Reviewerly/exHarmony huggingface dataset:
Recognition of Channel Codes based on BiLSTM-CNN

Channel code recognition, which aims to recognize the channel code adopted by the received signal, plays an important role in the fields of non-cooperative communications. Deep learning based channel code recognition methods have been attracting great attention due to their superiority in learning from massive signals and extracting signal features automatically. However, these methods mainly use a single type of neural network and suffer from low recognition accuracy. In this paper, we propose a channel code recognition algorithm based on two types of neural networks including bi-directional long short-term memory (BiLSTM) and convolutional neural network (CNN). According to the proposed algorithm, the received signal is firstly fed into BiLSTM and then handled by CNN, which inherits the advantages of both BiLSTM and CNN. Experimental results show that the proposed algorithm outperforms the existing TextCNN based algorithm, and the improvement of average recognition accuracy is about 4% at the low signal to noise ratio region.
Research on Microseismic Source Location Method Based on Waveform Characteristics Monitored by Nanomaterial Sensor under the Background of Metal Oxide Polluted Environment

In recent years, the monitoring range of source location technology has developed from being one-dimensional and two-dimensional to being three-dimensional. However, due to the complexity and nonuniformity of the seismic wave propagation medium and the uncertainty of the propagation law, there will be large errors in the source location results. Therefore, the analysis of vibration signal has become the key problem of current research. This paper designs a microseismic monitoring system based on Internet of Things sensors, which can monitor the vibration wave characteristics of vibration signals. In order to test the positioning accuracy of the system, this paper introduces three positioning methods: target positioning method based on time difference, time delay estimation method based on EMD, and source target positioning method based on the characteristic frequency of vibration signal. The purpose of this paper is to find the most accurate method from the three source location methods. Through these three methods, the vibration source generated by a single person walking in situ can be located in the vibration positioning experiment of human walking. The error between the actual position and the measurement source position is compared. The results show that the time delay estimation method based on empirical mode decomposition has the highest positioning accuracy. In addition, in the microseismic experiment, it is proved that the positioning accuracy of EMD using L1 norm statistical criterion is higher than that using L2 norm statistical criterion.
CT Modalitesinde U-Net Tabanlı Segmentasyon Ağlarını Kullanarak Pankreas Segmentasyonu: Karşılaştırmalı Bir Analiz

The pancreas is one of the small size organs in the abdomen. Moreover, anatomical differences make it difficult to detect the pancreas. This project aims to automatically segmentation of pancreas. For this purpose, NIH-CT82 data set, which includes CT images from 82 patients was used. U-Net which is state-of-the-art model and its different versions, namely Attention U-Net, Residual U-Net, Attention Residual U-Net, and Residual U-Net++ were tested. Best predict performance was achieved by Residual U-Net with the dice of 0.903, IoU of 0.823, sensitivity of 0.898, specificity of 1.000, precision of 0.908, and accuracy of 0.999. Consequently, an artificial intelligence (AI) supported decision support system was created for pancreas segmentation.
A Potential-Based Quantization Procedure of the Damped Oscillator

Today, two of the most prosperous fields of physics are quantum computing and spintronics. In both, the loss of information and dissipation play a crucial role. In the present work, we formulate the quantization of the dissipative oscillator, which aids the understanding of the abovementioned issues, and creates a theoretical frame to overcome these issues in the future. Based on the Lagrangian framework of the damped spring system, the canonically conjugated pairs and the Hamiltonian of the system are obtained; then, the quantization procedure can be started and consistently applied. As a result, the damping quantum wave equation of the dissipative oscillator is deduced, and an exact damping wave solution of this equation is obtained. Consequently, we arrive at an irreversible quantum theory by which the quantum losses can be described.
A Conversation with Shaimaa Lazem

Ubiquity's senior editor Dr. Bushra Anjum chats with Dr. Shaimaa Lazem, an associate research professor at SRTA-City Egypt. They discuss how computer science curricula should aim at increasing students' sensibilities and appreciation of the differences in the worldviews, skills, and abilities of technology users; as well as teach students how to harness and embrace these differences in technology design.
Thirty-Two Years of IEEE VIS: Authors, Fields of Study and Citations

