Reviewerly/exHarmony huggingface dataset:
Circular-symmetric correlation layer

Despite the vast success of standard planar convolutional neural networks, they are not the most efficient choice for analyzing signals that lie on an arbitrarily curved manifold, such as a cylinder. The problem arises when one performs a planar projection of these signals and inevitably causes them to be distorted or broken where there is valuable information. We propose a Circular-symmetric Correlation Layer (CCL) based on the formalism of roto-translation equivariant correlation on the continuous group $$S^1 \times \mathbb {R}$$ , and implement it efficiently using the well-known Fast Fourier Transform (FFT) algorithm. We showcase the performance analysis of a general network equipped with CCL on various recognition and classification tasks and datasets.
Undersampling of approaching the classification boundary for imbalance problem

Summary Using imbalanced data in classification affect the accuracy. If the classification is based on imbalanced data directly, the results will have large deviations. A common approach to dealing with imbalanced data is to re‐structure the raw dataset via undersampling method. The undersampling method usually uses random or clustering approaches to trimming the majority class in the dataset, since some data in the majority class makes not contribute to classification model. In this paper a revised undersampling approach is proposed. First, we perform space compression in the vertical direction of the separating hyperplane. Then, a weighted random sampling hybrid ensemble learning method is carried out to make the sampled objects spread more widely near the separating hyperplane. Experiments with 7 under‐sampling methods on 21 imbalanced datasets show that our method has achieved good results.
HCCI tool: a lens to support industrial designers during the conceptualisation of smart products

Interactions between humans and smart products (i.e. digital components integrated in physical Internet of Things devices) are becoming more complex and less visible. Yet designers lack tools to capture these interactions and incorporate them into their design. In this paper we present the Human-Computer-Context Interaction (HCCI) tool that helps the designer to consider the different interactions of the user early in the conceptualisation phase, in order to eventually improve the user experience for smart products. This tool introduces 5 relevant interaction levels to be considered, when defining the context-of-use. In this paper we assess the use of the tool by means of a design challenge with a total of 34 industrial design students, given the task to design a smart kitchen concept. The tool was evaluated by a mixed method approach. Results show that the tool was evaluated as useful and usable during the early concept phase of the design of smart products. Unsupported concepts typically envisioned a single user interacting with one object through explicit interactions. Hence, tool-supported designs were more holistic and better captured interactions with other objects, users, content and services. From this we conclude that the tool can help to detect possible pitfalls of design selections. The tool is aimed to be used in a new product development process by HCI researchers, designers, and developers and is technology independent. Based on observations and participant feedback, we reflect on the strengths and limitations of this tool.
Implementation of Naïve Bayes Algorithm for Classification of Mental Health of Social Media Users

Social media has become a human need to interact in everyday life. Apart from being a means of communication, social media also has the additional function of exchanging information on the internet in various forms including writing, images and videos. One of the social media that has many users is Instagram, where Instagram offers information sharing features in the form of images, photos and short videos. The purpose of this feature is for users to express themselves and attract the attention of others, thereby creating feelings of happiness and increasing self-confidence. In addition to positive impacts, there are also negative impacts on users, for example excessive use that causes addiction so that it can cause mental health disorders. Mental health needs to be handled properly so that it does not continue to get worse, but there are several obstacles in seeing a psychiatrist in mental health, including limited access and also negative stigma if someone sees a psychiatrist. Therefore, a tool is needed that can be an early indication in knowing the level of mental agitation, especially in the use of Instagram. Classification in data mining can help provide initial information on a person's condition in his mental health. The Naïve Bayes algorithm provides an accuracy rate of 92.5% in classifying mental health on data sets that have been clustered. Good accuracy can help social media users know their mental health condition.
COMPARISON OF RANDOM FOREST AND NAÏVE BAYES METHODS FOR CLASSIFYING AND FORECASTING SOIL TEXTURE IN THE AREA AROUND DAS KALIKONTO, EAST JAVA

Soil texture is used to determine airflow, heat, instability, water holding capacity, and the shape and structure of the soil structure. Soil texture as an important attribute that determines the direction of soil management must be modeled accurately. However, soil texture is a soil attribute that is quite difficult to model. It is a compositional data set that describes the particle size of the soil mineral fraction (sand, silt, and clay). The methods used to classification and predict soil texture with machine learning algorithms are Random Forest (RF) and Naïve Bayes (NB). The purpose of this study was to classify the distribution of soil texture using the Random Forest and Naïve Bayes methods to obtain the most accurate grouping results. This research was conducted in the area around Kalikonto River Basin, East Java Province. The performance-based tests show that the RF algorithm provides higher accuracy in predicting soil texture based on the Digital Elevation Model (DEM). The results of RF’s performance testing on training data and testing data gave an accuracy value of 92.55% and 87.5%. Classification using the Naïve Bayes method produces an accuracy value of 89.98% on testing data and 80.65% accuracy on training data.
VR for Pain Relief

