Reviewerly/exHarmony huggingface dataset:
Forge News Detection: A Systematic Review

The widespread use social media in human life for information access and the sharing of social life tales and events has grown its popularity. Due to the increasing influence of social media, the fake data is also increasing day by day and its impact in terms of ethical, cultural, and economical issues are significant. Now a day’s forge news is being utilized for many illegal activities such as political advantages against troops and military institutions etc. These aspects make it more severe. This study reviews the literature in order to emphasize forge news concerns, various algorithms and solutions for detecting forge news content have been focused in this study. The new researchers in the arena of automatic forge news content detection will learn a lot from this review study.
“Various Dehazing Techniques for Vehicle detection and tracking in Hazy Environment- A survey”

Vehicle detection and tracking is most important concern in today’s scenario. Traffic surveillance or monitoring system is used to monitor the various traffic related problems such as security, over-speeding, road accidents etc. Traffic control and management system is used in many of the cities where automatic number plate Recognition system (ANPR) is used. The ANPR system software captures the high resolution and high quality images and videos of the licensed number plates of the vehicles and then used in computer processing to extract the desired information. This ANPR System faces difficulty in capturing the images in bad weather conditions like haze, fog, dust, mist etc. This paper is study about different dehazing methods which helps in removing haze from the images and videos. Dehazing techniques are used to get the clear vision of captured images in ANPR system to provide security and detailed information about the vehicles under hazy atmosphere.
Improving reading comprehension using descriptive text related to the local culture at the second grade of SMA Negeri 14 Bandar Lampung

This research was aimed to find out whether there is significant difference after the implementation of descriptive text related to the local culture toward students’ reading comprehension and to investigate how the students’ appraisal after the implementation. Adapting quantitative approach, this research was conducted through one group pre-test and post-test design. The result showed that there was significant difference of students’ reading comprehension after they were taught through descriptive text related to the local culture concerning the gain of their score from the first to the second test. It could be seen from the computation that the significant value of the test were lower than alpha (0.00&lt;0.05) while the t-value (9.342) was higher than t-table (2.0322). It was also revealed that students had a positive appraisal after the implementation. It could be seen from the computation that frequency of students above 70% with range frequency (70-100%) who had a high appraisal were 25 and the frequency of students under 55% with range frequency (1-54%) who had a low appraisal were 6. Most of the students accepted the implementation of descriptive text related to the local culture. Thus, local culture can be used as reading material in teaching and learning English. The students have a good score after implementation and they also enjoyed the activities and learning process of reading that has been provided.
Valuation of Crypto Assets on Blockchain with Deep Learning Approach

The importance of crypto-asset valuation is increasing as the ecosystem of blockchain continues to boom in recent years. We develop a deep learning framework to predict the value index of crypto assets by integrating the value variables’ information and decomposing the price into value and sentiment components. We apply the framework on ETH and AAVE, demonstrate their value indices and provide four econometric tests on the results’ quality. We show that the value index can be an effective proxy for crypto assets’ intrinsic value, and can thus provide a benchmark to the investment, consumption and financial reporting on crypto assets.
A Deep Learning Model for Mammography Mass Detection Using Mosaic and Reconstructed Multichannel Images

