Reviewerly/exHarmony huggingface dataset:
Overview and research progress of no reference image quality evaluation methods

Abstract Image quality will greatly affect the acquisition of human visual information, but the image will inevitably cause a certain degree of distortion in the process of transmission, compression, coding and so on. At this time, the accuracy of image quality evaluation (IQA) is particularly important, which also makes IQA an important research direction in the field of image processing. IQA methods are divided into subjective image quality evaluation method and objective image quality evaluation method.there are many subjective evaluation methods, and the international telecommunications union (ITU) has also proposed many subjective evaluation methods and standards. there are three most commonly used methods, namely, the double stimulation continuous quality classification method, the single stimulation continuous quality classification method and the double stimulation damage classification method. Correspondingly, there are three kinds of objective evaluation methods, full reference, half reference and no reference. At present, this paper selects the research focus of IQA field-no reference image quality evaluation method as the main research object, summarizes the advantages and disadvantages of each non-reference evaluation method, and prospects the future development of the non-reference image quality evaluation method.
An energy-efficient lightweight security protocol for optimal resource provenancein wireless sensor networks

Security of resource sharing and provenance is a major concern in wireless sensor networks (WSNs), where the intruders can easily inject malicious intermediate nodes for various personal gains.This selective forwarding attack may reduce the flow of resource sharing and throughput in the network.Most of the existing techniques are complex and do not provide sufficient security to sensor nodes with low energy.This paper proposes an energy-efficient and lightweight security protocol for optimal resource provenance in multihop WSNs and the Internet of things (IoT) network.The sharing of the resources between the sensor nodes indicates the strength of the mutual cooperation between the nodes, which will act as a link to generate a cooperative correlating coefficient in the proposed security protocol.The cooperative correlating coefficient is computed and matched at the source and destination sensor node, which is broadcast to all the connecting intermediate sensor nodes.A higher value of the cooperative correlating coefficient indicates stronger and secure resource sharing between the sensor nodes with optimal resource provenance, and the lower value indicates the possibility of the presence of adversarial sensor nodes between the hops.We calculate the energy dissipated for the sensor nodes and the complete sensor network.The real-time cooperative correlating coefficient values are derived from the cooperation of two USRPs.The simulation for the detection of adversarial sensor node and resource provenance is done using MATLAB.The experimental result demonstrates secure resource provenance sharing in a sensor network with high energy efficiency compared to the existing techniques.
An Approach for Seasonally Periodic Frequent Pattern Mining in Retail Supermarket

Periodic-Frequent pattern mining is an empirical data mining technique that is used to investigate the customer purchase behavior pattern in the retail market. It provides the most extreme advantages to a retailer for product placements, product segmentation, store layout, selling strategies and different merchandising decisions to improve customer satisfaction as well as increase the sales. Traditionally, the single minimum support based frequent pattern mining approaches missed many lower supported items for high support threshold value, and on the other sites, the number of frequent patterns explode for low support threshold value. The retail market is highly seasonal, a number of items those are not periodic-frequent at all but those are periodic and frequent at a specific season, as results a huge number of seasonal periodic-frequent itemsets are remains out of reach. To resolve this problem, we propose an approach for extracting seasonally periodic-frequent patterns with user specified minimum support and maximum periodicity thresholds on multilevel retail supermarket dataset in this paper. The dataset has been created by collecting transaction slips in a Supermarket of Bangladesh. The results of our proposed approach extract many hidden seasonal patterns in transactional database for easy merchandising decisions and it shows the superiority over the traditional systems.
Pengembangan Permainan Ular Tangga Keyakinan dalam Peningkatan Efikasi Diri Siswa SMP

&lt;p&gt;The purpose of this research is to develop a game media of Snakes and Ladder of Faith which is effective for increasing the self-efficacy of students at SMP Negeri 2 Krian. This research and development method uses a research and development procedure with the following steps: 1) preliminary study, 2) media development, and 3) media testing. Based on the material expert's assessment, an average score of 0.46 was obtained which was categorized as quite feasible. Based on the media expert's assessment, an average score of 0.69 was obtained with the feasible category. The results of the user test assessment obtained results of 92.3% with the very valid category or can be used without revision. Based on the results of the limited field test, it was obtained that tcount 4.058&amp;gt; ttable 3.182 and Sig (2tailed) 0.027 &amp;lt;0.05, so that H&lt;sub&gt;0&lt;/sub&gt; was rejected and H&lt;sub&gt;a&lt;/sub&gt; was accepted. So it can be concluded that the game media, Snake and Ladder of Faith, is effective for increasing student self-efficacy.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;/strong&gt;&lt;strong&gt;&lt;br /&gt;&lt;/strong&gt;&lt;/p&gt;
Artificial intelligence-based models for augmenting media reporting of suicide: challenges and opportunities