The present chapter explores how immersive virtual reality (VR) systems can be used for pain research and treatment. Pain is a universal, yet entirely subjective and multifaceted unpleasant experience. One of the earliest VR studies on pain highlighted the role of attention in pain modulation. However, the role of body representation in pain modulation has also been described as a crucial factor. Through virtual reality systems, it is possible to modulate both attention to pain and body representation. In this chapter, first we define how immersive VR can be used to create the illusion of being present in immersive VR environments and argue why VR can be an effective tool for distracting patients from acute pain. However, distraction seems to be less useful in chronic pain treatment. Chronic pain can be highly disabling and can significantly impact not only the sufferer's quality of life, but also their perceptions of the bodily self. Close neural connections between the body matrix and pain open a chance for influencing pain through bodily illusions. This chapter explores approaches to inducing body ownership illusions in VR and discusses how they have been applied in pain research. The present chapter also covers a set of practical indications and methodological caveats of immersive VR and solutions for overcoming them. Finally, we outline several promising future research directions and highlight several yet unexplored areas.
Knowledge Base Embedding for Sampling-Based Prediction

Each link prediction task requires different degrees of answer diversity. While a link prediction task may expect up to a couple of answers, another may expect nearly a hundred answers. Given this fact, the performance of a link prediction model can be estimated more accurately if a flexible number of obtained answers are estimated instead of a predefined number of answers. Inspired by this, in this article, we analyze two evaluation criteria for link prediction tasks, respectively ranking-based protocol and sampling-based protocol. Furthermore, we study two classes of models on link prediction task, direct model and latent-variable model respectively, to demonstrate that latent-variable model performs better under the sampling-based protocol. We then propose a latent-variable model where the framework of Conditional Variational AutoEncoder (CVAE) is applied. Experimental study suggests that the proposed model performs comparably to the current state-of-the-art even under the conventional rank-based protocol. Under the sampling-based protocol, the proposed model is shown to outperform various state-of-the-art models.
INNOVATION ON 360° VIDEO APPLICATION AS A NEW NORM IN DEVELOPING HISTORICAL TOURISM AND ITS ACCEPTANCE AMONG VIEWERS: THE CASE OF MALAYSIA NATIONAL MUSEUM

Lack of maximising the technology in today’s tourism ways of doing things is the main concern of one development and success in tourism sectors. Previous studies have proven the success rate of using the current innovation and trends in tourism sector help to increase in terms of the visibility of the tourist attractions and numbers of visitors in one tourist location. Hence, in the present study, it focuses on National Museum planning in its initiative to adopt new technology for the betterment of Malaysia National Museum popularity. Museum is a must visit location of one country as it acts to introduce ways of live and culture of its people. Yet, many of local and international visitors tend to neglect the importance of visiting museums. This is due to the portrayal of physical static museum leads towards unattractive environment and not aware of the high potential in understanding historical background of the place that they visited. Recent years, the numbers of people visiting national museum in Malaysia specifically are reducing. It is impacted due to the present movement order control since March 2020. The limitation of physical visitation to a tourist spot such as national museum has worsen the situation. Therefore, the present study aims to explore the use and application of 360o view technology in Malaysia National Museum. The present study collected images and 360 o videos using device namely Insta360 ONE R Twin Edition. Once completed, questionnaire was distributed to collect 50 viewers use and satisfaction on the onsite national museum videos and images output. Results explained that viewers agreed and enjoy using the 360 o video to explore the museum virtually. Furthermore, it also assisted visitor to expect what they will see and go for future visit as results shows the viewer acceptance and intention to use the 360 o video is positively significant.
Instance selection using one‐versus‐all and one‐versus‐one decomposition approaches in multiclass classification datasets