Breast cancer is the most publicized cancer that hits women around the world. It’s considered as the second cause of death among females. Early detection helps a lot in increasing the survival rate, and the probability of recovery from this disease. The mammogram is the main screening modality that is used regularly for breast cancer diagnosis. The accurate interpretation of the mammogram is very important for mass detection and diagnosis. The rapid evolution of deep learning is contributing to introduce more accurate systems that can act as a second opinion for the radiologists, and accordingly, this can help in providing an accurate diagnosis. In this paper, we propose a model for mass detection and classification based on You Look Only Once (YOLO)v4. We designed the experiment to investigate the performance of different augmentation techniques using YOLOv4 including mosaic that was introduced by YOLOv4. Furthermore, in the preprocessing phase, the images were reconstructed to be in a multichannel format which enhanced the detection accuracy by almost $$\simeq$$ 10%. The model was evaluated with the usage of different combinations of augmentation techniques (mosaic, mix-up, and conventional augmentation). The experiments were conducted on the INbreast and MIAS datasets, the results of INbreast showed that mosaic with YOLOv4 achieved the best results with mAP (mean average precision), precession, and recall of almost $$\simeq$$ 99.5%, 98%, and 94% respectively for detection. In addition, the proposed model achieved AP of 99.16% and 99.58% for classifying the detected masses as benign and malignant respectively. Furthermore, the best results on MIAS achieved mAP, precession, and recall of 95.28%, 93%, and 90% respectively. Finally, our methodology showed competitive performance compared to other similar studies.
Pcas: An Improved Pairing-Free Certificateless Aggregate Signature with Conditional Privacy Preserving for Vanets

Due to the rapid topology changes, a large amount of data exchange is generated in large-scale, and variable-density vehicular ad-hoc networks (VANETs). Therefore, vehicle communication security becomes the most critical aspect of road traffic. It is extremely difficult to design protocols for vehicle authentication that satisfy the security and privacy requirements while still providing efficient communication. With the certificateless aggregate signature (CLAS) scheme, the key escrow problem based on identity signature is solved. Thus, some new CLAS mechanisms have been proposed for the authentication in VANETs. However, many flaws, such as the high computational overhead and malicious KGC attack, are still existed in most CLAS schemes. In this paper, we first review a newly proposed CLAS scheme, and give an analysis of the existing issues. Afterwards, we give an enhanced CLAS scheme and apply it to vehicle authentication in VANETs. The security of the scheme is proved under the random oracle security model. Moreover, through theoretical analysis and simulation, the proposed scheme is shown to own better transmission overhead and computation overhead compared with the literature ones.
Big Data Software Architectures: An Updated Review

Big Data usually refers to the unprecedented growth of data and associated processes to gather, store, process, and analyze them to provide organizations and users with useful insights and information. The intrinsic complexity and characteristics of systems handling Big Data require software architectures as founded drivers for these systems to meet functional and quality requirements. In light of the relevant role of software architectures for Big Data systems, we investigate the current state of the art of Big Data software architectures. This paper presents the results of a systematic mapping study that updates existing literature reviews on this topic. We selected and analyzed 23 primary studies published in the last five years. We identified 11 architecture-related quality requirements and six architectural modules relevant to the design of software architectures for Big Data systems, besides analyzing whether existing proposals of reference architectures comply with these requirements and modules. We expect the results presented in this paper can provide a continuous update of the state of the art while highlighting essential concerns in the design of software architectures for Big Data systems.
Face Virtual Sample Generation with 3D Model Based on Generative Adversarial Networks

Generating virtual multi-view face images from a single face image has always been a challenge in the area of computer vision. It often suffers from appearance distortions and artifacts if the generation rule is not well-defined. In order to generate virtual samples that can contribute to recognition accuracy, this paper proposes a three-dimensional face model reconstruction method based on generative adversarial networks to expand the training dataset. It transforms single training sample per person face recognition into multi-training sample face recognition by expanding the training dataset. Our generated virtual samples have been proved efficient in improving recognition rate on face database using different algorithms.
The Forecasting of the Energy Conversion Efficiency for PV Systems: Theoretical Approach [Y.soler.c-M.sahni, 2022], and Its Notes in the Draft from An Upcoming Paper.

