Reviewerly/exHarmony huggingface dataset:
A self-adaptive single underwater image restoration algorithm for improving graphic quality

Abstract A high-quality underwater image is essential to many industrial and academic applications in the field of image processing and analysis. Unfortunately, underwater images frequently demonstrate poor visual quality of low contrast, blurring, darkness, and color diminishing. This paper develops a new underwater image restoration framework that consists of four major phases: color correction, local contrast enhancement, haze diminution, and global contrast enhancement. A self-adaptive mechanism is designed to guide the image to either processing route based on a red deficiency measure. In the color correction phase, the histogram in each RGB channel is transformed for balancing the image color. An adaptive histogram equalization method is exploited to enhance the local contrast in the CIE-Lab color space. The dark channel prior haze removal scheme is modified for dehazing in the haze diminution phase. Finally, a histogram stretching method is applied in the HSI color space to make the image more natural. A wide variety of underwater images with various scenarios were employed to evaluate this new restoration algorithm. Experimental results demonstrated the effectiveness of our image restoration scheme as compared with state-of-the-art methods. It was suggested that our framework dramatically eliminated the haze and improved visual interpretation of underwater images.
A Hybrid Knowledge-Based and Collaborative Filtering Recommender System Model for Recommending Interventions to Improve Elderly Wellbeing

A recommender system is an information filtering system that helps users select items that most match their preferences from a vast amount of information available.It has been widely applied in many domains such as e-commerce, healthcare, entertainment and so on.Currently, there are some efforts have been done for recommending interventions to improve elderly well-being in different aspects of successful ageing.However, the recommendations are focused on only a single aspect of successful ageing such as nutrition or health.There are many aspects of successful ageing that should be considered when given interventions to improve elderly wellbeing namely socialization, health, physical, cognitive, nutrition, spirituality and environment.This paper aims to propose a Hybrid Knowledge-Based and Collaborative Filtering (KBCF) recommendation model to recommend interventions based on multiaspects of successful ageing in order to improve the elderly wellbeing.The Knowledge-based (KB) recommendations are generated by consulting a knowledge base which is developed based on knowledge provided by the domain experts.The Collaborative Filtering (CF) approach is applied to find similar users based on elderly profiles generated from the result of assessments done to the elderly.The results of the KB recommendations and the CF recommendations are integrated and ranked to select the final recommendations.The result of experiments conducted using precision, recall and F1 measure shows that the proposed KBCF model outperforms the baseline models which are Basic Search, Collaborative Filtering and Knowledge-based.This result demonstrates the proposed KBCF model provides more accurate and meaningful intervention recommendations for improving the elderly wellbeing in multiaspects of successful ageing.
Estimating the Complexity of Architectural Design Decision Networks

The stability and longevity of software systems rely on the quality of design decisions over time. In modern software-intensive systems the number of design decisions taken, the dependencies between those decisions, and the number of design alternatives considered, complicate software maintenance and jeopardize the system's longevity. Despite the existence of complexity metrics applied to code, there is a lack of metrics for design decisions. As estimating the complexity of a set of design decisions is needed for understanding the difficulty of software evolution, this paper proposes and validates a new metric to estimate the complexity of decision networks. The metric is based on decision topologies and provides a way to understand the complexity of decision sets and reason about the maintenance difficulty. We validate our metric empirically in two different ways: (i) evaluating the complexity of two service-based platform systems, and (ii) analyzing the evolution of complexity in four open-source projects and compare how the evolution of complexity affects to the architecture in one of the open-source projects. Our results show that certain network topologies are more difficult to maintain, so we provide a set of tactics to reduce the complexity of design decision networks.
SuccinctEdge

As edge computing is becoming a new platform for rich applications and services, it becomes more and more important to design adapted data management systems for this environment. In this paper, we present a prototype corresponding to a compact, in-memory RDF store that can answer SPARQL queries requiring reasoning services without necessitating any decompression. This demonstration highlights a design based on succinct data structures, shows some implementation details and provides encouraging performance measures over a set of real-world and synthetic data and query sets.
Private Function Evaluation Using Intel’s SGX

Private Function Evaluation (PFE) is the problem of evaluating one party’s private data using a private function owned by another party. Existing solutions for PFE are based on universal circuits evaluated in secure multiparty computations or on hiding the circuit’s topology and the gate’s functionality through additive homomorphic encryption. These solutions, however, are not efficient enough for practical use; hence there is a need for more efficient techniques. This work looks at utilizing the Intel Software Guard Extensions platform (SGX) to provide a more practical solution for PFE where the privacy of the data and the function are both preserved. Notably, our solution carefully avoids the pitfalls of side-channel attacks on SGX. We present solutions for two different scenarios: the first is when the function’s owner has an SGX-enabled device and the other is when a third party (or one of the data owners) has the SGX capability. Our results show a clear expected advantage in terms of running time for the first case over the second. Investigating the slowdown in the second case leads to the garbling time which constitutes more than 60% of the consumed time. Both solutions clearly outperform FairplayPF in our tests.
LTAI Management Based on Blockchain Technology to Increase Alexa Rank