Abstract Instance is important in data analysis and mining; it filters out unrepresentative, redundant, or noisy data from a given training set to obtain effective model learning. Various instance selection algorithms are proposed in the literature, and their potential and applicability in data cleaning and preprocessing steps are demonstrated. For multiclass classification datasets, the existing instance selection algorithms must deal with all the instances across the different classes simultaneously to produce a reduced training set. Generally, every multiclass classification dataset can be regarded as a complex domain problem, which can be effectively solved using the divide‐and‐conquer principle. In this study, the one‐versus‐all (OVA) and one‐versus‐one (OVO) decomposition approaches were used to decompose a multiclass dataset into multiple binary class datasets. These approaches have been widely employed when constructing the classifier but have never been considered in instance selection. The results of instance selection performance obtained with the OVA, OVO, and baseline approaches were assessed and compared for 20 different domain multiclass datasets as the first study and five medical domain datasets as the validation study. Furthermore, three instance selection algorithms were compared, including IB3, DROP3, and GA. The results demonstrate that using the OVO approach to perform instance selection can make the support vector machine (SVM) and k‐nearest neighbour (k‐NN) classifiers perform significantly better than the OVA and baseline approaches in terms of the area under the ROC curve (AUC) rate, regardless of the instance selection algorithm used. Moreover, the OVO approach can provide reasonably good data reduction rates and processing times, which are all better than those of the OVA approach.
A Hybrid Cross-Scale Transformer Architecture for Robust Medical Image Segmentation

Transformer architecture has emerged to be successful in many natural language processing tasks. However, its applications to clinical practice remain largely unexplored. In this study, we propose a Robust Cross-Scale Hybrid Transformer (RCSHT) architecture for medical image segmentation, which can effectively enhance the multi-scale feature representations while integrating local features with global dependencies. Specifically, we propose two new modules based on the self-attention mechanism: PSCM and PCCM, which perform matrix low-rank transformation on cross-scale images in spatial and channel space, respectively, to effectively enhance the discriminant ability of long-range dependencies of multi-scale images and effectively reduce the computational complexity. Meanwhile, we use spatial positional encoding to perform spatial regularization on the two attention modules, so that the model has more opportunities to obtain valuable spatial details and further enhance the spatial retention ability. Besides, by fusing outputs of the two modules, the robustness and discriminant ability of the feature representations are further improved. Note that our hybrid architecture allows transformers to be initialized as convolutional networks without pre-training. Extensive experiments conducted on our newly proposed dataset have demonstrated our RCSHT notably outperforms the state-of-the-art methods by a large margin, holding the promise to generalize well on other medical image segmentation.
Simulation of Water Resources Utilization in the Jereweh Watershed, West Sumbawa Regency, Indonesia

This study aims to simulate using water resources in the Jereweh watershed to meet irrigation water needs. From the calculation results of the analysis of the annual average water availability at the Lang Desa Dam of 0.96 m 3 /sec (29.98 mln m 3 ), the Tiu Bangkemah Embung of 1.53 m 3 /sec (47.60 mln m 3 ), and the Murus I Embung of 0.06 m 3 /sec (1.79 million m 3 ). The optimal allocation of water based on the water balance in the Jereweh watershed system is as follows: cropping pattern MT- I: rice (100%); MT-II: 30% rice + secondary crops (70%); MT-III: beo with a planting intensity of 200%. It was early planting November I, type of secondary crops: corn with reliability &gt; 80%, namely Lang Desa Weir 88%, Tiu Bangkemah Embung 92% and Murus I Dam 92%. The total area served is 650 ha: Lang Desa Dam - 300 ha, the Tiu Bangkemah Embung - 300 ha, Murus I Embung - 50 ha. From the simulation results consists, it is necessary to add water reservoirs (dams) to maximize the performance of existing natural resources facilities in the Jereweh watershed system (there is a deficit of 150 ha). From the results, it can be seen that the value of benefits obtained in the existing condition is Rp. 6,840 bln, while the value of benefits received after the simulation is Rp. 14,82 bln so that the difference in the value of benefits is Rp. 7,98 bln.
ENEX-FP: A BERT-Based Address Recognition Model

In e-commerce logistics, government registration, financial transportation and other fields, communication addresses are required. Analyzing the communication address is crucial. There are various challenges in address recognition due to the address text’s features of free writing, numerous aliases and significant text similarity. This study shows an ENEX-FP address recognition model, which consists of an entity extractor (ENEX) and a feature processor (FP) for address recognition, as a solution to the issues mentioned. This study uses adversarial training to enhance the model’s robustness and a hierarchical learning rate setup and learning rate attenuation technique to enhance recognition accuracy. Compared with traditional named entity recognition models, our model achieves an F1-score of 93.47% and 94.59% in the dataset, demonstrating the ENEX-FP model’s effectiveness in recognizing addresses.
Mobile Hand Gesture Controlled Application using Transfer Learning