The success on forecasting the energy conversion performance of PV systems lies in tracking down the non-linear behavior dynamics of I-V curve, as consequence of the operational conditions or solar photovoltaic resources changes where they are set up (A.Qayoom et al.,2014).
Parallel Chunk Encryption in Public Cloud Storage: Validating Data Privacy

Public cloud storage is an easy way for businesses and end users to license storage capacity from a third party to store their digital data.Common characteristics of public cloud storage include: Easy to deploy, self-provisioned capacity. Highly automated management with rich APIs.Cloud services currently use many organizations to capture a large segment of today's competitive market as well as security issues among wearers of cloud services, The most powerful service is store data in the cloud, and cloud storage seems to be data safety in terms of privacy and integrity, as per cyber security professionals concerns the top storage security challenges in recent day's data loss and leakage. The purpose of this work is to introduce a new concept called the Parallel Chunk Encryption (PCE) Scheme, which inspects the integrity of data in public cloud storage with the help of a Trusted Third-Party Auditor (TTPA). The data holders chop the file (documents, videos. etc.) into multiple chunks; ever chunk is encrypted using the PCE scheme and Forward Encipher (FE) technique. The proposed Forward Encipher technique forwards the encrypted chunk to carry out next chunk encryption is necessary to perform the XOR operation in every chunk encryption it constructs to strengthen the privacy of data. The proposed scheme PCE can accomplish more effective and secure in terms of data sharing and data storage in a public cloud environment.
Model Based Migration of Cloud Systems: Review and Roadmap

Cloud computing has become a dominating trend in the IT industry and academia. In recent years, many Cloud providers have emerged in the market, each one has proper standards and service interface. Migration of legacy systems and their data to a Cloud platform or moving applications from one Cloud provider to another are complicated and high cost processes. In major legacy on-premise applications, they bring technical and business challenges. The choice of the best migration strategy is a hard task for Small and Medium-Sized Enterprises (SMEs) looking at best price and zero risk. Although guidelines offered by Cloud providers to support their users in migration tasks, the user implication is often mandatory to perform a conclusive migration. In this paper, we provide an overview of contemporary approaches for the model based migration of applications into Cloud computing. We highlight the Pros and Cons of each one and we present a summary comparison of these approaches. As well, we propose a new Cloud migration approach that renders the migration process more straightforward than with existent approaches and guarantees the services and data portability.
Time Effective Cloud Resource Scheduling Method for Data-Intensive Smart Systems

The cloud computing platforms are being deployed nowadays for resource scheduling of real time data intensive applications. Cloud computing still deals with the challenge of time oriented effective scheduling for resource allocation, while striving to provide the efficient quality of service. This article proposes a time prioritization-based ensemble resource management and Ant Colony based optimization (ERM-ACO) algorithm in order to aid effective resource allocation and scheduling mechanism which specifically deals with the task group feasibility, assessing and selecting the computing and the storage resources required to perform specific tasks. The research outcomes are obtained in terms of time-effective demand fulfillment rate, average response time as well as resource utilization time considering various grouping mechanisms based on data arrival intensity consideration. The proposed framework when compared to the present state-of-the-art methods, optimal fitness percentage of 98% is observed signifying the feasible outcomes for real-time scenarios.
Deep Learning-Based Helmet Wearing Detection for Safety Surveillance

Detecting a safety helmet wearing behavior is of great importance in protecting the life of the workers in power substations and construction sites. Comparing to traditional methods, deep learning could achieve better performances. Challenges of this work include changes of illumination, foreground objects shadows, dynamic background motion, camera motion, camouflage, or subtle regions. In addition, the postures of the workers are flexible, the workers may of low pixel resolution and be blocked by dense pipelines in the remote surveillance videos. In this research, a YOLO-based method is proposed to improve the accuracy of safety helmets detection. Different from most published methods which firstly predict the head and helmet, and then find out the person who did not wear a helmet, our method predicts the head without helmet case directly. The experimental results demonstrated the high accuracy and efficiency of the proposed method.
Image Compression and Encryption Based on Integer Wavelet Transform and Hybrid Hyperchaotic System

