Reviewerly/exHarmony huggingface dataset:
Modulation Recognition of MIMO Systems Based on Dimensional Interactive Lightweight Network

Automatic modulation recognition is the core algorithm in the field of modulation classification in communication systems.Our investigations show that deep learning (DL) based modulation recognition techniques have achieved effective progress for multiple-input multiple-output (MIMO) systems.However, network complexity is always an additional burden for high-accuracy classifications, which makes it impractical.Therefore, in this paper, we propose a low-complexity dimensional interactive lightweight network (DilNet) for MIMO systems.Specifically, the signals received by different antennas are cooperatively input into the network, and the network calculation amount is reduced through the depth-wise separable convolution.A two-dimensional interactive attention (TDIA) module is designed to extract interactive information of different dimensions, and improve the effectiveness of the cooperation features.In addition, the TDIA module ensures low complexity through compressing the convolution dimension, and the computational burden after inserting TDIA is also acceptable.Finally, the network is trained with a penalized statistical entropy loss function.Simulation results show that compared to existing modulation recognition methods, the proposed DilNet dramatically reduces the model complexity.The dimensional interactive lightweight network trained by penalized statistical entropy also performs better for recognition accuracy in MIMO systems.
An attack Resistant Privacy-Preserving Access Control Scheme for Outsourced E-pharma Data in Cloud

Key Aggregate Cryptosystem was used to construct a safe and scalable healthcare data exchange system in the cloud (KAC). KAC offered single-key encryption and decryption rights delegation for any group of ciphertexts utilizing an aggregated secret key. However, leakage of delegation keys and a specified bound on number of maximal ciphertext classes were major issues with KAC.The Scalable and Strengthened -KAC (SSKAC) approach was created to decide the number of ciphertext classes using an elliptic curve dynamically while simultaneously boosting the security level of KAC by utilising double encryption. Access control is crucial because the majority of healthcare data is extremely sensitive and should only be accessed by those who are permitted. As a result, SSKAC includes attribute-based access control with anonymous authentication to give access control on health data with anonymous authentication. However, the access policy may expose end-user privacy to an attacker. To tackle this problem,we used the cuckoo filter to disguise the right of entry policy tosafeguard the privacy information of the owner. The inference assault affects medical records. One of the strategies for ensuring data confidentiality from unforeseen data access is inference control. The Individual data privacy is secured through the use of two inference control methods; The Query Set Size Prevention and K-Anonymity.
Use of Online Technologies, Open Hardware, and Open-Software for Advanced Architecture Design

The teaching of current technical disciplines poses a substantive change since the student moves from an activity focused on the acquisition of information to an acquisition of skills of various kinds. This learning requires that teaching objectives be set in terms of ability, in professional and existential situations, which are likely to manifest in real life. The project presented aims to develop coordination between three subjects in order to develop an educational experience through the execution of a real case. In the case presented, students will be able to immerse themselves in the complete process of providing intelligence to buildings through the use of low-cost and accessible micro-technologies for capturing information and transmitting data, using cloud databases and data exchange. With this project, it is proposed to implement new teaching methodologies that promote learning based on the acquisition of skills and cooperative work among students.
Particle Swarm Optimization of Convolutional Neural Networks for Human Activity Prediction

The increased usage of smartphones for daily activities has created a huge demand and opportunities in the field of ubiquitous computing to provide personalized services and support to the user. In this aspect, Sensor-Based Human Activity Recognition (HAR) has seen an immense growth in the last decade playing a major role in the field of pervasive computing by detecting the activity performed by the user. Thus, accurate prediction of user activity can be valuable input to several applications like health monitoring systems, wellness and fit tracking, emergency communication systems etc., Thus, the current research performs Human Activity Recognition using a Particle Swarm Optimization (PSO) based Convolutional Neural Network which converges faster and searches the best CNN architecture. Using PSO for the training process, intends to optimize the results of the solution vectors on CNN which in turn improve the classification accuracy to reach the quality performance compared to the state-of-the-art designs. The study investigates the performances of PSO-CNN algorithm and compared with that of classical machine leaning algorithms and deep learning algorithms. The experiment results showed that the PSO-CNN algorithm was able to achieve the performance almost equal to the state-of-the-art designs with a accuracy of 93.64%. Among machine learning algorithms, Support Vector machine found to be best classifier with accuracy of 95.05% and a Deep CNN model achieved 92.64% accuracy score.
A Study on Factors Affecting Companies Long-Term Investments within the GCC Markets

