Reviewerly/exHarmony huggingface dataset:
Android-Based Mobile Learning as an English Language Learning Media during Pandemic Times

In an era that is all advanced and modern, technological sophistication causes learning that initially runs in one direction, where teachers and students meet and communicate with each other in the classroom, can be carried out through the help of information technology. Along with the development of information, where books and teachers were originally the main learning resources, now they have begun to experience developments where learning resources originating from the internet have begun to be often used in the teaching and learning process, especially during the Covid 19 pandemic which requires students and teachers to learn. With the Online system (In the Network) which requires innovation in the teaching and learning process. Mobile learning is defined as a mobile device that is used in the teaching and learning process. English subject itself is one of the subjects that must be taken by students of Madrasah Ibtidaiyah Sinar Betung which must be achieved as well as possible. This subject emphasizes the concept of interaction using English and increasing English vocabulary at the elementary school level for English speaking skills in everyday life. Mobile learning developed in this research is in the form of electronic teaching materials in wave courses. The purpose of this research is to develop electronic teaching materials in the form of mobile learning. Mobile learning that has been built is only limited to conveying information using Internet facilities and students can download materials taught by teachers via smartphones that have installed Mobile Learning on Android owned by their students. The mobile learning application is built using the android studio programming language, note Path, PHP, Mysql database, display design with Photoshop. With this mobile learning application, it is easier for teachers and students to get online interaction and in delivering material that can be accessed anytime and anywhere.
Incorporating Types of Types in Ontology-Driven Conceptual Modeling

The Unified Foundational Ontology (UFO) has been used to provide foundations for the major conceptual modeling constructs. So far, UFO has reflected a view in which domain entities are fundamentally divided into those that collect invariants of the domain (i.e., types) and those entities that manifest those invariants (i.e., instances), following the conventional two-level classification scheme. This paper extends UFO with support for multi-level classification schemes, in which some entities accumulate both type-like and instance-like characteristics. This requires an ontological interpretation and a formal theory of types of types. This theory is employed to engineer new constructs and constraints into the OntoUML language, and to develop computational support for the formal verification of constraint violation over multi-level conceptual models.
Tissue and Tumor Epithelium Classification using Fine-tuned Deep CNN Models

The field of Digital Pathology (DP) has become more interested in automated tissue phenotyping in recent years.Tissue phenotyping may be used to identify colorectal cancer (CRC) and distinguish various cancer types.The information needed to construct automated tissue phenotyping systems has been made available by the introduction of Whole Slide Images (WSIs).One of the typical pathological diagnosis duties for pathologists is the histopathological categorization of epithelial tumors.Artificial intelligence (AI) based computational pathology approaches would be extremely helpful in reducing the pathologists ever-increasing workloads, particularly in areas where access to pathological diagnosis services is limited.Investigating several deep learning models for categorizing the images of tumor epithelium from histology is the initial goal.The varying accuracy ratings that were achieved for the deep learning models on the same database demonstrated that additional elements like pre-processing, data augmentation, and transfer learning techniques might affect the models' capacity to attain better accuracy.The second goal of this publication is to reduce the time taken to classify the tissue and tumor Epithelium.The final goal is to examine and fine-tune the most recent models that have received little to no attention in earlier research.These models were checked by the histology Kather CRC image database's nine classifications (CRC-VAL-HE-7K, NCT-CRC-HE-100K).To identify and recommend the most cutting-edge models for each categorization, these models were contrasted with those from earlier research.The performance and the achievements of the proposed preprocessing workflow and fine-tuned Deep CNN models (Alexnet, GoogLeNet and Inceptionv3) are greater compared to the prevalent methods.
Lecturers’ Challenges and Strategies in Teaching Maritime English Online to Students with Low English Proficiency

This study aimed to investigate the problems faced by ESP lecturers when teaching low English proficiency students during online Maritime English lessons and their strategies in dealing with the problems. A questionnaire with six open-ended questions was distributed to 9 Maritime English lecturers from 6 different Maritime Polytechnics in Indonesia. A follow-up interview was conducted with four lecturers to obtain more detailed information regarding their online teaching practices, especially the difficulties and the strategies to mitigate them. The findings of this study revealed 3 (three) main problems faced by the lecturers. Firstly, students were reluctant to interact and participate in class activities. Secondly, students showed low motivation and interest in learning English. Thirdly, the lecturers needed extra time to explain the teaching materials. To overcome the problems, the lecturers used various texts, pictures, videos, or online platforms to expose students to maritime vocabulary. They also grouped the lower proficiency students with the higher proficiency students in a collaborative activity, designed class activities based on students’ learning styles, and employed various scaffolding techniques. Keywords: English for Specific Purposes (ESP) teaching, low English proficiency, Maritime English, online learning.
Leveraging Adversarial Examples to Quantify Membership Information Leakage

