Reviewerly/exHarmony huggingface dataset:
Factors Affecting Behavioral Intentions towards Cloud Computing in the Workplace: A Case Analysis for Jordanian Universities

Adopting new technologies in Jordanian universities, namely cloud services, represents change in practices that needs to be investigated, as it is expected to face resistance in adoption by faculty members and staff that are used to old practices. A dedicated questionnaire was constructed based on the UTAUT model in order to identify the factors affecting the behavioral intentions leading to use new technologies. Five Jordanian universities participated in this study, and the results showed that there is a high behavioral intention (BI) among staff and faculty members to use cloud services and solutions within their workplace. This research showed that there are factors positively affecting the adoption of cloud services in Jordanian universities, and negative factors have been identified too. University management and staff members needs to be introduced to these factors in order to have better judgement on the future investment and practices of using new technologies.
Augmented Reality Application in Manufacturing Industry: Maintenance and Non-destructive Testing (NDT) Use Cases

In recent years, a structural transformation of the manufacturing industry has been occurring as a result of the digital revolution. Thus, digital tools are now systematically used throughout the entire value chain, from design to production to marketing, especially virtual and augmented reality. Therefore, the purpose of this paper is to review, through concrete use cases, the progress of these novel technologies and their use in the manufacturing industry.
Gutenberg Goes Neural: Comparing Features of Dutch Human Translations with Raw Neural Machine Translation Outputs in a Corpus of English Literary Classics

Due to the growing success of neural machine translation (NMT), many have started to question its applicability within the field of literary translation. In order to grasp the possibilities of NMT, we studied the output of the neural machine system of Google Translate (GNMT) and DeepL when applied to four classic novels translated from English into Dutch. The quality of the NMT systems is discussed by focusing on manual annotations, and we also employed various metrics in order to get an insight into lexical richness, local cohesion, syntactic, and stylistic difference. Firstly, we discovered that a large proportion of the translated sentences contained errors. We also observed a lower level of lexical richness and local cohesion in the NMTs compared to the human translations. In addition, NMTs are more likely to follow the syntactic structure of a source sentence, whereas human translations can differ. Lastly, the human translations deviate from the machine translations in style.
Effects of Foreign Aid Inflow on Economic Growth of Uganda: Autoregressive Distributed Lag Modelling (ARDL)

Uganda has always relied on foreign assistance to enhance its economic development and has been a recipient of foreign assistance from individual countries and from multilateral organizations.The study aims at establishing the effects of foreign aid on the economic growth of Uganda.It used annual time series data for the period 1970-2017.The hypothesis that foreign aid can promote growth was explored.This hypothesis was tested using time series data for foreign aid.An empirical model is estimated using the Autoregressive Distributed Lag (ARDL) approach to cointegration proposed by Pesaran and Shin [59].Results show that foreign aid inflow significantly reduced economic growth in Uganda in the short run and long run.The domestic investment was significant and had a positive sign in the short run while exports increased output in the short and long run.Dummy variable for insecurity increase output in the short run and increased it in the long run.The study identified previous periods of democracy index and effective labor to have negatively affected output in the short run.In the long run, effective Labour force, exports, Dummy variable for insecurity and democratic index was found to increase output in the long run.This study contributes to the literature on foreign aid and economic growth by providing the theoretical and empirical evidence for Uganda.
An improved de-interleaving algorithm of radar pulses based on SOFM with self-adaptive network topology

As a core part of the electronic warfare (EW) system, de-interleaving is used to separate interleaved radar signals. As interleaved radar pulses become more complex and denser, intelligent classification of radar signals has become very important. The self-organizing feature map (SOFM) is an excellent artificial neural network, which has huge advantages in intelligent classification of complex data. However, the de-interleaving process based on SOFM is faced with the problems that the initialization of the map size relies on prior information and the network topology cannot be adaptively adjusted. In this paper, an SOFM with self-adaptive network topology (SANT-SOFM) algorithm is proposed to solve the above problems. The SANT-SOFM algorithm first proposes an adaptive proliferation algorithm to adjust the map size, so that the initialization of the map size is no longer dependent on prior information but is gradually adjusted with the input data. Then, structural optimization algorithms are proposed to gradually optimize the topology of the SOFM network in the iterative process, constructing an optimal SANT. Finally, the optimized SOFM network is used for de-interleaving radar signals. Simulation results show that SANT-SOFM could get excellent performance in complex EW environments and the probability of getting the optimal map size is over 95% in the absence of priori information.
Efficient Private Set Intersection Using Point-Value Polynomial Representation