The IEEE VIS Conference (VIS) recently rebranded itself as a unified conference and officially positioned itself within the discipline of Data Science. Driven by this movement, we investigated (1) who contributed to VIS, and (2) where VIS stands in the scientific world. We examined the authors and fields of study of 3,240 VIS publications in the past 32 years based on data collected from OpenAlex and IEEE Xplore, among other sources. We also examined the citation flows from referenced papers (i.e., those referenced in VIS) to VIS, and from VIS to citing papers (i.e., those citing VIS). We found that VIS has been becoming increasingly popular and collaborative. The number of publications, of unique authors, and of participating countries have been steadily growing. Both cross-country collaborations, and collaborations between educational and non-educational affiliations, namely “cross-type collaborations”, are increasing. The dominance of the US is decreasing, and authors from China are now an important part of VIS. In terms of author affiliation types, VIS is increasingly dominated by authors from universities. We found that the topics, inspirations, and influences of VIS research is limited such that (1) VIS, and their referenced and citing papers largely fall into the Computer Science domain, and (2) citations flow mostly between the same set of subfields within Computer Science. Our citation analyses showed that award-winning VIS papers had higher citations. Interactive visualizations, replication data, source code and supplementary material are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://32vis.hongtaoh.com</uri> and <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://osf.io/zkvjm</uri> .
Handling consumer vulnerability in e-commerce product images using machine learning

<h2>Absract</h2><h3>Need</h3> In recent years, secondhand products have received widespread attention, which has raised interest in them. The susceptibility issues that consumers encounter while buying online products in reference to the display images of the products are also not well researched. <h3>Motivation</h3> Retailers employ clever tactics such as ratings, product reviews, etc., to establish a strong position thereby boosting their sales and profits which may have an indirect impact on the consumer purchase that was not aware of that retailer's behavior. This has led to the novel method that has been suggested in this work to address these issues. <h3>Proposed methodology</h3> In this study, a handling method for reused product images based on user vulnerability in e-commerce websites has been developed. This method is called product image-based vulnerability detection (PIVD). The convolutional neural network is employed in three steps to identify the fraudulent dealer, enabling buyers to purchase goods with greater assurance and fewer damages. <h3>Summary</h3> This work is suggested to boost consumers' confidence in order to address the issues they encounter when buying secondhand goods. Both image processing and machine learning approaches are utilized to find vulnerabilities. On evaluation, the proposed method attains an F1 score of 2.3% higher than CNN for different filter sizes, 4% higher than CNN-LSTM when the learning rate is set to 0.008, and 6% higher than CNN when dropout is 0.5.
Human capital and internet literacy impact on economic growth in Indonesia

This study investigates the development of human capital in Indonesia through investment in education and health by the Indonesian government on job participation and economic growth.Modeling "autoregressive vectors" used to understand the causal link between variables required 21 years, from 2000 to 2020.The World Bank and the Indonesian Central Statistics Agency provided secondary data for this study.We use the variables of education, health, internet literacy, work participation, and economic growth in Indonesia.We found that internet literacy has a significant effect on economic growth and also provides a significant boost to education and health.Where education and health are the main factors forming human capital that have an impact on the quality of human resources.Improving the quality of education from the education and health development process has an impact on job participation.Employment participation itself encourages increased investment in education and health.This can illustrate the role of internet literacy and human capital on economic growth in Indonesia where economic growth can be boosted by increasing internet literacy and human capital.
Galaxy deblending using residual dense neural networks

We present a new neural network approach for deblending galaxy images in astronomical data using residual dense neural network (RDN) architecture. We train the network on synthetic galaxy images similar to the typical arrangements of field galaxies with a finite point spread function (PSF) and realistic noise levels. The main novelty of our approach is the usage of two distinct neural networks: (i) a deblending network which isolates a single galaxy postage stamp from the composite and, (ii) a classifier network which counts the remaining number of galaxies. The deblending proceeds by iteratively peeling one galaxy at a time from the composite until the image contains no further objects as determined by the classifier, or by other stopping criteria. By looking at the consistency in the outputs of the two networks, we can assess the quality of the deblending. We characterize the flux and shape reconstructions in different quality bins and compare our deblender with the industry standard, sextractor. We also discuss possible future extensions for the project with variable PSFs and noise levels.
Implementation of Reinforcement-Learning Algorithms in Autonomous Robot Navigation