The use of personal data for training machine learning systems comes with a privacy threat and measuring the level of privacy of a model is one of the major challenges in machine learning today. Identifying training data based on a trained model is a standard way of measuring the privacy risks induced by the model. We develop a novel approach to address the problem of membership inference in pattern recognition models, relying on information provided by adversarial examples. The strategy we propose consists of measuring the magnitude of a perturbation necessary to build an adversarial example. Indeed, we argue that this quantity reflects the likelihood of belonging to the training data. Extensive numerical experiments on multivariate data and an array of state-of-the-art target models show that our method performs comparable or even outperforms state-of-the-art strategies, but without requiring any additional training samples.
Decentralized IoT Resource Monitoring and Scheduling Framework Based on Blockchain

With the continuous advancement of edge intelligence, edge servers undertake more and more intelligent computing tasks. Nowadays, there are a large number of IoT devices in the network in idle state. For instance, the mining process for consensus of miners in blockchain such as Bitcoin causes a waste of computing resources and energy. A natural question arises: can we couple the idle computing resources of network devices to continuously and credibly share the burden of edge intelligent computing tasks in a secure manner? The answer of this paper is yes. We propose a blockchain-based IoT resource monitoring and scheduling framework that supports resource management and trusted edge computing. We analyze the security threats in all phases of distributed edge computing, and utilize the trusted computing and public verifiability features of blockchain to ensure reliability and fairness in the trusted measurement of device computing power, the decomposition of intelligent computing tasks, the matching of task and computing power, and the verification of computing result. Finally, we implement a simulation on the edge network by performing distributed machine learning task for weather prediction, and the simulation results demonstrate the availability of our scheme.
GAN-Based Satellite Imaging: A Survey on Techniques and Applications

Satellite image analysis is widely used in many real-time applications, from agriculture to the military. Due to the wide range of Generative Adversarial Network (GAN) applications in multiple areas of satellite imaging, a comprehensive review is required in this area. This paper takes the first step in this direction by categorizing the GAN-based satellite imaging research using seven considerations. We discuss not only the challenges but also future research trends and directions. Among the major findings, we have observed increasing componentization and modularization of GANs to be used as elements of larger systems. In addition to the GAN types used exclusively in each application, we demonstrate the deep neural network architectures used as the generator structure. Eventually, we summarize the results and evaluate the significant impact of GANs on improving performance compared to traditional approaches.
Tair-PMem

In-memory databases (IMDBs) have been the backbone of modern systems that demand high throughput and low latency. Because of the cost and volatility of DRAM, IMDBs become incompetent when dealing with workloads that require large data volume and strict durability. The emergence of non-volatile memory (NVM) brings new opportunities for IMDBs to tackle this situation. However, it is non-trivial to build an NVM-based IMDB, due to performance degradation, NVM programming complexity, and other challenges. In this paper, we present Tair-PMem , an NVM-based enterprise-strength database atop Redis, the most popular IMDB. Tair-PMem adopts a well-controlled data layout and a log-as-user-data design to mitigate NVM overheads. It eases the NVM programming complexity by providing a hybrid memory programming toolkit. To better leverage the enterprise-strength features and implementations from Redis, Tair-PMem retrofits it in a less intrusive way to achieve full compatibility and stability, while retaining its advanced features. With all of the above techniques elaborately implemented, Tair-PMem satisfies full durability, high throughput, and low latency at the same time. Tair-PMem has now been publicly available as a cloud service on Alibaba Cloud. To the best of our knowledge, Tair-PMem is the first cloud service that makes good use of the persistence capability of NVM.
A Secret JPEG Image Sharing Method Over <i>GF</i>(2<i> <sup>M</sup> </i>) Galois Fields