This paper address the issue of image compression and encryption is to minimize the amount of data required to represent the images and to provide better privacy and security to the users. A joint lossless algorithm: Integer Wavelet Transform (IWT) with hybrid hyperchaotic system is proposed to enhance the security of data transmission. IWT is used to compress the images and then the encryption is performed by hybrid hyper chaotic system. A hybrid hyperchaotic system: Fractional Order Hyperchaotic Cellular Neural Network (FOHCNN) and Fractional Order Four Dimensional Modified Chua’s Circuit (FOFDMCC) is used to generate the pseudorandom sequences. The pixel substitution and scrambling is realized by Global Bit Scrambling (GBS) that improves the cipher unpredictability. In this study, Deoxyribonucleic Acid (DNA) sequence is adopted instead of binary operation that resists the cipher images from crop attack, differential attack and salt and pepper noise. In the experimental phase, the proposed algorithm showed good performance in encryption compared to existing algorithms.
Model Sistem Informasi Inventaris Berbasis Android Menggunakan QR Code Pada Sekolah Dasar 1 Tumbakbayuh

Abstrak. Sistem pencatatan barang inventaris di Sekolah Dasar 1Tumbakbayuh masih dilakukan secara manual sehingga tidak efisien dalam proses pencatatan, pelaporan serta temu kembali data/informasi yang berkaitan dengan identitas barang inventaris saat diperlukan. Artikel ini menyajikan sebuah model aplikasi sistem pencatatan barang inventaris yang dilengkapi dengan fitur &lt;em&gt;QR&lt;/em&gt; &lt;em&gt;Code &lt;/em&gt;untuk mempercepat pendataan, pelaporan, serta pengenalan identitas inventaris. Sistem berbasis Web pada sisi Admin untuk manajemen data inventaris dan berbasis Android pada sisi &lt;em&gt;User&lt;/em&gt; untuk penyajian dan akses informasi inventaris, dikembangkan mengikuti tahapan-tahapan dalam model &lt;em&gt;Waterfall&lt;/em&gt;. Hasil uji &lt;em&gt;BlackBox &lt;/em&gt;menunjukkan fitur-fitur fungsional sistem yang berkaitan dengan rekaman data dan penyajian informasi telah sesuai dengan analisis kebutuhan sistem. Fitur &lt;em&gt;QR&lt;/em&gt; &lt;em&gt;Code&lt;/em&gt; juga telah berfungsi mengenali identitas inventaris. Uji &lt;em&gt;Usability &lt;/em&gt;juga dilakukan untuk menilai persepsi pengguna terhadap aplikasi. Hasil uji &lt;em&gt;Usability&lt;/em&gt; yang melibatkan 3 orang respon sebagai pengguna sistem menunjukan perolehan angka 76,7 (pada skala 1 – 100) yang berarti bahwa sistem berada pada kategori &lt;em&gt;Baik&lt;/em&gt; dan dapat diterima oleh &lt;em&gt;user&lt;/em&gt;.&lt;br /&gt;&lt;p&gt;&lt;strong&gt;Kata kunci&lt;/strong&gt;&lt;strong&gt;:&lt;/strong&gt; Sistem Informsi, Barang inventaris, Berbasis Web dan Android, &lt;em&gt;QR&lt;/em&gt; &lt;em&gt;Code&lt;/em&gt;&lt;/p&gt;&lt;em&gt;Abstract. The inventory recording system at 1 Tumbakbayuh Elementary School is still done manually so it is not efficient in the process of recording, reporting and retrieving data/information related to the identity of inventory items when needed. This article presents an application model for an inventory recording system equipped with a QR Code feature to speed up data collection, reporting, and identification of inventory identities. Web-based system on the admin side for inventory data management and Android-based on the User side for presenting and accessing inventory information, developed following the stages in the Waterfall model. The results of the BlackBox test show that the functional features of the system related to data recording and presentation of information are in accordance with the analysis of system requirements. The QR Code feature has also been used to identify inventory identities. Usability testing is also carried out to assess the user's perception of the application. The results of the Usability test involving 3 people responding as system users showed the acquisition of a score of 76.7 (on a scale of 1-100) which means that the system is in the good category and can be accepted by the user.&lt;/em&gt;&lt;br /&gt;&lt;strong&gt;&lt;em&gt;Keywords&lt;/em&gt;&lt;/strong&gt;&lt;em&gt;: Information System, Inventory Items, Web and Android Based, QR Code&lt;/em&gt;
Technoparticipation

