Reviewerly/exHarmony huggingface dataset:
Unsupervised Temporal Attention Summarization Model for User Created Videos

Unlike surveillance videos, videos created by common users contain more frequent shot changes, more diversified backgrounds, and a wider variety of content. The existing methods have two critical issues for summarizing user-created videos: 1) information distortion 2) high redundancy among keyframes. Therefore, we propose a novel temporal attention model to evaluate the importance scores of each frame. Specifically, on the basis of the classical attention model, we combine the predictions of both encoder and decoder to ensure using integrate information to score frame-level importance. Further, in order to sift redundant frames out, we devise a feedforward reward function to quantify diversity, representativeness, and storyness properties of candidate keyframes in attention model. Last, the Deep Deterministic Policy Gradient algorithm is adopted to efficiently solve the proposed formulation. Extensive experiments on the public SumMe and TVSum datasets show that our method outperforms the state of the art by a large margin in terms of the F-score.
Pemanfaatan Akses Aplikasi Belajar pada Proses Pembelajaran di Indonesia di Masa Pandemi Covid-19

The Covid-19 pandemic that hit Indonesia has a very significant impact on education. The spread of that virus is not only in Indonesia but throughout the world, so many learning processes run down. Educational institutions have been closed due to concerns about the Covid-19 expansion. The spread of the virus is very fast, but learning must be carried out through an online process without a face-to-face meeting. However, the digital world made the safest way to break the chain of spreading the coronavirus outbreak because getting an education is students' right without neglecting their health and mental safety. Digital technology which greatly helps the teacher using the learning media throughout applications can facilitate the relationship between teachers and students in the learning process, even though many obstacles and adjustments were made than the previous face to face learning.
Lightweight Cipher for H.264 Videos in the Internet of Multimedia Things with Encryption Space Ratio Diagnostics

Within an Internet of Multimedia Things, the risk of disclosing streamed video content, such as arising from video surveillance, is of heightened concern. This leads to the encryption of that content. To reduce the overhead and lack of flexibility arising from full encryption of the content, a good number of selective-encryption algorithms have been proposed in the last decade. Some of them have limitations, in terms of significant delay due to computational cost, or excess memory utilization, or, despite being energy efficient, do not provide a satisfactory level of confidentiality, due to their simplicity. To address such issues, this paper presents a lightweight selective encryption scheme, in which encoder syntax elements are encrypted with the innovative EXPer (EXtended Permutation with exclusive OR).&amp;nbsp; The selected syntax elements are taken from the final stage of video encoding that is during entropy coding. As a diagnostic tool, the Encryption Space Ratio measures encoding complexity of the video relative to the level of encryption so as to judge the success of the encryption process, according to entropy coder. A detailed comparative analysis of EXPer with state-of-the-art algorithms confirms that the EXPer provides significant confidentiality with a small computational cost and negligible encryption bitrate overhead. Thus, the results demonstrate that the proposed security scheme is a suitable choice for constrained devices in an Internet of Multimedia Things environment.
SECURITY THREATS AND CHALLENGES IN IOT TECHNOLOGIES

IoT technology is one of the key issues that need to be addressed. There are various configurations of issues that need to be addressed in the physically connected part of the IoT and in the network domain. IoT automation programs have special requirements in real time, they have a high level of reliability and security in a stable environment. These requirements strengthen security and high security measures. This article discusses security threats that may arise in various layers of iot architecture, especially in the automation domain. Impact mitigation methods are discussed with security in mind, with equipment in the field, communication infrastructure resulting in a variety of physical solutions and measurements for data processing applications.
The Saga of the Covid-19 Contact Tracing Apps: Lessons for Data Governance

This note selectively unpacks the rapid evolution of the (Western) debate around the opportunity to deploy contact tracing apps, alongside other digital tools such as apps for symptoms sharing and immunity certificates to mitigate the Covid-19 pandemics.I do so from the perspective of a social scientist interested in the implications of the development of digital tools at times of emergency in terms of data governance.I argue that a more articulated reflection is needed towards the development of a healthy institutional structure that regulates the role of large tech platforms, such as Google and Apple (G&A), and public institutions, in governing data, particularly when health data and public value are involved.I unravel the saga of contact tracing apps in the UK and EU, looking at the technical, legal and ethical aspects and I attempt to draw more general lessons for data governance.
Chinese description generation of dual attention images based on multi-modal fusion