This study aims to explore the determinants of companies' long-term investments within the context of Gulf Cooperation Council markets. The importance of this study arises from the fact that earlier researchers who investigated this subject had mostly concentrated on developed markets and evidence from developing markets is largely absent. Besides, the existing studies had focused on factors influencing companies' long-term investment at the company level, and evidence related to the impact of macroeconomic factors on such investments is scarce. A Panel Ordinary Least Squares regression is utilized to analyze the data of 435 companies from the year 2000 to 2014. This period is chosen due to the steady economic conditions of the investigated markets as they enjoyed a less volatile macroeconomic environment compared to post-2014, where the fall of oil prices severely affected these markets' growth. The analysis revealed contradicting findings compared to the existing literature regarding both macroeconomic factors and companies related factors that influences long-term investments. For instance, government spendings, oil prices, and trade levels are all significant factors in the Gulf Cooperation Council companies' long-term investments which is not the case in other emerging markets. Also, the industrial companies within these markets are not severely influenced by these factors compared to the services companies. Hence, the current study extends the existing literature on two dimensions by providing new insight into this subject from new markets and reveals new findings regarding the impact of macroeconomic factors on companies' long-term investments.
A constraint-based analysis of relative clause extraposition

This paper aims to provide a constraint-based analysis of relative clause extraposition (hereafter RCE) with the help of 597 tokens from BYU-BNC and COCA. In corpus findings, indefiniteness of subject NP and passive voice are salient. The grammatical weight of relative clause is the heaviest, thus being a very likely trigger for the extraposition. Discourse-New-Old-RCE is the most frequent type, implying that discourse-old information tends to be delivered in extraposed clause. As well, the extraposition is assumed to be employed in order to maximize focus effect on main predicate. All these corpus findings are well described in the system of sign-based construction grammar which enables multiple constraints to be inherited into RCE construction through inheritance hierarchy. Head-Extra Construction leads nonlocal EXTRA feature to be percolated up to sentential node. Consequently, the extraposition comes in with the help of constraints from stative-v, decl-cl, re-cl, and hd-noun-extra-cx.
The Evolution of Cyberattack Motives

A cyberattack can be defined as an action aiming to cause damages and losses to computer networks, information systems, and even personal devices and data. Many professionals and organizations have put a lot of effort and resources into preventing cyberattacks based on how they occur, their targets, and what damages they can cause. However, one of the aspects that are often overlooked and one of the reasons that cyberattacks are successfully carried out is the fact that the nature of attackers' motivations is not fully understood. Therefore, this research examines the main reasons for cyberattacks to be carried out by adversaries and the motives behind cyberattacks. Specifically, we studied over 7,700 cyber records and events between 2006 and 2018, including data breaches, privacy violations, and cyber incidents, to learn how attack motives have evolved over the years. The analyses of the data were mainly carried out using descriptive analysis. Our study found that the early cyberattacks were mainly financially motivated. However, in the later years, the cyberattack motives included espionage, ideology, and skill and knowledge testing. This implies that the motives behind cyberattacks became more varied in terms of types, proportions, and correlations between them. It is hoped and expected that the results of the analyses will be helpful to various stakeholders in such a way that they will better understand the reasons and motivations for cyberattacks.
An Improved BiLSTM Approach for User Stance Detection Based on External Commonsense Knowledge and Environment Information

In the age of social networks, the number of tweets sent by users has led to a sharp rise in public opinion. Public opinions are closely related to user stances. User stance detection has become an important task in the field of public opinion. However, previous studies have not distinguished between user viewpoints and stances. These studies usually detected stance from the perspective of the tweet level but rarely the user level. Therefore, in this paper, we defined user stance, which is the user viewpoint (support, oppose, and neutral) toward the entire target event process. On this basis, we put forward a user stance detection method based on external commonsense knowledge (such as SenticNet) and environment information (such as a user’s historical tweets, topic information, and neighbor tweets) and denote this method as ECKEI. First, in order to better integrate external commonsense knowledge into the neural network, we improved BiLSTM and called it CK-BiLSTM for complementary commonsense information to the memory cell. Secondly, we used LDA to extract the topic of user tweets and designed a topic-driven module to capture the users’ neighbors’ information. Finally, we used the attention mechanism to integrate information from users’ historical tweets and neighbors’ tweets obtained through topic information; then, we used the softmax layer to classify user stances into the support, neutral and oppose classes. In this paper, we conducted experiments and assessments on datasets containing information on Brexit and the elections to verify the practicability and effectiveness of our proposed method. Extensive experimental results on the Brexit and elections datasets show that our approach outperforms six baseline methods (SVM-ngram, NB, MTTRE (RNN), Pkudblab (CNN), TAAT, and Aff-feature). We use the average micro-F1 and average accuracy to measure performance on the detection of a user’s stance. The ECKEI model makes improvements of 4.30–16.89% and 1.22–16.58% on the Brexit and election datasets, respectively, in terms of average micro-F1. Meanwhile, this model makes improvements of 4.24–17.46% and 0.48–14.64% on the Brexit and election datasets, respectively, in terms of average accuracy. Our model makes improvements of 5.34–17.30% and 2.65–19.73%, respectively, on the Brexit and election datasets in terms of average recall.
Mapping of extensible markup language-to-ontology representation for effective data integration