The sensationalised and harmful content of media reporting of suicide is a modifiable risk factor for suicide and suicidal behaviour.Although the World Health Organization (WHO) has published guidelines for responsible media reporting of suicide to prevent suicide contagion, the uptake of these recommendations across media outlets remains limited due to several barriers such as the motivation of stakeholders, inadequate training of media personnel, and a lack of real-time monitoring by the government.In this report, we suggest that artificial intelligence (AI) based models, can be used to address barriers to guideline adherence and improve the quality of media reporting.It is our understanding that the development and implementation of AI-based models or tools can assist in improving adherence to suicide reporting guidelines.We propose a hybrid model that incorporates steps that can be taken at different levels of the media news communication cycle.The algorithmic approach can help in simultaneously processing large amounts of data while also facilitating the design of article structures and placement of key information recommended by media reporting guidelines.The potential benefits of the AI-based model to the various stakeholders and the challenges in implementation are discussed.Given the positioning of responsible media reporting of suicide as a key population-level suicide prevention strategy, efforts should be made to develop and evaluate AI-based models for improving the quality of media reporting in different national or international settings.
A Deep Learning-Based Object Detection System for Blind People

Visual impairment is one of the top disabilities among men and women across the world of all ages. Object detection is the primary task for them, and it can be implemented by deep learning techniques. Earlier implementation techniques involve in object detection with a strategy of single labeling. The proposed model uses classification techniques which reduce the recognize time of multi-objects with best time complexities and can help the visually impaired people in assisting the accurate navigation, in both indoor and outdoor circumstances. The proposed hybrid model is a combination of U-Net with base as residual network (ResNet) which improves accuracy in detection of objects in indoor and outdoor for visually impaired people.
Optimization of the Hybrid Movie Recommendation System Based on Weighted Classification and User Collaborative Filtering Algorithm

Aiming at the problem that the single model of the traditional recommendation system cannot accurately capture user preferences, this paper proposes a hybrid movie recommendation system and optimization method based on weighted classification and user collaborative filtering algorithm. The sparse linear model is used as the basic recommendation model, and the local recommendation model is trained based on user clustering, and the top-N personalized recommendation of movies is realized by fusion with the weighted classification model. According to the item category preference, the scoring matrix is converted into a low-dimensional, dense item category preference matrix, multiple cluster centers are obtained, the distance between the target user and each cluster center is calculated, and the target user is classified into the closest cluster. Finally, the collaborative filtering algorithm is used to predict the scores for the unrated items of the target user to form a recommendation list. The items are clustered through the item category preference, and the high-dimensional rating matrix is converted into a low-dimensional item category preference matrix, which further reduces the sparsity of the data. Experiments based on the Douban movie dataset verify that the recommendation algorithm proposed in this article solves the shortcomings of a single algorithm model to a certain extent and improves the recommendation effect.
Enhancement of photon detection in superconducting nanowire single photon detector exposed to oscillating magnetic field

We measured the photon count rate (PCR) and dark count rate (DCR) of a superconducting nanowire single photon detector (SNSPD) exposed to either a DC magnetic field (up to 60 mT) or to a low-amplitude oscillating field (0.12–0.48 mT, up to 50 kHz). In both cases, the results show an increase in the PCR and the DCR as the DC field or the frequency of the AC field increase. However, the ratio DCR/(PCR + DCR) increases significantly with an increasing DC field, whereas this ratio is approximately constant as the frequency of the AC field increases up to frequencies in the kHz regime. The results suggest a more favorable effect of AC fields on the operation of the SNSPD.
Advances in synthetic biology and regulatory policy of the state

Synthetic biology is a new interdisciplinary area of research applies the principles of engineering design in genetic and biomedical research. Progress in the development of tools used to modify natural biological systems (or used for creation of new ones) has allowed the development of traditional biomedicine and genomics. At the beginning of this review article, we look at the key achievements of synthetic biology over the past ten years. Despite the fact that the contribution of synthetic biology to fundamental research in the field of biology, medicine, ecology and other life sciences is undeniable, the potential risks for biological safety and security associated with the further development synthetic biology also require attention. This problem has become particularly relevant in recent years, as the technology becomes less expensive, more advanced and affordable. A brief assessment of the risks associated with the possible misuse or abuse of synthetic biology technologies is given from the point of view of danger to humanity and nature as a whole. To solve the problems arising due to the rapid progress of synthetic biology, technical, ethical and legal measures have been developed and planned.
Analyzing The Impact of the Digital Economy on Development in the Arab Countries : A Study on Some Arab Countries