Abstract In view of the lack of attention to image features in most of the current research on image Chinese description generation, the low quality of image details, and the low accuracy of generated language description, a multi-modal fusion dual attention image description generation model is proposed. The model uses the pre-trained VGG19 network to extract image features, LSTM extracts key information features of text, and combines image features and text features through attention stitching to obtain the attention weight of each spatial position of the image, and then input it into the LSTM language model Generate descriptive sentences. In the AI Challenger 2017 test set, the BLEU-1 and CIDEr scores of this algorithm can reach 71.2 and 106.1 respectively, which are better than baseline models based on a single attention structure, such as NIC, and perform manual observation and comparison of the effects of several models. The model can generate natural language descriptions that can better represent the details of the picture.
Language aspects in English for tourism class

English for Tourism (EfT) class for higher education is a means of response to Industrial Revolution 4.0 era. Besides, it is also a collaborative field between English language and Tourism. For the subject, In English Education Department which mostly focuses on language instruction, the lecturers are expected to formulate the comprehensible materials to facilitate the instructional process. Thus, several related aspects should be considered in formulating the instructional design, including developing the materials. Then, the instruments to gain the data about the needs analysis are also important factors to make sure that the instructional design really meets the needs. As the research focuses on the language aspect in EfT class, specific expressions will be the answers for the big question which becomes the starting point of this research. It will be the basis in formulating the appropriate materials for the instruction.
Interaction of Higher School and Employers on Training of Management Personnel in Digital Society (According to Materials of Mass Media and Social Media)

This article is devoted to the requirements that digital society proposes to educational organizations and employers. Educational organizations should improve the specialists’ and employers’ training quality as well as their participation in the evaluation of educational programs. Ensuring the quality of education is a task of all actors in the education system such as educational organizations, students, their parents, and employers. The authors carried out a study to determine employers’ opinion on the question referring to training quality in Moscow higher schools. For this purpose, leading Moscow universities were selected that have the right to form and approve their own educational standards. The study has fixed the positive nature of the most publications in mass-media in relation to the largest Moscow universities. There is positive coverage of university graduates’ employment and adaptation problems. The information reasons were universities activities that contribute to the improvement of the graduates’ competence level. The study has revealed that the problems of evaluation and quality assurance of education are relevant not only for educational organizations, but also for employers. The research has determined that the requirements of the digital society development are such that educational organizations as producers and suppliers of labour should be more result-oriented, and employers should be directly involved in the formation and evaluation of the quality of educational programs.&#x0D; Keywords: digital society, educational organizations, quality of education, employers, social and traditional media. Media messages, specialists, labour market, quality of education, Moscow universities
Generation of echocardiographic 2D images of the heart using cGAN

Abstract One of the most significant tasks of echocardiography is the automatic delineation of the cardiac structures from 2D echocardiographic images. Over the past decades, the automation of this task has been the subject of intense research. One of the most effective approaches is based on the deep convolutional neural networks. Nonetheless, it is necessary to use echocardiogram frames of the cardiac muscle, which show the boundaries of the cardiac structures labeled/annotated by experts/cardiologists to train it. However, the number of databases containing the necessary information is relatively small. Therefore, generated echocardiogram frames are used to increase the amount of training samples. This process is based on the ultrasound images of the heart, annotated by experts. The article proposes an improved method for generating echocardiograms using a generative adversarial neural network (GAN) with a patch-based conditional discriminator. It has been demonstrated that it is possible to improve the quality of generated echocardiogram frames in both two and four chamber views (AP4C, AP2C) using the masks of cardiac segmentation with sub-pixel convolution layer (pixel shuffle). It is demonstrated that the proposed approach makes it possible to generate ultrasound images, the structure of which corresponds to the specified segmentation masks. It is expected that this method will improve the accuracy of solving the direct problem of automatic segmentation of the left ventricle.
The Impact of Text Messaging as an Instructional Tool to Enhance Learner Autonomy and Perception