The problem of autonomous robot navigation in indoor environments must overcome various difficulties such as the dimensionality of the data, the computational cost, and the possible presence of mobile objects. This chapter addresses the implementation of an algorithm for autonomous navigation of robots in indoor environments based on machine learning. It characterizes some strategies that the literature reports and specifies a Deep Q-Network reinforcement-learning algorithm to implement on the Turtlebot robotic platform of the Gazebo simulator. Besides, a series of experiments changing the parameters of algorithm to validate the strategy shows how the robotic platform, through the exploration of the environment and the subsequent exploitation of the information, creates effective route planning.
The contribution of activity theory to modeling multi-actor decision-making: A focus on human capital investments

Making investment decisions is usually considered a challenging task for investors because it is a process based on risky, complex, and consequential choices ( Shanmuganathan, 2020 ). When it comes to Investments in human capital (IHC), such as startups fundings, the aspect of decision-making (DM) becomes even more critical since the outcome of the DM process is not completely predictable. Indeed, it has to take into consideration the will, goals, and motivations of each human actor involved: those who invest as well as those who seek investments. We define this specific DM process as multi-actor DM (MADM) since not a group is making decisions but different actors, or groups of different actors, who – starting from non-coinciding objectives – need to reach a mutual agreement and converge toward a common goal for the success of the investment. This review aims to give insights on psychological contributions to the study of complex DM processes that deal with IHC to provide scholars and practitioners with a theoretical framework and a tool for describing the complex socio-ecological systems involved in the DM processes. For this purpose, we discuss in the paper how the third generation of activity theory ( Leont’ev, 1974 , 1978 ; Engeström, 1987 , 2001 ) could be used as an appropriate model to explain the specificities of MADM construct, focusing on the particular case of startup funding. Design thinking techniques will be proposed as a methodology to create a bridge between different activity systems.
DPVisCreator: Incorporating Pattern Constraints to Privacy-preserving Visualizations via Differential Privacy

Data privacy is an essential issue in publishing data visualizations. However, it is challenging to represent multiple data patterns in privacy-preserving visualizations. The prior approaches target specific chart types or perform an anonymization model uniformly without considering the importance of data patterns in visualizations. In this paper, we propose a visual analytics approach that facilitates data custodians to generate multiple private charts while maintaining user-preferred patterns. To this end, we introduce pattern constraints to model users' preferences over data patterns in the dataset and incorporate them into the proposed Bayesian network-based Differential Privacy (DP) model <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PriVis</i> . A prototype system, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DPVisCreator</i> , is developed to assist data custodians in implementing our approach. The effectiveness of our approach is demonstrated with quantitative evaluation of pattern utility under the different levels of privacy protection, case studies, and semi-structured expert interviews.
INVESTIGATING STUDENTS’ FAMILIARITY WITH CONVERSION, AN ENGLISH WORD FORMATION PROCESS

One of the paramount aspects of acquiring a language is mastering the vocabulary. One way to enrich our vocabulary is by familiarizing ourselves with word formation process. This study aimed to investigate students’ familiarity with a word formation process called conversion. This was a descriptive quantitative study. Using Google Forms, the researchers distributed a questionnaire containing statements on conversion to collect data. The participants were 10 first year students of the English Language Education Study Program (ELESP) of Sanata Dharma University, Yogyakarta. The researchers calculated the students’ correct and incorrect answers. Results showed that most of the students were unfamiliar with conversion, one of the word formation processes, especially the conversion involving verbs to nouns (50%) and adjectives to nouns (50%). Most students were knowledgeable about the conversion from nouns to verbs (80%). One of the implications is that ELESP students study conversion, one of the word formation processes in English, to enrich their vocabulary items.
TransNav: spatial sequential transformer network for visual navigation

Abstract Visual navigation task is to steer an embodied agent finding the given target based on observation. The effective transformer from observation of the agent to visual representation determines the navigation actions and promotes more informed navigation policy. In this work, we propose a spatial sequential transformer network (SSTNet) for learning informative visual representation in deep reinforcement learning. SSTNet is composed by spatial attention probability fused model (SAF) and sequential transformer network (STNet). SAF enforces cross-modal state into visual clues in reinforcement learning. It encodes semantic information about observed objects, as well as spatial information about their location, which jointly exploiting image inter-relations. STNet generates (imagines) the next observations and makes action inference of the aspects most relevant to the target. It decodes the image intra-relations. This way, the agent learns to understand the causality between navigation actions and dynamic changes in observations. SSTNet is conditioned on an auto-regressive model on the desired reward, past states, actions, and knowledge graph. The whole navigation framework considers the local and global visual information, as well as time sequential information. Thus, it allows the agent to navigate towards the sought-after object effectively. We evaluate our model on the AI2THOR framework show that our method attains at least $10\%$ improvement of average success rate over most state-of-the-art models. Code and datasets can be found in https://github.com/zhoukang123/SDTNet_2022.
Literature Review on the Barriers to Online Learning during Covid-19 Pandemic

