not exactly the same in detailed records.
Hybrid Intelligent System for Medical Diagnosis in Health Care

The autonomous city implies a global vision that incorporates artificial intelligence, deep learning, big data, decision-making, ICT and the Internet of Things (IoT) to promote sustainable development. The ageing issue is that researchers, companies and the government should devote efforts to developing smart health care, innovative technology and applications. For an extended period, conventional intelligence systems have played a critical role in health care. However, with the increased popularity and widespread use of these hybrid intelligent computer systems, there is a significant shift in the healthcare sector. Diagnosis and detection of various diseases using several techniques can be resolved by using these techniques. Different novel methods are applied to biomedical engineering to diagnose diseases, and new models are being studied and compared with the existing technologies. Hybrid intelligence systems can be implied in decision-making, remote monitoring, healthcare logistics, medical diagnosis, and modern information system. This success's fundamental cause seems to be derived by different intelligent computational mechanisms, such as genetic algorithms, evolutionary computation, convolutional neural network (CNN), long short-term memory (LSTM), autoencoders, deep generative models and deep belief networks. To solve complex problems, we need domain knowledge that comprises the methodologies that provide hybrid systems with complementary reasoning and empirical data. This chapter will focus on the need for a hybrid intelligent system in the healthcare industry and their medical diagnosis applicability.
Generalised Quantum Tree Search

This extended abstract reports on on-going research on quantum algorithmic approaches to the problem of generalised tree search that may exhibit effective quantum speedup, even in the presence of non-constant branching factors. Two strategies are briefly summarised and current work outlined.
A Connected Components Based Layout Analysis Approach for Educational Documents

Layout analysis, which aims to detect and categorize areas of interest on document images, is an increasingly important part in document image processing. Existing researches have conducted layout analysis on various documents, but none has been proposed for documents yielded from teaching, i.e. exam papers and workbooks, which are worth studying. In this paper, we propose a novel layout analysis system to achieve two tasks for workbook pages and exam papers respectively. On one hand, we segment text and non-text areas of workbook pages. On the other hand, we extract regions of interest on exam papers. Our system is based on connected component (CC) analysis, specifically, it extracts geometric features and spatial information of CCs to recognize page elements. We carried out experiments on images collected from real-world scenarios, and promising results confirmed the applicability and effectiveness of our system.
Legal and civil patriotic education in Russia university as a factor of sustainable development societies

The article analyzes the problem of forming patriotism and citizenship during the period of students ’ education in higher educational institutions. Educational work is considered as an integral part of the educational activity of a higher educational institution. The goals and objectives of legal and civil patriotic education, its role in the formation of a fullfledged citizen and patriot of their country are formulated. The article reveals the successful experience of individual educational institutions in organizing mass events of a civil-patriotic orientation among students, as well as the difficulties and problems in conducting them. Special attention is paid to the experience of the Kazan National Research Technological University.
Smoothed functional-based gradient algorithms for off-policy reinforcement learning: A non-asymptotic viewpoint

We propose two policy gradient algorithms for solving the problem of control in an off-policy reinforcement learning (RL) context. Both algorithms incorporate a smoothed functional (SF) based gradient estimation scheme. The first algorithm is a straightforward combination of importance sampling-based off-policy evaluation with SF-based gradient estimation. The second algorithm, inspired by the stochastic variance-reduced gradient (SVRG) algorithm, incorporates variance reduction in the update iteration. For both algorithms, we derive non-asymptotic bounds that establish convergence to an approximate stationary point. From these results, we infer that the first algorithm converges at a rate that is comparable to the well-known REINFORCE algorithm in an off-policy RL context, while the second algorithm exhibits an improved rate of convergence.
Research on the Interaction Method that Can Alleviate Cybersickness in Virtual Reality Games