An increasing number of companies move to a touchless user interface, partly because of the pandemic's impact and the restricted rules of using public access objects. The market for Zero User Interface is increasing and industrial implementations are growing and promising. Currently, the knowledge of keeping yourself healthy and clean is essential to prevent the spread of Covid-19 disease. A proposed project – a hand gesture-controlled application to properly guide the user through the handwashing process can increase the overall level of hygiene in public. The solution is realized with a transfer learning method based on EfficientNet Lite models making it possible to run on Android, iOS, embedded Linux devices, and microcontrollers.
Use of Klett Augmented Service in the Process of Studying German as a Second Foreign Language

The article considers the didactic priciples of using Klett Augmented service in the study of German as a second foreign language at the Department of Applied Linguistics of the National University “Lviv Polytechnic The authors summarize the experience of domestic and foreign researchers on the introduction of electronic technologies in the educational process. Modern achievements in neurodidactics are taken into account, in particular “Learning by Doing”, the effectiveness of ”multichannel” learning through the receipt of information through audio, visual, audio-visual, motor-kinetic channels is analyzed. In the course of the research the authors came to the conclusion that expedient and methodically correct use of Klett mobile application is an effective application in the process of learning German as a foreign language, promotes phonetic, lexical, communicative, translation skills, ability to use language skills in future professional activities.
Input decomposition by clusterization for symbolic execution

The clusterization approach to a symbolic execution input decomposition was evaluated in the paper. The study uses the Klee engine to demonstrate the effects of input clustering for the console chess game written in the C language. The clusterization algorithm is adapted to the Klee by isolating a chosen cluster from the program code. The effect of different cluster sizes on the coverage, error detection, and execution time is evaluated. The obtained result shows benefits of the clusterization as a decomposition approach and may be used in further research to improve symbolic execution performance.
An effective and efficient approach to collect, accumulate and analyze feedback from the client

Gathering feedback from the client can be the only way to track client satisfaction and monitor team status. Usually, it can take much time and effort especially if you have several teams with different stakeholders. In such cases, voice calls can be ineffective and might not bring expected results. Having a system/approach which will decrease the required for feedback gathering time, increase the accuracy of the data, make the process of gathered data validation and analyzing easier managers can concentrate their efforts and time on other aspects of the project management.
Adaptive Least-Squares Support Vector Machine and its Combined Learning-Selflearning in Image Recognition Task

In the paper, we introduce an image recognition system that is based on least squares support vector machines and has matrix inputs. It is designed to solve a vast class of tasks within general problems of Data Stream Mining and Big Data Mining, in particular, an image recognition task when observations are fed sequentially in online mode. Its distinctive features include not just the ability to process images in their initial matrix form without vectorization, but also that centers of activation functions are formed with the observations from the training set. The tuning procedure of the system is characterized by the combination of the supervised learning paradigm, “lazy” learning using the “neurons at data points” concept, T. Kohonen’s self-learning, and learning vector quantization.
Two-dimensional Computer Modelling of Contaminant Migration in Unsaturated Porous Media with Nanoparticles

The complicated physical problem of computer modeling pollutant migration in the zone of suspended water with nanoparticles to the catcher filter is discussed in this study. The brand-new mathematical model of nonlinearity in two dimensions is offered. The generalized Darcy-Clute laws for mass and moisture movement in various subregions of soil are taken into careful consideration in the model. The finite difference method was used to find a numerical solution to the boundary value problem and to provide an algorithm for computer implementation. Utilizing a newly created Nanosurface program solution, computer modeling was carried out along with the analysis of numerical experiments. The nanoparticle influence was demonstrated.
The Recurrent-and-Parallel Implementation of COMBI GMDH algorithm on CPU and GPU

the paper presents a parallel implementation of recurrent combinatorial algorithm which enables the efficient utilization of available computing resources both the host CPU cores and CUDA kernels. A series of test experiments shows an increase in the comparative efficiency of the algorithm with an increase in the task complexity.
An Implementation of Large Scale Hate Speech Detection System for Streaming Social Media Data

The omnipresence of online social media brings various positive and negative consequences for society. Besides benefits, social media can cause big problem caused by hate and offensive contents. Detecting and removing those toxic contents using machine learning is a major research topic in social network. Two of the challenges of this topic are that the volume of social media data is so big and that these data need to be processed in real-time. In this paper, we set out to develop system to detect hate speech in Vietnamese YouTube comments using machine learning and big data technology. The streaming data from Youtube is processed in real-time using Kafka, Spark, and machine learning technology. Finally, a dashboard powered by Streamlit will be used to display the results.