Private set intersection (PSI) allows participants to securely compute the intersection of their inputs, which has a wide range of applications such as privacy-preserving contact tracing of COVID-19. Most existing PSI protocols were based on asymmetric/symmetric cryptosystem. Therefore, keys-related operations would burden these systems. In this paper, we transform the problem of the intersection of sets into the problem of finding roots of polynomials by using point-value polynomial representation, blind polynomials’ point-value pairs for secure transportation and computation with the pseudorandom function, and then propose an efficient PSI protocol without any cryptosystem. We optimize the protocol based on the permutation-based hash technique which divides a set into multisubsets to reduce the degree of the polynomial. The following advantages can be seen from the experimental result and theoretical analysis: (1) there is no cryptosystem for data hiding or encrypting and, thus, our design provides a lightweight system; (2) with set elements less than <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:msup><mml:mrow><mml:mn>2</mml:mn></mml:mrow><mml:mrow><mml:mn>12</mml:mn></mml:mrow></mml:msup></mml:math>, our protocol is highly efficient compared to the related protocols; and (3) a detailed formal proof is given in the semihonest model.
Extracting Semantic Concepts and Relations from Scientific Publications by Using Deep Learning

With the large volume of unstructured data that increases constantly on the web, the motivation of representing the knowledge in this data in the machine-understandable form is increased. Ontology is one of the major cornerstones of representing the information in a more meaningful way on the semantic Web. The current ontology repositories are quite limited either for their scope or for currentness. In addition, the current ontology extraction systems have many shortcomings and drawbacks, such as using a small dataset, depending on a large amount predefined patterns to extract semantic relations, and extracting a very few types of relations. The aim of this paper is to introduce a proposal of automatically extracting semantic concepts and relations from scientific publications. This paper introduces a novel relevance measurement for concepts, and it suggests new types of semantic relations. Also, it points out of using deep learning (DL) models for semantic relation extraction.
On the Composition of Single-Keyed Tweakable Even-Mansour for Achieving BBB Security

Observing the growing popularity of random permutation (RP)-based designs (e.g, Sponge), Bart Mennink in CRYPTO 2019 has initiated an interesting research in the direction of RP-based pseudorandom functions (PRFs). Both are claimed to achieve beyond-the-birthday-bound (BBB) security of 2n/3 bits (n being the input block size in bits) but require two instances of RPs and can handle only oneblock inputs. In this work, we extend research in this direction by providing two new BBB-secure constructions by composing the tweakable Even-Mansour appropriately. Our first construction requires only one instance of an RP and requires only one key. Our second construction extends the first to a nonce-based Message Authentication Code (MAC) using a universal hash to deal with multi-block inputs. We show that the hash key can be derived from the original key when the underlying hash is the Poly hash. We provide matching attacks for both constructions to demonstrate the tightness of the proven security bounds.
Development of breast cancer diagnosis system based on fuzzy logic and probabilistic neural network

Breast cancer is one of the most common kinds of cancers that infect females in the whole world. It has happened when the cells in breast tissues start to grow in an uncontrollable way. Because it leads to death, early detection and diagnosis is a very important task to save the patient's life. Due to the restriction of human observers, computer plays a significant role in detecting early cancer signs. The proposed system uses a multi-resolution analysis and a top-hat operation for detecting the suspicious regions in a mammogram image. The discrete wavelet transform feature analysis is utilized for extracting features from the region of interest. Fuzzy Logic (FL) and Probabilistic Neural Network (PNN) are utilized for classifying the tumor into normal or abnormal. The differences between the proposed system and other researches are the use of adaptive threshold value depending on each image, by using Discrete Wavelet Transform (DWT) in both segmentation and feature extraction phases, which decrease complexity and time. Additionally, the detection of more than one tumor in the breast mammogram image and the utilization of FL and PNN work on increasing the system efficiency that led to raising the accuracy rate of the system and reducing the time. The obtained results of accuracy, sensitivity, and specificity were equal to 99 %, 98 %, and 47 %, respectively, and these results showed that the proposed system is more accurate than the other previous related works
Performance assessment of Deep Learning procedures on Malaria dataset