The major transformations has occurred in the economic system in general due to the rapid and integrated growth in the information and communications technology sector.This sector has reached the peak in merge the integration of computer and communications technology, which is one of the results of this merge and the scientific and technological transformation.The emergence of the digital economy society or the new economy (so-called Internet economy).One of the most prominent forms of this economy is electronic commerce, electronic banking and electronic management.Unlike other economic sectors that depend on natural resources, it became obligatory for all countries to keep pace with the developed countries that have made great strides in the digital world by increasing spending on research , development and , human resource projects.This is in addition to the increase of their knowledge capabilities and infrastructure.As the digital economy has become one of the factors involved in the equation of economic growth , the strength of nations is not only seen in terms of its possession of economic wealth, but also in the amount of its possession of information and its ability to use this information in its real economic activities.
A Session Subtyping Tool

Session types are becoming popular and have been integrated in several mainstream programming languages. Nevertheless, while many programming languages consider asynchronous fifo channel communication, the notion of subtyping used in session type implementations is the one defined by Gay and Hole for synchronous communication. This might be because there are several notions of asynchronous session subtyping, these notions are usually undecidable, and only recently sound (but not complete) algorithmic characterizations for these subtypings have been proposed. But the fact that the definition of asynchronous session subtyping and the theory behind related algorithms are not easily accessible to non-experts may also prevent further integration. The aim of this paper, and of the tool presented therein, is to make the growing body of knowledge about asynchronous session subtyping more accessible, thus promoting its integration in practical applications of session types.
A First Look: Towards Explainable TextVQA Models via Visual and Textual Explanations

Explainable deep learning models are advantageous in many situations.Prior work mostly provide unimodal explanations through posthoc approaches not part of the original system design.Explanation mechanisms also ignore useful textual information present in images.In this paper, we propose MTXNet, an end-toend trainable multimodal architecture to generate multimodal explanations, which focuses on the text in the image.We curate a novel dataset TextVQA-X, containing ground truth visual and multi-reference textual explanations that can be leveraged during both training and evaluation.We then quantitatively show that training with multimodal explanations complements model performance and surpasses unimodal baselines by up to 7% in CIDEr scores and 2% in IoU.More importantly, we demonstrate that the multimodal explanations are consistent with human interpretations, help justify the models' decision, and provide useful insights to help diagnose an incorrect prediction.Finally, we describe a real-world ecommerce application for using the generated multimodal explanations.
Efficient Iterative Amortized Inference for Learning Symmetric and Disentangled Multi-Object Representations

Unsupervised multi-object representation learning depends on inductive biases to guide the discovery of object-centric representations that generalize. However, we observe that methods for learning these representations are either impractical due to long training times and large memory consumption or forego key inductive biases. In this work, we introduce EfficientMORL, an efficient framework for the unsupervised learning of object-centric representations. We show that optimization challenges caused by requiring both symmetry and disentanglement can in fact be addressed by high-cost iterative amortized inference by designing the framework to minimize its dependence on it. We take a two-stage approach to inference: first, a hierarchical variational autoencoder extracts symmetric and disentangled representations through bottom-up inference, and second, a lightweight network refines the representations with top-down feedback. The number of refinement steps taken during training is reduced following a curriculum, so that at test time with zero steps the model achieves 99.1% of the refined decomposition performance. We demonstrate strong object decomposition and disentanglement on the standard multi-object benchmark while achieving nearly an order of magnitude faster training and test time inference over the previous state-of-the-art model.
A gravitational search algorithm based on levy flight

Abstract A gravitational search algorithm based on levy flight is proposed to solve the problem that the gravitational search algorithm falls into the local optimal solution. Firstly, the dynamic adjustment strategy of gravitational constant is introduced to increase the diversity of particles and improve the convergence speed and accuracy of the algorithm. Then, on the basis of gravitation search algorithm, levy flight is introduced, and the particle search range is increased by using levy flight search mode combining small step and long stride search, so as to improve the global search capability of the algorithm. Finally, six standard test functions are used for simulation analysis. The results show that the improved algorithm has obvious advantages in both convergence speed and convergence precision, and it is more advantageous in multi-peak function optimization.
Could COVID-19 be a Catalyst for Disruption in Higher Education?