This paper investigates the efficiency of text messaging as an English as a Foreign Language (EFL) instructional tool to enhance learner autonomy and perception at the Islamic Azad University-South Tehran Branch, Iran. The study considers seventy-four learners to participate in the study after the administration of an Oxford Placement Test to measure their proficiency level. Participants are randomly assigned in experimental and control groups, including 37 participants each. A questionnaire is used as a pretest and posttest to measure learners` autonomy. Participants from the experimental group use text messaging (the treatment) to receive instructions, whereas those from the control group receive traditional classroom instructions in a face-to-face channel. A semi-structured interview is also used to collect date on experimental group participants` perception in using MALL in classrooms. The results reveal remarkable differences between the experimental and control groups’ means on their learner autonomy scores. However, the impact of the independent samples t-test has shown that there is no statistically meaningful gender difference among the learners regarding their autonomy scores. The findings based on the semi-structured interview showed complimentary views on MALL. The current study is beneficial since its outcomes could be relevant for EFL curriculum developers and English language teachers in the use of mobile learning and text messaging in the English classroom.
Analisa Klasifikasi C4.5 Terhadap Faktor Penyebab Menurunnya Prestasi Belajar Mahasiswa Pada Masa Pandemi

The purpose of the study was to classify the factors causing the decline in student achievement during the pandemic using the C4.5 datamining method. Sources of research data were obtained by conducting interviews and distributing questionnaires to 7th semester students of the 2020-2021 school year information system study program. Attributes that used in the classification of the factors causing the decline in student achievement include: Learning Method (C1), Study Time (C2), Material Understanding (C3), Giving Assignments (C4) and Environment (C5). The results of the calculation show that the Material Understanding (C3) attribute is the attribute that most influences the decline in student learning achievement. Testing was also carried out using the help of Rapidminer software and obtained an accuracy of 97.5%.
An efficient framework for deep learning‐based light‐defect image enhancement

The enhancement of light-defect images such as extremely low-light, low-light and dim-light has always been a research hotspot. Most of the existing methods are excellent in specific illuminations, and there is much room for improvement in processing light-defect images with different illuminations. Therefore, this study proposes an efficient framework based on deep learning to enhance various light-defect images. The proposed framework estimates the reflectance component and illumination component. Next, we propose a generator guided by an attention mechanism in the reflectance part to repair the light-defect in the dark. In addition, we design a colour loss function for the problem of colour distortion in the enhanced images. Finally, the illumination map of the light-defect images is adjusted adaptively. Extensive experiments are conducted to demonstrate that our method can not only deal with the images with different illuminations but also enhance the images with clearer details and richer colours. At the same time, we prove its superiority by comparing it with state-of-the-art methods under both visual quality comparison and quantitative comparison of various datasets and real-world images.
Grey-box Extraction of Natural Language Models

Model extraction attacks attempt to replicate a target machine learning model from predictions obtained by querying its inference API. Most existing attacks on Deep Neural Networks achieve this by supervised training of the copy using the victim's predictions. An emerging class of attacks exploit algebraic properties of DNNs to obtain high-fidelity copies using orders of magnitude fewer queries than the prior state-of-the-art. So far, such powerful attacks have been limited to networks with few hidden layers and ReLU activations. In this paper we present algebraic attacks on large-scale natural language models in a grey-box setting, targeting models with a pre-trained (public) encoder followed by a single (private) classification layer. Our key observation is that a small set of arbitrary embedding vectors is likely to form a basis of the classification layer's input space, which a grey-box adversary can compute. We show how to use this information to solve an equation system that determines the classification layer from the corresponding probability outputs. We evaluate the effectiveness of our attacks on different sizes of transformer models and downstream tasks. Our key findings are that (i) with frozen base layers, high-fidelity extraction is possible with a number of queries that is as small as twice the input dimension of the last layer. This is true even for queries that are entirely in-distribution, making extraction attacks indistinguishable from legitimate use; (ii) with fine-tuned base layers, the effectiveness of algebraic attacks decreases with the learning rate, showing that fine-tuning is not only beneficial for accuracy but also indispensable for model confidentiality.
Integrating Fairy Tales into Reading Class: Do They Catch Young Learners’ Reading Interest?

The aim of the present research is to investigate how fairy tales attract young learners’ interest in reading. Qualitative approach with ethnography method was applied. The participants involved consisted of 30 fifth graders and an English teacher of a public elementary school in Bekasi, West Java, Indonesia. The data were collected through observation, interview, and document analysis. The data were analyzed using data reduction, description, and verification. The finding demonstrated that fairy tales are proven to catch the young learners’ reading interest as they provide imaginative and enchanting content. By way of addition, it was indicated that affective and cognitive factors influence the students’ reading interest.
Entanglement of two Jaynes–Cummings atoms in single-excitation space