The development of technology and information that is taking place today has an impact on the creation of many online community websites that circulate, providing various kinds of information that are served and packaged in as attractive a form as possible, such as the Alphabet Incubator Website.Alphabet Incubator has a lot of information contained in the use of the blockchain in LTAI's schedule management.The Alphabet Incubator website is a corporate creative preneur-based startup community website.Alphabet Incubator has a goal with a very high potential to manage the increased traffic rank of the Alphabet Incubator website as seen from its LTAI schedules.Blockchain research in recent years has produced a need to conduct research studies.As this research goes on, there are 3 (three) problems that are being faced regarding the management of increasing Alphabet Incubator website traffic rank by visiting the Alphabet Incubator website to make LTAI schedule management.In the stage of improving quality, blockchain is utilized in the LTAI schedule on the Alphabet Incubator website.To measure the traffic rank of the Alphabet Incubator website and to support the development of the Alphabet Incubator, the implementation is carried out by using the blockchain on the LTAI schedule that has been made by students.Resulting in blockchain utilization activities on the LTAI schedule and for the management of increasing Alphabet Incubator website rankings using Alexa Rank.
Approximate spectral clustering using both reference vectors and topology of the network generated by growing neural gas

Spectral clustering (SC) is one of the most popular clustering methods and often outperforms traditional clustering methods. SC uses the eigenvectors of a Laplacian matrix calculated from a similarity matrix of a dataset. SC has serious drawbacks: the significant increases in the time complexity derived from the computation of eigenvectors and the memory space complexity to store the similarity matrix. To address the issues, I develop a new approximate spectral clustering using the network generated by growing neural gas (GNG), called ASC with GNG in this study. ASC with GNG uses not only reference vectors for vector quantization but also the topology of the network for extraction of the topological relationship between data points in a dataset. ASC with GNG calculates the similarity matrix from both the reference vectors and the topology of the network generated by GNG. Using the network generated from a dataset by GNG, ASC with GNG achieves to reduce the computational and space complexities and improve clustering quality. In this study, I demonstrate that ASC with GNG effectively reduces the computational time. Moreover, this study shows that ASC with GNG provides equal to or better clustering performance than SC.
Image Steganography by Deep CNN Auto-Encoder Networks

The existing traditional image steganography methods often adopt the selection and mapping approaches.Among all the pixels of the cover image, only those which have the portability of incorporating the secret bits without noticeable distortion are chosen.This results to small integration capacity.In this paper, we propose a generic system of image steganography that uses the architecture of auto-encoding networks based on end to end trained deep Convolutional Neural Networks to ensure the process of concealment and extraction.The trained network includes two sub-networks, one for hiding used by the sender to encode a color image in another of the same size.The other for extraction, used by the recipient to retrieve the secret image from the received stego image.To validate our system, we carried out several tests on a range of challenging images dataset publicly available such as ImageNet, CIFAR10, LFW, PASCAL-VOC12.Results show that the proposed method is generic regardless the source of the images used and solves the problem of capacity at acceptable PSNR and SSIM values.
Modelling and Planning Evolution Styles in Software Architecture

The purpose of this study is to find the right model to plan and predict future evolution paths of an evolving software architecture based on past evolution data. Thus, in this paper, a model to represent the software architecture evolution process is defined. In order to collect evolution data, a simple formalism allowing to easily express software architecture evolution data is introduced. The sequential pattern extraction technique is applied to the collected evolution styles of an evolving software architecture in order to predict and plan the future evolution paths. A learning and prediction model is defined to generate the software architecture possible future evolution paths. A method for evaluating the generated paths is presented. In addition, we explain and validate our approach through a study on two examples of evolution of component-oriented software architecture.
Constructive Graph Tasks in Distant Contests

The paper discusses a certain type of competitions based on distance interaction of a participant with simulation models of concepts from discrete mathematics and computer science.One of them is the "Construct, Test, Explore" (CTE) competition, developed by the authors, the other is the Olympiad in Discrete Mathematics and Theoretical Informatics (DM&TI).The tasks presented in this paper are generally devoted to the concept of a graph isomorphism.Most of the tasks are verified automatically.
Transdisciplinary Approach to Training Future Managers for Digital Education in Yakutia