&lt;span lang="EN-US"&gt;Extensible markup language (XML) is well-known as the standard for data exchange over the Internet. It is flexible and has high expressibility to express the relationship between the data stored. Yet, the structural complexity and the semantic relationships are not well expressed. On the other hand, ontology models the structural, semantic and domain knowledge effectively. By combining ontology with visualization effect, one will be able to have a closer view based on respective user requirements. In this paper, we propose several mapping rules for the transformation of XML into ontology representation. Subsequently, we show how the ontology is constructed based on the proposed rules using the sample domain ontology in University of Wisconsin-Milwaukee (UWM) and mondial datasets. &lt;br /&gt; We also look at the schemas, query workload, and evaluation, to derive the extended knowledge from the existing ontology. The correctness of the ontology representation has been proven effective through supporting various types of complex queries in simple protocol and resource description framework query language (SPARQL) language.&lt;/span&gt;
Effects of Social Support and Teacher Disposition on the View of Teaching Profession of Preservice Early Childhood Teachers

이 연구의 목적은 예비유아교사의 사회적지지와 교직인성이 교직관에 영향을 미치는 요인은 무엇인지를 알아보는데 목적이 있다. 이러한 연구목적을 달성하기 위하여 J도에 소재한 유아교육과 152명을 대상으로 설문조사를 실시하였다. 자료분석은 SPSS 프로그램을 활용한 빈도분석, 평균과 표준편차를 구하였다. 또한 신뢰도 검증으로 Cronbach"s α를 산출하였고, 각 변인간의 관련성을 알아보기 위하여 상관관계 분석, 다면적 회귀분석 방법을 활용하여 다음과 같은 결과를 도출하였다. 연구결과 첫째, 예비유아교사들이 지각한 사회적지지에서는 친구지지와 부모지지가, 교직인성에서는 도덕성과 교육적신념이, 교직관에서는 교직에 관한 인식 및 개인적 자질과 전문적 자질을 가장 높게 인식하는 것으로 나타났다. 둘째, 예비유아교사들이 사회적지지, 교직인성, 교직관은 통계적으로 유의미한 상관관계를 가지고 있는 것으로 나타났다. 셋째, 예비유아교사들의 사회적지지의 하위요인과 교직인성의 하위요인이 교직관의 하위요인 모두에 긍정적인 영향을 미치는 것으로 나타났다. 이러한 연구결과로 볼 때, 예비유아교사들이 긍정적인 사회적지지를 받고 교직인성을 함양함으로써 올바른 교직관 확립을 갖추기 위한 기초자료로 활용하여 다양한 방안의 연구가 진행되어야 할 것으로 사료된다. The purpose of this study was to understand the effects of social support and teacher disposition on preservice early childhood teachers" view of the teaching profession. A survey was conducted using questionnaires targeting a total of 152 students majoring in early childhood education in the J province. The data were analyzed using frequency analysis, mean, and standard deviation on the SPSS statistical package program. Also, Cronbach"s α was calculated for verifying the reliability of the responses. Correlation and multiple regression analyses were used to understand the relationships between the variables. The results were as follows: First, the preservice early childhood teachers perceived the support of friends and parents to be most important in terms of social support. Further, they perceived morality and educational beliefs to be the prime elements of a teacher"s disposition. They also felt that the perception of the teaching profession and personal and professional qualifications were the factors that affected their view of the teaching profession. Second, the social support, teacher disposition, and view of the teaching profession of preservice early childhood teachers showed statistically significant positive correlations. Third, all the sub-factors of social support and teacher disposition had positive effects on the subfactors of the view of the teaching profession of preservice early childhood teachers. Based on the results of this study, there should be multilateral research studies to provide the basic data for establishing the correct view of the teaching profession in preservice early childhood teachers by getting positive social support and cultivating the proper teacher disposition.
A Deep Regression Network with Key-joints Localization for Accurate Hand Pose Estimation

In this paper, a new method is proposed to improve hand joint regression in 3D hand pose estimation.The existing methods regress all joints together given a depth map.This causes misallocations of some hand joints, misuse of hand depth information, and have difficulties in estimating 3D coordinates accurately.In this paper, joint regression is performed in stages, such that highly flexible joints e.g.fingertip joints are regressed first followed by less flexible joints to avoid getting some errors while estimating all joints together.In practice, fingertip joints constitute relatively higher estimation errors than all other joints.Thus, we perform fingertip joint localization (2D joint estimation) after obtaining rough pose estimates from the pose estimator to locate fingertip joint positions.We then use these 2D joint estimates to generate the depth coordinates of the pose estimator.To further ensure the accuracy of the absolute pose hypothesis, we integrate a robust implicit shape-based hand detector with the deep regression pose estimator into one pipeline through a shared convolutional layer.Finally, a shared convolutional layer converts the 2D joint location to 3D poses.Consequently, our system can accurately estimate hand pose based on the prior knowledge of a well detected human hand and the properly located joint positions.Experiments were carried out on three publicly available datasets, ICVL, NYU, and MSRA.The proposed hand pose estimation system attains an accuracy of 96.4% at the threshold level of 40mm on the ICVL dataset, 92% on MSRA, and 89% on the NYU dataset illustrating the effectiveness of the proposed system over many state-of-art approaches.
Will the Role of Infrastructure in Higher Education Become Redundant?-A Critical Study of the Expectation of Students