Cybersickness is a symptom that is common among game players. Although each player has a different level of symptom, this feeling will occur during the long-term experience of video games. Studies have shown that cybersickness is caused by many factors, including display hardware, screen content, and human factors. With the development of VR game platforms over the years, cybersickness has become a high-priority in the virtual reality industry, and cybersickness has also been called VR sickness. There have been many different studies on how to alleviate VR sickness, such as adding a virtual nose model to the content, providing Dynamic Gaussian Blur, using Dynamic FOV, etc. These studies are focused on reducing VR sickness by improving the displayed content. Not like previous studies, in this paper, we looked forward to discussing the VR sickness that was caused by different interaction. In this study, the researchers designed several VR interactions based on different hardware. Then they organized user tests that asked testers to complete several goals in the same VR environment. After comparing multiple sets of test data, the researchers discussed the possibility of an interaction method that will reduce VR sickness and how it looked like. Through this work, we tried to give an insight into the current research findings of alleviating VR sickness and discussing the possibility of long-term VR interaction. In this context, this study aimed to explore a virtual reality interaction method that allows players to alleviate VR sickness during gaming.
The Influence of Different Drawing Tools on the Learning Motivation and Color Cognition of the Fourth Grade Students at the Elementary School

Applying scientific and technological products to the domain of education has gradually become a trend. In the area of art education, using digital tools such as tablets and stylus not only help students freely create their imagination, but strengthen their ability to understanding the colors of the real objects. Therefore, in order to understand how the different digital drawing tools influence on the color cognition and learning motivation of the fourth grade students at the elementary school, we asked the students using “interactive electronic drawing pen”, which can capture the colors of the object, “stylus” and “traditional drawing tool” to take the drawing task. Four-stage drawing experiment with 27 students from the fourth grade of elementary school were carried out, and the differences on the drawing outcome and color cognition of the students were compared for further discussion. The result shows that: 1) Comparing the results of “drawing behavior analysis” and “the pre-test and post-test of their color cognition”, we found that the students using “interactive electronic drawing pen” can improve their color cognition and depict objects with richer and more accurate colors than using the other drawing tools; 2) Through the analysis of their drawing behaviors, we found that the students frequently interacted with the drawing objects when using the “interactive electronic drawing pen”, showing that “interactive electronic drawing pen” indeed helps to increase the chances for them to observing and realizing real objects; 3) Analyzing the result on the interviews of the students’ learning motivation, 85% of the students consider that using the “interactive electronic drawing pen” to learn color is interesting; 63% of them believe that the “interactive electronic drawing pen” can trigger their curiosity on colors and enhance their learning motivation.
Where is the User in User-Centered Design?

If ISO [1] definitions are viewed through a “human” filter, the terms support only a system-side elimination. Is it more useful to develop solutions that enable people to achieve a goal, or to create well-being and positive experiences? In my thesis, I describe concrete examples from the ISO of how focus falls on the user. The main point is to resolve the question between usability and user experience (“Can the methods work together?”) and between the interests of project participants (“effective and efficient”) and the interests of users (“positive user experiences”). Because of the malleable and imprecise (nondeterministic) human influence factor, there is a need for an empirical investigation in the form of an evaluation metric [2]. I write about the international standard ISO-9241 (guidelines for human-computer interactions), contributions from human-centered design, design thinking, and psychological needs. The question is: Are psychological needs a tool that can bring humans more into focus, and if so, how does this manifest itself in the design process?
Examining the relationship between facilities, teaching quality and administration towards student satisfaction

Today, enhancing and providing the best learning experience and top-quality education are now being embedded into the strategic thrusts of higher learning institutions. Previous research also has contemplated to investigate the factors affecting student satisfaction. However, the focus towards nursing college in the country is still lacking. As Malaysia is now moving towards becoming a hub for medical tourism, the enhancement and improvement on the nursing college is deemed important. In this study, three factors were proposed to influence student satisfaction significantly. Based on 148 samples of respondents, the results indicated that, collectively, the level of satisfaction among students is good. The result of mean analysis also indicated that the respondents perceived teaching quality as the most important factor for their satisfaction, followed by administrative and facility. The results from Pearson Correlation also showed support for all three hypotheses proposed, in which facility, teaching quality and administrative were found to significantly influence student satisfaction. Implications of the study are also discussed.
Multi-task Learning Neural Networks for Comparative Elements Extraction