Maggi Savin-Baden’s (2007) theories on the interplay between reflection and interruption consider moments of reflection as interruption as catalysts that provoke self-reflection and deep critical thinking. Such interruptions, disruptions and ‘disturbances’ (Vale 2017) have the power to produce new knowledge, new taxonomies and revised thinking. Aware of the increasing need for students to engage in self-reflection and critical thinking processes, reflection is not viewed, in respect to Savin-Baden’s ideas, as being a form of interruption but I consider interruptions to performance experiences as an immediate call for reflection (Savin-Baden 2007) and ultimately, interruption, in parallel with Fred Meller (2017) as a ‘critical thinking tool’. This short article will share and reflect upon aspects of Technoparticipation, the name given to a research project I first conceived in 2015 at Loughborough University that encourages digital criticality and creative learning potential via student engagement in digital technology where educational formats (tutorials, workshops, seminars, etc.) are viewed as ‘performative events’ (Nunes 2006: 130–1) to help students develop as autonomous self-reflective thinkers and doers in a constantly evolving digital age. The article will share a recent project I have recently set up with colleagues called Digital Pedagogies Open Studio at the University of the Arts London, which explores a set of questions we feel are pertinent right now given the heightened move to online/virtual forms of learning and teaching due to Covid-19. These questions include: 1) How do you disrupt the digital space pedagogically? and 2) How can you replicate chance happenings/an interruption online?
Unity in a Time of Uncertainty: Ithra’s Approach During the Pandemic in Saudi Arabia

One landmark of the Saudi cultural scene in the time of Covid-19 has been the King Abdulaziz Center for World Culture (also known as Ithra), a recently opened Saudi cultural institution that features museums, a library, a cinema, a theatre, a children’s museum and more. Ithra’s mission is ‘to make a tangible and positive impact on human development by inspiring a passion for knowledge, creativity and cross-cultural engagement for the future of the kingdom’. It is particularly unusual insofar as the Kingdom of Saudi Arabia has historically counted few museums and has an underdeveloped cultural ecosystem. The nation has been undergoing an intentional transformation with Vision 2030 Vision 2030. 2016. [Online]. National Transformation Program 2020. Available at: https://web.archive.org/web/20190401034619/https://vision2030.gov.sa/sites/default/files/NTP_En.pdf [Accessed 10 Feb. 2022].[Crossref] , [Google Scholar]. The programme includes vast investment in the cultural and creative sector, advancement of the quality of life and the development of cultural and creative industries (CCI).This article aims to map and understand the effect of the Covid-19 pandemic on Ithra’s institutional presence and programming. It argues that the challenges of the pandemic – which are expected to lead to the permanent closure of more than 15 per cent of the world’s museums – became an opportunity to expand Ithra’s efforts to widen its relationships with audiences, communities, creative professionals and institutional stakeholders. Saudi Arabia’s response to the Covid-19 pandemic has placed it at the highest ranks on international indexes in 2021. This article will present three different initiatives that showcase the tactics of the institution during and post-lockdown, highlighting key achievements.When Ithra opened to the public in 2018, there was no pre-existing audience for cultural offerings. Ithra had to create and engage audiences, position itself as an active member of the different communities and raise awareness related to culture and creativity in the whole region. Its focus on outreach and its diverse offerings have been essential components of Ithra’s existence. In some cases, Ithra has found more success than its established international peers that exist in mature CCI ecosystems. During lockdown, instead of curtailing its offerings, Ithra increased them, greatly expanding by capitalising on its already robust digital and virtual platforms.