The Covid-19 pandemic had pressured institutions around the world to move to online learning.Students, teachers, and education administrators were not prepared for the sudden change in the mode of delivery.As a result, there have been issues with online learning.Many studies have been conducted in the last two years to explore the matter.In this paper, the author will attempt to provide a literature review of previous studies about barriers to online learning both before and after the pandemic.A comprehensive list of barriers is presented.As a result of the review, there is not much difference in the barrier to online learning before and after the pandemic.This paper aims to present a broad overview of the topic so educators and school administrators can develop a plan to enhance the quality of online education in the future.
Personal and professional development of students – future teachers with the use of blended learning

Introduction. The relevance of the research is conditioned by the needs of education in development of innovative approaches providing high-quality competence training of future pedagogues. The forms of blended learning have a potential to strengthen the subjectivity and optimisation in the process of personal and professional development of students, but they are applied fragmentarily in higher pedagogical education and are not systematically explored. Purpose of the research: development of a model of personal and professional development of students – future pedagogues with the use of blended learning forms as well as testing its effectiveness in the course of experimental work. Materials and methods. The experiment involved 54 students, future bachelors of pedagogical profile studying at Moscow State Regional University (Russian Federation). Methods: testing, students’ self-assessment and peer assessment, expert evaluation of students’ personal and professional development by teachers. Statistical methods: Mann-Whitney U-test. Results. The model of personal and professional growth of students – future pedagogues was developed with the use of the forms of blended learning – as a set of components (value/target focused, organisational/managerial, design/technological) and stages (informational/stimulating, productive/activity based, evaluative/reflexive) integrating students’ personal activity, students’ joint activity and teachers’ activity as subjects of electronic educational environment. It was revealed that the application of the developed model of personal and professional development of students – future pedagogues with the use of the forms of blended learning showed efficiency in the development of competence parameters: academic proficiency, involvement and motivation of future educators (U = 187; p &lt; 0.01). Conclusion. The novelty of the research lies in the elaboration of a model for personal and professional development of students with the use of the forms of blended learning, as well as of a complex of supporting content and assignments for group and independent work. The results can be used for designing of educational technologies based on the developed model, for other areas and levels of higher education.
Pruning Weightless Neural Networks

Weightless neural networks (WNNs) are a type of machine learning model which perform prediction using lookup tables (LUTs) instead of arithmetic operations.Recent advancements in WNNs have reduced model sizes and improved accuracies, reducing the gap in accuracy with deep neural networks (DNNs).Modern DNNs leverage "pruning" techniques to reduce model size, but this has not previously been explored for WNNs.We propose a WNN pruning strategy based on identifying and culling the LUTs which contribute least to overall model accuracy.We demonstrate an average 40% reduction in model size with at most 1% reduction in accuracy.
IoS-OSA: Open System Architecture for Internet of Services

Many cross-domain and cross-region services and applications have flooded the Internet, such as API services, IoT services, etc. These services are interconnected and form a new service ecosystem, the Internet of Services (IoS). Through IoS, service providers have a broader platform to deliver their services to users. Users can ask for a composite service solution without finding multiple suitable services themselves. However, IoS requires a standard modeling language and methodology to provide developers with a unified understanding of IoS. This modeling methodology should guide developers through the IoS modeling task. Therefore, we propose an Open System Architecture for the Internet of Services (IoS-OSA). We divide IoS into a three-dimensional cube according to layers, views, and lifecycle dimensions, with 64 different views. It integrates the existing scattered work of IoS into a unified framework and introduces the content description and the recommended modeling specification of each view. Finally, we propose the corresponding modeling methods for several key views in IoS-OSA such as service ecosystem, value/quality/capability (VQC) of IoS, etc., which improve the overall architecture of IoS.