Comparative sentences are common in human languages. In online comments, a comparative sentence usually contains the subjective attitude or emotional tendency of a reviewer. Hence, comparative elements extraction (CEE) is valuable for opinion mining and sentiment analysis. Most of the existing CEE systems use rule-based or machine learning approaches that need to construct a rule base or spend a huge amount of effort on feature engineering. These approaches usually involve multiple steps, and the performance of each step relies on the accuracy of the previous step, risking error cascading oversteps. In this paper, we adopt a neural network approach to CEE, which supports end-to-end training and automatic learning of sentence representation. Furthermore, considering the high relevance of CEE and comparative sentences recognition (CSR), we propose a multi-task learning model to combine the two tasks, which can further improve the performance of CEE. Experiment results show that both our neural network approach and multi-task learning are effective for CEE.
On the Fairness of DCTCP and CUBIC in Cloud Data Center Networks

Data center networks (DCNs) hold a key role in information industry as the infrastructure for hosting cloud data services and related applications. The increasing number of customers using cloud services sets, however, new challenges for the efficient management of the DCN traffic handled by the transport protocols. TCP is adapted as the main transport control protocol in DCNs, but the standard congestion control algorithm it employs has been found to be very inefficient in providing a fair sharing of the available channel capacity among the competing DCN traffic flows. DCTCP and CUBIC are two promising TCP variants designed to resolve the fairness issues identified in these environments. In this paper, we evaluate the fairness properties of these two TCP variants in comparison to the conventional TCP Reno with the use of network simulator 3 (ns-3) in realistic DCN scenarios. Our results show that DTCP improves substantially the fairness properties of TCP, but it requires more buffer space for large number of flows; it also requires the intermediate switches to be Explicit Congestion Notification (ECN)-aware. CUBIC achieves very low queue occupancies, but it demonstrates low fairness when a large and a small set of flows compete at the output port of a bottleneck switch (TCP Outcast issue).
A Multilingual Lexicon-based Approach for Sentiment Analysis in Social and Cultural Information System Data

Sentiment analysis, also known as opinion mining, has become ubiquitous in our society, with applications in online searching, computer vision, image understanding, artificial intelligence and marketing communications. In this paper, is described an unsupervised automatic method of multilingual lexicon-based sentiment analysis algorithm, with a dictionary-based approach. The developed algorithm was tested in the sentiment analysis of users' publications on a digital tourism platform. The results obtained demonstrate the efficiency of the solution, which presents a high accuracy in the classification of publications in four different languages.
Detecting and Distinguishing Ophiolite Rocks via Multispectral Remote Sensing in Bi’r Umq, Southeast Al Madinah Al Munawarrah, Saudi Arabia

The objective of this work is to show the contribution of multispectral remote sensing data to detect and distinguish the ophiolite rocks of Bi'r Umq and their geologic setting, southeast of Al Madinah Al Munawarrah in Saudi Arabia.This work includes detailed fieldwork lab studies, as well as processing operations, were performed on The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER 2006(ASTER , 2007) ) and Satellite Pour L'Observation de la Terre SPOT 5 (2005) images of the study region.Among the processing techniques applied are band ratio, histogram stretching, the combination of spectral bands, image fusion.The techniques used permitted a clearly show that the ultramafic rocks are distinct from other rock units and contain important economic minerals.This research also illustrates the tectonic parameters and that the remains of the oceanic crust are exposed and juxtaposed to the rocks of the continental crust of the Arabian Shield.The results of the spatial data processing revealed a good positive concordance with the results of the field investigations.
A Cross-Domain Method for Generation of Constructive and Perturbative Heuristics