We study the entanglement dynamics of two atoms coupled to their own Jaynes-Cummings cavities in single-excitation space. Here we use the concurrence to measure the atomic entanglement. And the partial Bell states as initial states are considered. Our analysis suggests that there exist collapses and recovers in the entanglement dynamics. The physical mechanism behind the entanglement dynamics is the periodical information and energy exchange between atoms and light fields. For the initial Partial Bell states, only if the ratio of two atom-cavity coupling strengths is a rational number, the evolutionary periodicity of the atomic entanglement can be found. And whether there is time translation between two kinds of initial partial Bell state cases depends on the odd-even number of the coupling strength ratio.
Hessian-Aware Pruning and Optimal Neural Implant

Pruning is an effective method to reduce the memory footprint and FLOPs associated with neural network models. However, existing structured-pruning methods often result in significant accuracy degradation for moderate pruning levels. To address this problem, we introduce a new Hessian Aware Pruning (HAP) method coupled with a Neural Implant approach that uses second-order sensitivity as a metric for structured pruning. The basic idea is to prune insensitive components and to use a Neural Implant for moderately sensitive components, instead of completely pruning them. For the latter approach, the moderately sensitive components are replaced with with a low rank implant that is smaller and less computationally expensive than the original component. We use the relative Hessian trace to measure sensitivity, as opposed to the magnitude based sensitivity metric commonly used in the literature. We test HAP for both computer vision tasks and natural language tasks, and we achieve new state-of-the-art results. Specifically, HAP achieves less than $0.1\%$/$0.5\%$ degradation on PreResNet29/ResNet50 (CIFAR-10/ImageNet) with more than 70\%/50\% of parameters pruned. Meanwhile, HAP also achieves significantly better performance (up to 0.8\% with 60\% of parameters pruned) as compared to gradient based method for head pruning on transformer-based models. The framework has been open sourced and available online.
Rapid Neural Architecture Search by Learning to Generate Graphs from Datasets

Despite the success of recent Neural Architecture Search (NAS) methods on various tasks which have shown to output networks that largely outperform human-designed networks, conventional NAS methods have mostly tackled the optimization of searching for the network architecture for a single task (dataset), which does not generalize well across multiple tasks (datasets). Moreover, since such task-specific methods search for a neural architecture from scratch for every given task, they incur a large computational cost, which is problematic when the time and monetary budget are limited. In this paper, we propose an efficient NAS framework that is trained once on a database consisting of datasets and pretrained networks and can rapidly search a neural architecture for a novel dataset. The proposed MetaD2A (Meta Dataset-to-Architecture) model can stochastically generate graphs (architectures) from a given set (dataset) via a cross-modal latent space learned with amortized meta-learning. Moreover, we also propose a meta-performance predictor to estimate and select the best architecture from those sampled from MetaD2A. The experimental results demonstrate that our model meta-learned on subsets of ImageNet-1K and architectures from NAS-Bench 201 search space successfully generalizes to multiple benchmark datasets including CIFAR-10 and CIFAR-100, with the search time of less than 30 GPU seconds on CIFAR-10. We believe that the MetaD2A proposes a new research direction for rapid NAS as well as ways to utilize the knowledge from rich databases of datasets and architectures accumulated over the past years.
The Development of Gamification Model Assisted by Online Learning to Improve the Quality of Student Learning in Economic Education at Pamulang University

Gamification is an integrated process in a game in the form of a website, social media, and daily operational activities, moreover it can motivate students to be active in learning process.The aim of this study is to improve the quality of student learning by producing a chapter on the module for renewal of economic education learning.The research method used is Research and Development (R&D).In this research, The application of gamification learning model assisted by online learning consists of 3 (three) stages consisting of limited trials, extensive trials and field trials.The results showed that: (1) Development of gamification learning models assisted by online learning was declared valid for use; (2) There is a significant difference in the quality of student learning before and after using online learning assisted gamification learning models.Therefore, this learning model is very effective to be applied in lectures.
Research on Performance Optimization Technology of Complex Equipment Software Database

Abstract Complex equipment software usually uses a database to store data. In order to enable it to run stably and efficiently during use, it is necessary to ensure the performance of database access. How to design and manage the database reasonably and optimize the database query has attracted much attention. This article studies the database access performance problems of MySQL equipment used by complex equipment software, studies database optimization techniques, and provides a database optimization solution for the database access delay problems that occur during project operation. Reduced waste of resources, thereby improving the efficiency of system database access, and solving the problem of system access to the database delay.