Malaria detection is a time-consuming procedure. Only blood sample investigation is the practice which provides the confirmation. Now numerous computational methods have been used to make it faster. The proposed model uses the conception of Convolutional Neural Network (CNN) to lessen the time complexity in identification of Malaria. The prototypical model uses different deep learning algorithms which uses the same dataset to validate the stability. Model uses the two various components of CNN like Sequential and ResNet. ResNet uses more of number of hidden layers rather than sequential. The ResNet model achieved 96.50% accuracy on the training data, 96.78% accuracy on the validation data and 97% accuracy on the testing data. Sequential model on the other hand achieved 98% accuracy on the training data, 96% accuracy on the validation data and 96% accuracy on the testing data. From the initial hypothesis, we get to know that there is no significant difference in the accuracy when we have too many layers.
VR Technology in Marketing From the Perspective of Customer Experience

Virtual reality technology is a new media technology.On the one hand, virtual reality technology has brought a brand-new impact on people's production and life.On the other hand, our understanding of virtual reality is far from enough, and its derived social problems, Academic issues and ethical issues need to be further explored, and use correct theories to promote and guide the further development of technology.In the process of market segmentation being considered as the core concept of marketing strategy, many scholars at home and abroad have conducted a lot of research on market segmentation.Based on the research results at home and abroad, this article uses factor analysis, cluster analysis, comparative analysis, and inductive deduction as research methods to establish an analysis model for the application of VR technology in marketing from the perspective of customer experience.Based on the application of VR technology in marketing from the perspective of customer experience, the research results show that after using the research method of this article, the data error rate is significantly controlled, and the data error rate is maintained within five percentage points, compared with previous studies The method data accuracy rate is increased by 15%, and the data accuracy rate is higher, which has certain practical value.
Homomorphic-Encrypted Volume Rendering

Computationally demanding tasks are typically calculated in dedicated data centers, and real-time visualizations also follow this trend. Some rendering tasks, however, require the highest level of confidentiality so that no other party, besides the owner, can read or see the sensitive data. Here we present a direct volume rendering approach that performs volume rendering directly on encrypted volume data by using the homomorphic Paillier encryption algorithm. This approach ensures that the volume data and rendered image are uninterpretable to the rendering server. Our volume rendering pipeline introduces novel approaches for encrypted-data compositing, interpolation, and opacity modulation, as well as simple transfer function design, where each of these routines maintains the highest level of privacy. We present performance and memory overhead analysis that is associated with our privacy-preserving scheme. Our approach is open and secure by design, as opposed to secure through obscurity. Owners of the data only have to keep their secure key confidential to guarantee the privacy of their volume data and the rendered images. Our work is, to our knowledge, the first privacy-preserving remote volume-rendering approach that does not require that any server involved be trustworthy; even in cases when the server is compromised, no sensitive data will be leaked to a foreign party.
Edge map response of dilated and reconstructed classical filters

Edge detection is a basic technique of fundamental feature detection in image processing domain. Dilation of edge filters kernels has proven to bring benefits for the edge detection operation by permitting to filter out noise and to take in consideration a bigger region of the image when processing. Numerous techniques were used in the past for finding edge features, one of the most common used being finding features in lower level scale of the image pyramid. Now, naturally, we want to investigate if our dilating of the filter kernels bring similar benefits as finding edges in a lower scale pyramid level.
UI/UX Design for Metora: A Gamification of Learning Journalism Interviewing Method

In learning, effective media is needed to deliver the material. Visual media can be more acceptable and facilitate understanding and strengthen memory. In this modern era, digital-based visual media is more effectively captured by students. Educational games in the form of gamification can provide the same effect as effective learning media. To support educational games, interface design and user experience are the first aspects seen by users. This study explored the user interface and user experience in the process of developing a gamification of a non-game subject, Journalism Interviewing Method. User interface includes colour, shape and layout the interface. User experience in game design process includes the tangible user interface, constructive and helpful feedback, content and storytelling, how to appreciate user. This study explored the user interface and user experience in the process of developing a gamification of a non-game subject, Journalism Interviewing Method.
Pengembangan Media Pembelajaran Pop Up Book Pada Pembelajaran IPS Di Kelas IV SD