Advances in digital technologies have already transformed numerous industries, leading scholars to predict that online learning technologies would cause similar disruption in higher education. While these predictions have yet to be realized, will the COVID-19 pandemic change the situation? This conceptual chapter draws from the authors’ complementary expertise in entrepreneurship, pedagogy, business, and higher education organizational theory to address this question from complementary perspectives. While highlighting the potential of the COVID-19 pandemic to accelerate disruption for higher education, the results of this analysis also help us better understand the potential implications of the continuing growth of online learning. Implications for universities include leveraging the current experience to inform innovations that could potentially strengthen their institutions and lead them to thrive in the future.
Dynamic Prime Chunking Algorithm for Data Deduplication in Cloud Storage

The data deduplication technique identifies the duplicates and minimizes the redundant storage data in the backup server.The chunk level deduplication plays a significant role in detecting the appropriate chunk boundaries, which solves the challenges such as minimum throughput and maximum chunk size variance in the data stream.To provide the solution, we propose a new chunking algorithm called Dynamic Prime Chunking (DPC).The main goal of DPC is to dynamically change the window size within the prime value based on the minimum and maximum chunk size.According to the result, DPC provides high throughput and avoid significant chunk variance in the deduplication system.The implementation and experimental evaluation have been performed on the multimedia and operating system datasets.DPC has been compared with existing algorithms such as Rabin, TTTD, MAXP, and AE.Chunk Count, Chunking time, throughput, processing time, Bytes Saved per Second (BSPS) and Deduplication Elimination Ratio (DER) are the performance metrics analyzed in our work.Based on the analysis of the results, it is found that throughput and BSPS have improved.Firstly, DPC quantitatively improves throughput performance by more than 21% than AE.Secondly, BSPS increases a maximum of 11% than the existing AE algorithm.Due to the above reason, our algorithm minimizes the total processing time and achieves higher deduplication efficiency compared with the existing Content Defined Chunking (CDC) algorithms.
The Development of ARCS-Based Economic E-Book Media for Senior High School

This study aimed to determine the appropriateness of ARCS-based economic e-books on international trade material in the class XI IPS of Senior High School in terms of experts' assessments and students' responses to e-book products developed. This study used the Borg &amp; Gall development model which was modified into 10 stages, namely: 1) research and information gathering; 2) planning; 3) early e-book product development; 4) initial trials; 5) revision I; 6) main trials; 7) revision II; 8) operational trials; 9) revision III; 10) dissemination. The research was conducted in class XI of Senior High School in Surakarta. Data collection methods in this study used validation sheets and questionnaire sheets which were analyzed descriptively. The results showed the feasibility of an economic e-book based on ARCS qualified "very feasible" for the media aspect with a percentage of 93,75% and the material aspect with a percentage of 88,09% according to experts, and linguist with a percentage of 100% and received a positive response from students with a percentage of 76,11%.
Analisis Pengaruh E-Learning dan Kesiapan Belajar Terhadap Minat Belajar Melalui Motivasi Belajar Sebagai Variabel Intervening Mahasiswa Program Beasiswa FLATS di Surabaya pada Masa Pandemi Covid-19

This study aims to analyze the influence of e-learning and learning readiness on learning interests through learning motivation as an intervening variable for flats scholarship students in Surabaya during the Covid-19 pandemic. The method used in this study is quantitative and the type of research is associative. The population of this study is flats scholarship students in Surabaya with a sample of 70 people. This research instrument uses questionnaires. Techniques for analyzing the data using path analysis or path analysis through the SmartPLS 3.0 program. The results of this study show that: 1) there is a significant influence between e-learning and student learning motivation; 2) there is an insignificant influence between e-learning and student learning interests; 3) there is a significant influence between learning readiness and student learning motivation; 4) there is a significant influence between learning readiness and student learning interests; 5) there is a significant influence between learning motivation and student learning interests; 6) there is an insignificant influence between e-learning and the interest in learning through student learning motivation; and 7) there is a significant influence between learning readiness and learning interests through student learning motivation.
Controlled Random Walk: Conjecture and Counter-Example

In this paper we investigate the following conjecture about the random walk on the positive integer lattice, starting from a large point \(i>0\) and up to the absorption at negative points: on the first steps, one has to maximize the expected reward coming from passing through one point on the lattice. Under appropriate conditions, this conjecture is true. The counter-example shows that sometimes it is not valid.