With the rise of exchanges over the cloud and social networks, JPEG images have taken an important place in world image transmission and storage. In order to avoid security breaches and combat threats on the Internet, numerous JPEG image security methods have been proposed in both the academic and industrial communities. Encryption methods have been specifically designed to make JPEG images visually secure using so called crypto-compression methods. The drawback of crypto-compression is that it depends on only one secret key. Indeed, if this key is lost, so is the totality of the content of the secret original image. In this paper, we propose a secret JPEG image sharing approach. Shamir’s secret sharing scheme over Galois fields is used during the JPEG Huffman coding step, ensuring the visual security of the secret image in the compressed domain, while solving the issue of secret key loss. In addition, we also describe an eco-friendly scenario, dealing with a public shared JPEG image. In this scenario, we can obtain compressed shares because there is no need to duplicate the redundant information. According to our obtained results, our approach is fully format compliant and size preserving when compared to a standard JPEG compression of a secret original image, while ensuring the visual security of the content of the secret original image.
Embracing the Metaverse: Mechanism and logic of a new digital economy

&lt;p&gt;The Metaverse refers to virtual world and survival vision in which human beings will be more deeply engaged. It is initiated in game platforms and based on the innovation and circulation of digital currency and dependent on the development of digital technology and devices. The Metaverse craze hit China in 2021 for various reasons, such as the systematic change of the Internet protocol due to the development of block chain technology, more involvement in virtual life due to the COVID-19 pandemic, the aggravated crisis of old global narrative, and new patterns emerging in digital economy following PC digital economy and mobile digital economy. The Metaverse economy is a new digital economy different from the previous digital economies due to the emergence of digital currency along with the development of digital legal currency, and a series of new production and business models around digital assets. What underlies this new digital economy is the fact that digital currency is no longer controversial cryptocurrency and that several central banks have agreed to issue digital legal currency. The new digital assets in the form of NFT or NFR backed by the distributed ledger technology will bring a variety of reforms in business models and big upheavals in production, circulation, and consumption patterns. The interaction between digital and physical assets will create new economies. In the Metaverse economy where recognition determines value, in terms of technology it is possible to capitalize on the values such as cultural value, historical value and aesthetic value. This will give rise to more diverse economies and expand global economy.&lt;/p&gt;
An Ontology of Security from a Risk Treatment Perspective

In Risk Management, security issues arise from complex relations among objects and agents, their capabilities and vulnerabilities, the events they are involved in, and the value and risk they ensue to the stakeholders at hand. Further, there are patterns involving these relations that crosscut many domains, ranging from information security to public safety. Understanding and forming a shared conceptualization and vocabulary about these notions and their relations is fundamental for modeling the corresponding scenarios, so that proper security countermeasures can be devised. Ontologies are instruments developed to address these conceptual clarification and terminological systematization issues. Over the years, several ontologies have been proposed in Risk Management and Security Engineering. However, as shown in recent literature, they fall short in many respects, including generality and expressivity - the latter impacting on their interoperability with related models. We propose a Reference Ontology for Security Engineering (ROSE) from a Risk Treatment perspective. Our proposal leverages on two existing Reference Ontologies: the Common Ontology of Value and Risk and a Reference Ontology of Prevention, both of which are grounded on the Unified Foundational Ontology (UFO). ROSE is employed for modeling and analysing some cases, in particular providing clarification to the semantically overloaded notion of Security Mechanism.
Publication Venue Recommendation Using Profiles Based on Clustering

In this paper, we study the venue recommendation problem in order to help researchers identify a journal or conference to submit a given paper. A common approach for tackling this problem is to build profiles to define the scope of each venue. These profiles are then compared against the target paper. In our approach, we will study how clustering techniques can be used to construct topic-based profiles and an information retrieval-based approach be used to obtain the final recommendations. Additionally, we will explore how the use of authorship (which supplements the information) helps to improve the recommendations.
Vehicle Classification and Counting for Traffic Video Monitoring Using YOLO-v3

Traffic has been a major concern in most of the cities. Monitoring cameras are used to track, detect and count vehicles in real-time to ensure proper management of traffic. Counting of vehicles like cars, trucks and two wheelers is important for Intelligent Transportation System (ITS) to identify the intensity of traffic flow. In this paper we proposed vision based vehicle classification and counting approach using YOLO- v3 framework. The proposed method is composed of steps like masking, detection, classification and counting of different classes of vehicles. We have tested proposed method over 2000 vehicles of different categories obtained from the CCTV camera installed at main gate of lIT Patna campus. Experimental results show that the proposed approach has achieved accuracy of 93.65 % and 87.68 % during daytime and nighttime respectively.
CloudJump