During the countrywide lockdown due to the pandemic, students attended online classes and it appeared that physical classes may not be necessary or lose their relevance in future as students could easily get access to online learning materials.Students sitting in the comfort of their homes could access the best learning materials available anywhere in the world and excel in their respective fields of study.One of the astounding questions that came for debate those days was whether we need huge college buildings and enormous infrastructure facilities when laptop, a mobile phone or any device serves the purpose.But interestingly, students slowly but steadily showed signs of boredom and ennui with the online classes and were eager to join the physical classes.Many of them went to the extent of demanding physical classes.This phenomenon has led to some of the significant questions that we, as educationists and policy makers need to seriously address with immediate priority.First and foremost, why is that students prefer physical classes in spite of the fact that best of the resource material is available on their finger tips?What are the expectations of students in the changed contemporary scenario?Will the words of Bill Gates on the complete irrelevance of physical campuses, classrooms and physical infrastructure prove to be prophetic?This paper will delve deep into these research questions by conducting an systematic empirical study of the phenomenon.The research partners wouldbe the managers of education, students, staff, parents and other stakeholders.It is envisaged that the outcome of the study will be of immense use in the context of most of the higher education institutions adopting virtual, blended or hybridized pedagogies.They will have tremendous implications to the higher education scenario of the future.
A Conceptual Model of Transformational Leadership on Employees’ Digital Creativity in the Digital Age

The objective of this paper is to present a framework based on transformational leadership theory. The framework demonstrates how direct managers' transformational leadership behavior influences employees' digital creativity by increasing employees' innovative self-efficacy in the context of digital technology use. At the individual level, the theory is applied to investigate the mediating mechanism of two digital technology learning styles (digital exploration and digital utilization) between transformational leadership and employee digital creativity. Moreover, the role of gender is introduced as a moderator in digital exploration, digital utilization, and digital creation. In practice, it can guide the enterprise's front-line management to adjust their leadership behavior in a timely manner, as well as enhance and promote employees' innovative self-efficacy through transformational leadership behaviors such as idealized influence, encouragement, inspiration, and individualized care. Innovative use of digital technology to effectively can boost employees' digital capability.
An Anomaly Warning Method for Structural Monitoring Data based on Spatio-temporal Correlation

Based on the existing research results of data cleaning and data fusion based on spatio-temporal correlation, aiming at the problem of low warning accuracy due to the failure to consider the cause of outliers in the existing warning methods, a structure monitoring data anomaly warning method based on spatio-temporal correlation is proposed: Based on the analysis of abnormal types and causes of structural monitoring data, the outliers of real-time monitoring data were compared with the spatio-temporal prediction results at the same time, and the causes of anomalies were judged and warned according to the process. The practical application results show that the structure monitoring anomaly warning method based on spatio-temporal correlation can avoid losing part of the spatio-temporal characteristics of the data. According to the spatio-temporal prediction results, the cause of anomaly classification can avoid the error warning caused by system anomaly or fault.
The Influence of Community Volunteer Service on Students’ Voluntary Service and Professional Development

Student volunteering as a form of community participation is essential for higher education's goal. Volunteering focuses on the service delivered, and the service user is the beneficiary. Youth that participates in community service have better outcomes. While the globe is grappling with a pandemic pushing people to keep their distance from one another, youth community service opportunities may seem different but are still a vital part of youth development. The university's community extension initiatives provide students with a learning experience through outreach activities. UCLM College of Hospitality and Tourism Management outreach program features the students' voluntary participation in the outreach program to conduct the community extension activities amid the COVID-19 pandemic. The outreach program supports the students' desire to participate in community outreach amidst the COVID-19 pandemic. This study was conducted to assess the impact of community extension services on student-volunteers and how this volunteerism can contribute to their professional development. The respondents of this study were the student-volunteers of the university on the community extension services during the COVID-19 pandemic. It was found out that the involvement of the student volunteers in various community extension activities has a great impact on their knowledge and skills. Moreover, the involvement of the student volunteers in the limited community extension activities has given them a chance to strengthen values and positive attitudes towards life, studies, family, community, and environment.