Hyper-heuristic frameworks, although intended to be cross-domain at the highest level, usually rely on a set of domain-specific low-level heuristics which exist below the domain-barrier and are manipulated by the hyper-heuristic itself. However, for some domains, the number of available heuristics can be very low, while for novel problems, no heuristics might exist at all. We address this issue by describing two general methods for the automated production of constructive and perturbative low-level heuristics. Grammatical evolution is used to evolve low-level heuristics that operate on an “intermediate” graph-based representation built over partial permutations. As the same grammar can be applied to multiple application domains, assuming they follow this representation, the grammar can be viewed as a cross-domain. The method is evaluated on two domains to indicate generality (the Travelling Salesman Problem and Multidimensional Knapsack Problem). Empirical results indicate that the approach can generate both constructive and perturbative heuristics that outperform well-known heuristic methods in a number of cases and are competitive with specialised methods for some instances.
Empirical Research as a Challenge in Day-to-Day Teaching During the Pandemic of 2020/21 - Practical Solutions

Due to the pandemic of 2020, many teaching and research institutions are confronted with extraordinary working conditions. In order to enable empirical data collection under these special circumstances, teachers and scientists need to respond flexibly and new concepts need to be developed. This paper deals with the challenges that arise in day-to-day teaching and provides different approaches to meet these challenges. It covers quantitative surveys, remote UX-testing methods as an alternative to eye tracking studies in the lab, as well as face-to-face user experience testings under strict hygiene measures.
Preventing Spoliation of Evidence with Blockchain: A Perspective from South Asia

Evidence destruction and tempering is a time-tested tactic to protect the powerful perpetrators, criminals, and corrupt officials. Countries where law enforcing institutions and judicial system can be comprised, and evidence destroyed or tampered, ordinary citizens feel disengaged with the investigation or prosecution process, and in some instances, intimidated due to the vulnerability to exposure and retribution. Using Distributed Ledger Technologies (DLT), such as blockchain, as the underpinning technology, here we propose a conceptual model – ‘EvidenceChain’, through which citizens can anonymously upload digital evidence, having assurance that the integrity of the evidence will be preserved in an immutable and indestructible manner. Person uploading the evidence can anonymously share it with investigating authorities or openly with public, if coerced by the perpetrators or authorities. Transferring the ownership of evidence from authority to ordinary citizen, and custodianship of evidence from susceptible centralized repository to an immutable and indestructible distributed repository, can cause a paradigm shift of power that not only can minimize spoliation of evidence but human rights abuse too. Here the conceptual model was theoretically tested against some high-profile spoliation of evidence cases from four South Asian developing countries that often rank high in global corruption index and low in human rights index.
Face Angle Identification with Ensemble Learning

The current face recognition methods may not work well on non-upright faces due to the heterogeneity of the training set. This study proposes an ensemble model to identify the angle of a face. The face is then rotated according to the predicted angle before feeding to face recognition. Our proposed model takes advantages of Divide-and-Conquer methods which breaks down a complicated problem to several simple tasks. A number of based classifier, i.e. Convolutional Neural Network, with a simple structure is used to classify whether a face is in a given range or not. The final angle prediction is determined by the majority voting. The experimental results suggest that our method achieve excellent performance in terms of accuracy and speed in face angle identification.
Link Prediction Based on Hyper-Substructure Network

Link prediction has long been the focus in the analysis of network-structured data. Though straightforward and efficient, heuristic approaches like Common Neighbors perform link prediction with pre-defined assumptions and only use superficial structural features. While it is widely acknowledged that a node could be characterized by a bunch of neighbor nodes, network embedding algorithms and newly emerged graph neural networks still exploit structural features on the whole network, which may inevitably bring in noises and limits the scalability of those methods. In this chapter, we propose an end-to-end deep learning framework, namely hyper-substructure enhanced link predictor (HELP), for link prediction. HELP utilizes local topological structures from the neighborhood of the given node pairs, avoiding useless features. For further exploiting higher-order structural information, HELP also learns features from hyper-substructure network (HSN). Extensive experiments on five benchmark datasets have shown the state-of-the-art performance of HELP on link prediction.