There has been an increasing interest in building cloud-native databases that decouple computation and storage for elasticity. A cloud-native database often adopts a cloud storage underneath its storage engine, leveraging another layer of virtualization and providing a high-performance and elastic storage service without exposing complex storage details. It helps reduce the maintenance cost and expedite development cycles for the database kernels. We have observed that there are significant differences between the local and the cloud storage that invalid many designs inside existing databases when they are ported to the cloud storage. In this paper, we analyze the challenges and opportunities of both B-tree and LSM-tree-based storage engines when they are deployed on a cloud storage. We propose an optimization framework that guides database developers to transform on-premise databases into their cloud-native counterparts. We use a B+-tree-based InnoDB as a demonstration vehicle where we have implemented a suite of optimizations using the proposed framework and extend such efforts to the LSM-tree-based RocksDB. On both engines, our evaluations show significant performance improvements on the cloud storage.
Multi-Module Convolutional Neural Network Based Optimal Face Recognition with Minibatch Optimization

Technology is getting smarter day by day and facilitating every part of human life from automatic alarming, automatic temperature, and personalised choice prediction and behaviour recognition.Such technological advancements are using different machine learning techniques for artificial intelligence.Face recognition is also one of the techniques to develop futuristic artificial intelligence-based technology used to get devices equipped with personalised features and security.Face recognition is also used for keeping information of facial data of employees of any company citizens of any country to get tracked and control over crimes in unfair incidents.For making face recognition more reliable and faster, several techniques are evolving every day.One of the fastest and most dependable face recognitions is CNN based face recognition.This work is designed based on the multiple convolutional module-based CNN equipped with batch normalisation and linear rectified unit for normalising and optimising features with minibatch.Faces in CNN's fully connected layer are classified using the SoftMax classifier.The ORL and Yale face datasets are used for training.The average accuracy achieved is 94.74% for ORL and 96.60% for Yale Datasets.The convolutional neural network training was done for different training percentages, e.g., 66%, 67%, 68%, 69%, 70%, and 80%.The experimental outcomes exhibited that the defined approach had enhanced the face recognition performance.
Evaluation of artificial neural network methods to forecast short-term solar power generation: a case study in Eastern Mediterranean Region

Solar power forecasting is substantial for the utilization, planning, and designing of solar power plants.Global solar irradiation (GSI) and meteorological variables have a crucial role in solar power generation.The ever-changing meteorological variables and imprecise measurement of GSI raise difficulties for forecasting photovoltaic (PV) output power.In this context, a major motivation appears for the accurate forecast of GSI to perform effective forecasting of the short-term output power of a PV plant.The presented study comprises of four artificial neural network (ANN) methods; recurrent neural network (RNN) method, feedforward backpropagation neural network (FFBPNN) method, support vector regression (SVR) method, and long short-term memory (LSTM) for daily total GSI prediction of Tarsus by using meteorological data.Moreover, this study proposes a model that utilizes the predicted daily GSI for output power forecasting of a grid-connected PV plant.The obtained results are compared with the output power generation data of a 350 kW solar power plant.The results are evaluated with the performance indices as mean absolute percentage error (MAPE), normalized root mean squared error (NRMSE), weighted mean absolute error (WMAE), and normalized mean absolute error (NMAE).FFBPNN method is chosen with the best results of MAPE 7.066%, NMAE 3.629%, NRMSE 4.673%, and WMAE 5.256%.
RoSummary: Control Tokens for Romanian News Summarization

Significant progress has been achieved in text generation due to recent developments in neural architectures; nevertheless, this task remains challenging, especially for low-resource languages. This study is centered on developing a model for abstractive summarization in Romanian. A corresponding dataset for summarization is introduced, followed by multiple models based on the Romanian GPT-2, on top of which control tokens were considered to specify characteristics for the generated text, namely: counts of sentences and words, token ratio, and n-gram overlap. These are special tokens defined in the prompt received by the model to indicate traits for the text to be generated. The initial model without any control tokens was assessed using BERTScore (F1 = 73.43%) and ROUGE (ROUGE-L accuracy = 34.67%). Control tokens improved the overall BERTScore to 75.42% using &lt;LexOverlap&gt;, while the model was influenced more by the second token specified in the prompt when performing various combinations of tokens. Six raters performed human evaluations of 45 generated summaries with different models and decoding methods. The generated texts were all grammatically correct and consistent in most cases, while the evaluations were promising in terms of main idea coverage, details, and cohesion. Paraphrasing still requires improvements as the models mostly repeat information from the reference text. In addition, we showcase an exploratory analysis of the generated summaries using one or two specific control tokens.