The article discusses the effectiveness of a transdisciplinary approach to training future managers of Yakut education. The study is based on the analysis of Russian and foreign theoretical positions on education management and organization of teaching programs for future education managers in the context of the digital education implementation.
Technology Use by Urban Local Bodies in India to Combat the COVID-19 Pandemic

This article provides a comprehensive review of the use of information and communication technologies by urban local bodies in India in their war against the COVID-19 pandemic based on a detailed survey conducted during the pandemic period. India reported its first case of COVID-19 in late January, and government authorities have been on a war footing since then to curb the spread of the virus. Following a tradition that has been instilled within government agencies since the Modi Government came into power in 2014, local, state, and central government agencies turned to a widespread use of geospatial, surveillance and information and communication technologies as part of a strategy to monitor and track movement, manage individuals, and enforce quarantine norms. However, several important questions arise from the blind use of technology that remain unanswered. The use of technology by government agencies raise key questions on privacy, civil liberties, and suitability and viability of their use.
Optical Remote Sensing Image Registration Using Spatial-Consistency and Average Regional Information Divergence Minimization via Quantum-Behaved Particle Swarm Optimization

Due to invariance to significant intensity differences, similarity metrics have been widely used as criteria for an area-based method for registering optical remote sensing image. However, for images with large scale and rotation difference, the robustness of similarity metrics can greatly determine the registration accuracy. In addition, area-based methods usually require appropriately selected initial values for registration parameters. This paper presents a registration approach using spatial consistency (SC) and average regional information divergence (ARID), called spatial-consistency and average regional information divergence minimization via quantum-behaved particle swarm optimization (SC-ARID-QPSO) for optical remote sensing images registration. Its key idea minimizes ARID with SC to select an ARID-minimized spatial consistent feature point set. Then, the selected consistent feature set is tuned randomly to generate a set of M registration parameters, which provide initial particle warms to implement QPSO to obtain final optimal registration parameters. The proposed ARID is used as a criterion for the selection of consistent feature set, the generation of initial parameter sets, and fitness functions used by QPSO. The iterative process of QPSO is terminated based on a custom-designed automatic stopping rule. To evaluate the performance of SC-ARID-QPSO, both simulated and real images are used for experiments for validation. In addition, two data sets are particularly designed to conduct a comparative study and analysis with existing state-of-the-art methods. The experimental results demonstrate that SC-ARID-QPSO produces better registration accuracy and robustness than compared methods.
S-Paths: Set-based visual exploration of linked data driven by semantic paths

Meaningful information about an RDF resource can be obtained not only by looking at its properties, but by putting it in the broader context of similar resources. Classic navigation paradigms on the Web of Data that employ a follow-your-nose strategy fail to provide such context, and put strong emphasis on first-level properties, forcing users to drill down in the graph one step at a time. We introduce the concept of semantic paths: starting from a set of resources, we follow and analyse chains of triples and characterize the sets of values at their end. We investigate a navigation strategy based on aggregation, relying on path characteristics to determine the most readable representation. We implement this approach in S-Paths, a browsing tool for linked datasets that systematically identifies the best rated view on a given resource set, leaving users free to switch to another resource set, or to get a different perspective on the same set by selecting other semantic paths to visualize.
Evaluation of Object Tracking System using Open-CV In Python

Object Tracking System used to track the motion trajectory of an object in a video.First, I use the OpenCV's function, select ROI, to select an object on a frame and track its motion using built-in-tracker.Next, Instead of using selectROI, I use YOLO to detect an object in each frame and track them by object centroid and size comparison.Then I combine YOLO detection with the OpenCV's built in tracker by detecting the object in the first frame using YOLO and tracking them using selectROI.The video tracking is widely used in multiple purpose such as: human -computer interaction, security and surveivallence, traffic control, medical imaging, and so on.
Deep Monocular Visual Odometry for Ground Vehicle

Monocular visual odometry, with the ability to help robots to locate themselves in unexplored environments, has been a crucial research problem in robotics. Though the existed learning-based end-to-end methods can reduce engineering efforts such as accurate camera calibration and tedious case-by-case parameter tuning, the accuracy is still limited. One of the main reasons is that previous works aim to learn six-degrees-of-freedom motions despite the constrained motion of a ground vehicle by its mechanical structure and dynamics. To push the limit, we analyze the motion pattern of a ground vehicle and focus on learning two-degrees-of-freedom motions by proposed motion focusing and decoupling. The experiments on KITTI dataset show that the proposed motion focusing and decoupling approach can improve the visual odometry performance by reducing the relative pose error. Moreover, with the dimension reduction of the learning objective, our network is much lighter with only four convolution layers, which can quickly converge during the training stage and run in real-time at over 200 frames per second during the testing stage.
Classification and evaluation of digital forensic tools