In the learning process in the classroom, students often find it difficult to understand the material being taught because the use of learning media is not optimal. This study aims to develop pop-up book learning media and to determine the feasibility of the developed pop-up book learning media. The type of research used is Research and Development (R&amp;D) with research and development procedures from Borg and Gall. The instrument used was a questionnaire sheet for expert validation and student responses. Based on the data analysis, the material expert's assessment was 95.6% in the "Very Good" category; the linguist's assessment of 87.5% is categorized as "Very Good"; the media expert's assessment of 94.3% is categorized as "Very Good" and the assessment of the response of students in class IV SD Negeri Secang by 99% is categorized as "Very Good". So that the pop-up book meets the criteria to be used as a learning medium.
Research on Improved Chaotic Particle Optimization Algorithm Based on Complex Function

In order to improve the performance of Particle Swarm Optimization (PSO) algorithm in solving continuous function optimization problems, a chaotic particle optimization algorithm for complex functions is proposed. Firstly, the algorithm uses qubit Bloch spherical coordinate coding scheme to initialize the initial position of the population. This coding method can expand the ergodicity of the search space, increase the diversity of the population, and further accelerate the convergence speed of the algorithm. Secondly, Logistic chaos is used to search the elite individuals of the population, which effectively prevents the PSO algorithm from falling into local optimization, thus obtaining higher quality optimal solution. Finally, complex functions are used to improve chaotic particles to further improve the convergence speed and optimization accuracy of PSO algorithm. Through the optimization tests of four complex high-dimensional functions, the simulation results show that the improved algorithm is more competitive and its overall performance is better, especially suitable for the optimization of complex high-dimensional functions.
Inclusive Education in Remote Instruction with Universal Design for Learning

When schools are closed for a longer period of time, due to emergency situations, as in this COVID-19 pandemic, the educational landscape changes, so teachers and students must adapt to different remote learning solutions. Distance education has been used successfully before, with a wide range of students of different ages and abilities, but the sudden need to use technology and remote instruction for all, in order to replace face-to-face classroom interaction is challenging. Certainly, many teachers and students were unprepared for this ad-hoc need to use distance learning. Teachers’ knowledge and abilities to use remote instruction solutions can vary greatly, and so are student’s readiness and their at-home technical capacity to access the information sent by their teachers. Students may be very different even in the traditional classroom, but school closures and the technological demands of distance learning can disproportionately affect students, especially those with Special Educational Needs (SEN) or at risk. From the perspective of inclusive education, teachers are assuming new roles and responsibilities as they are expected to meet the needs of all students in their classroom. Those teachers, committed to the principles of inclusion, will embrace the challenges of remote instruction, and will try to design the curriculum having in mind all their students, regardless of their abilities, and use all the advantages that the remote learning environment has to offer. The guidelines of the Universal Design for Learning (UDL) are in line with the principles of inclusion and provide a useful framework for discussion.
Becoming University Language Teachers in South Korea: The Application of the Interpretative Phenomenological Analysis and Social Cognitive Career Theory

Due to the developments of globalisation, the international reputation, the entertainment industry, and Korean pop culture, a large number of language teachers, international schoolteachers, and professional school staff decided to travel to South Korea for career development and enhancement. However, due to a constant shortage of teachers through high turnover, many South Korean schools and universities are facing recruitment issues. The purpose of this study was to investigate two problems. Why do individuals decide to come to South Korea as foreign language university teachers, and how do individuals describe their teaching experiences as foreign language teachers at one of the university teaching and learning environments in South Korea. From the perspective of the Social Cognitive Career Theory (SCCT), the results indicated that all participants travelled to South Korea for financial and career development goals. Also, all faced negative experiences and discrimination due to their skin colours and nationalities. The researcher hopes the appropriate personnel can take this study as an opportunity to improve and enhance their current workplace and community for a better environment and development.
Investigation of Cloud Computing Barriers: A Case Study in Saudi Arabian SMEs

Cloud Computing is arguably the most significant technological development after the Internet. It accelerated technology adoption and gave birth to new business models. More importantly, it is acting as a foundation for new technologies like the Internet of Things (IoT) and Artificial Intelligence (AI). The Cloud Computing paradigm provides a level playing field for Small and Medium-sized Enterprises (SMEs) as they are able to adopt technologies that were not affordable before. Governments, vendors and business support organisations across the world have a plethora of initiatives to encourage SME adoption of Cloud Computing technologies. Despite these initiatives, many SME decision makers are still hesitant to adopt the cloud. This research reports an engagement with 16 information technology (IT) managers working for Saudi manufacturing SMEs. This paper presents their perceived barriers to migrating their applications to cloud services.