Digital forensic tools (DFTs) are used to detect the authenticity of digital images. Different DFTs have been developed to detect the forgery like (i) forensic focused operating system, (ii) computer forensics, (iii) memory forensics, (iv) mobile device forensics, and (v) software forensics tools (SFTs). These tools are dedicated to detect the forged images depending on the type of the applications. Based on our review, we found that in literature of the DFTs less attention is given to the evaluation and analysis of the forensic tools. Among various DFTs, we choose SFTs because it is concerned with the detection of the forged digital images. Therefore,the purpose of this study is to classify the different DFTs and evaluate the software forensic tools (SFTs) based on the different features which are present in the SFTs. In our work, we evaluate the following five SFTs, i.e.,“FotoForensics”, “JPEGsnoop”, “Ghiro”, “Forensically”, and “Izitru”, based on different features so that new research directions can be identified for the development of the SFTs.
Research and Application of Data Privacy Protection Technology in Cloud Computing Environment Based on Attribute Encryption

With the continuous progress of Internet information technology, cloud computing has come into being. In the cloud computing environment, most applications and data are moved to the huge network data centers of cloud service providers. However, when users outsource sensitive data to cloud services, this raises a number of issues related to data security and access control. Focusing on cloud computing environment based on the weight of attribute more agencies encryption problem, this paper puts forward a cloud computing environment based on the weight of attribute more agencies encryption scheme, and by introducing concept of attribute weights, attributes authority according to the attribute importance for attributes assigned different weights. Attribute weights of attributes can be converted to set. This scheme can reflect the importance of attributes, so it has more practical significance, so as to prove the security and correctness of the cloud computing environment.
Students' Metacognitive Awareness and Reading Comprehension of Narrative Texts

ABSTRACTThis study aims to describe the relationship of students' metacognitive awareness and their reading comprehension of narrative texts by employing a correlational study design. The participants of this study were students of IPS 3 Class of SMAN 3 Bone, South Sulawesi, consisting of 25 students. They were selected as participants for this study using cluster sampling. The researchers used a questionnaire and test to collect the data from participants. The questionnaire was used to determine the students' metacognitive awareness, while the test was used to assess their reading comprehension of narrative texts. Pearson Product Moment and regression analyses were conducted to find out the correlation between the two variables. Results of this study show a significant correlation between metacognitive awareness and students' reading comprehension of narrative texts with r 0.786 &gt; r table 3.961. This research could have implications for the English teacher, students, and next researchers. ABSTRAKTujuan dari penelitian ini adalah untuk mendeskripsikan hubungan antara kesadaran metakognitif siswa dengan pemahaman bacaan mereka dalam teks naratif. Penelitian korelasional digunakan dalam penelitian ini. Sampel penelitian ini adalah siswa kelas XI IPS 3 SMAN 3 Bone. Mereka dipilih dengan menggunakan cluster random sampling yang terdiri dari 25 siswa. Peneliti menggunakan angket dan tes untuk mengumpulkan data dari sampel. Angket digunakan untuk mengetahui metakognitif siswa dan tes digunakan untuk mengetahui pemahaman bacaan siswa dalam teks naratif. Product Moment Pearson dan analisis regresi digunakan untuk mengetahui korelasi antara dua variabel. Hasil penelitian menunjukkan bahwa terdapat hubungan yang signifikan antara kesadaran metakognitif dengan kemampuan membaca siswa dalam teks naratif dengan r 0,786&gt; r tabel 3,961. Penelitian ini dapat berimplikasi pada guru bahasa Inggris, siswa, dan peneliti selanjutnya. How to Cite: Rosnaeni, Dj. M. Zuhri , Nur, H. (2020). Students' Metacognitive Awareness and Reading Comprehension of Narrative Texts. IJEE (Indonesian Journal of English Education), 7(1), 72-84. doi:10.15408/ijee.v7i1. 17027
Offensive language detection in Arabic using ULMFiT

In this paper, we approach the shared task OffenseEval 2020 by Mubarak et al. (2020) using ULMFiT Howard and Ruder (2018) pre-trained on Arabic Wikipedia Khooli (2019) which we use as a starting point and use the target data-set to fine-tune it. The data set of the task is highly imbalanced. We train forward and backward models and ensemble the results. We report confusion matrix, accuracy, precision, recall and F1 of the development set and report summarized results of the test set. Transfer learning method using ULMFiT shows potential for Arabic text classification. Mubarak, K. Darwish,W. Magdy, T. Elsayed, and H. Al-Khalifa. Overview of osact4 arabic offensive language detection shared task. 4, 2020. Howard and S. Ruder. Universal language model fine-tuning for text classification. arXiv preprint arXiv:1801.06146, 2018. Khooli. Applied data science. https://github.com/abedkhooli/ds2, 2019.