Reviewerly/exHarmony huggingface dataset:
Boosted Dense Segmentation Networks For Constrained Distributed Systems

Deployed AI applications are often heavily con-strained in computational resources; to this end, a method of producing miniature, well-performing neural networks for 2D image segmentation is devised and applied to the Severstal Steel Defect Detection and Kidney Tumor Segmentation (KiTS19) Challenges. By limiting the width of U-Net and employing a full-domain activation function, a network of aggregated weak learners is able to achieve a mean F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> score within 93% of the EfficientNetB0 baseline using only 0.5% of the trainable parameters. A similar network of aggregated strong learners matches the mean F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> score of the baseline on the Severstal dataset using only 10% of the trainable parameters. Gradient boosting is then applied to the weak learners, achieving a mean F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> score within 98% of the strong learner network on the Severstal dataset with approximately 20% of the FLOPS; the key insight is in constraining the O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) relationship between network width and FLOPS. The same approach is applied to the KiTS19 dataset with good success in kidney detection. Interestingly, the method does not perform as well on the much harder to isolate tumor class, and the authors explore some possible reasons. In analyzing the impact of the full-domain activation function, the authors show that density of information is promoted by significantly reduced peaks in layer outputs and a wider range of output values. The method has significant implications in constrained deployments, as many small devices could be used to compute the overall network.
Using Web-Based Games to Improve Mandarin Vocabulary Learning

Web-based games as learning media could be used in Mandarin vocabulary learning. This study investigates the effect of using web-based games on Mandarin vocabulary learning. This research applied an experimental study. The results show that by using web-based games on Mandarin vocabulary learning, student's learning outcomes and student's enthusiasm for learning get better, and student scores increase between 5.7% to 17.1%. The concept of customizable web-based games can be used as a supporting learning media for teachers and students, it helps teachers in providing creative and fun assignments for students and the web-based game also can be used by students as an additional learning media to learn Mandarin vocabulary independently.
Enhanced DIBR Framework for Free Viewpoint Video

DIBR (depth-image-based-rendering) is an essential technology for free-viewpoint video. However, the existing DIBR framework cannot resolve the artifacts, blur, and temporal inconsistency in the synthesized video due to the imperfect depth map. In this paper, we proposed an enhanced DIBR framework to improve the quality of the synthesized virtual view. Firstly, we reconstruct the background of the reference views to refine the reference depth maps and fill holes in the synthesized virtual view to improve the quality and temporal consistency. Then we mark the edge between the foreground and background of the reference view and modify the post-processing method of the virtual view depth map to avoid artifacts. Finally, we propose a foreground edge filtering method to make the synthesized virtual view more realistic. The experiments show that our enhanced DIBR framework can achieve superior performance and improve the temporal consistency in the virtual view.
Autonomous learning features: A case study in an Indonesian ESP classroom

This article explores autonomous learning features and effective strategies for meeting the needs of the autonomous learner. A 4-month ethnographic study examined the learning features of three non-English major undergraduates enrolled in an English for Specific Purposes (ESP) course at an Indonesian university. The study also explored the students’ and teacher’s understanding of how the ESP classroom can benefit the autonomous learner. Data gathered through interviews, focus group discussions, and classroom observation indicate four key autonomous learning features: 1) a willingness to accept responsibility; 2) dedicated planning; 3) implementing effective strategies; and 4) monitoring progress. The findings also indicate that a variety of scaffolded and authentic learning materials combined with a flexible and creative teaching approach enhance autonomous learning. We also address how our results are applicable to most educational situations as autonomous learning is not limited to the language classroom.&#x0D; HIGHLIGHTS:&#x0D; &#x0D; The three subjects indicated different processes of autonomy such as monitoring learning progress using a specific task, holistic approach, and regular reflection strategies.&#x0D; Adopting and promoting a spirit of independent learning provides ESP learners with a supportive framework to develop and practice autonomous learning habits&#x0D; The ESP learners not only required to learn general English but also to learn specific English (their content knowledge in English) as their major. This is the novelty of the study. Therefore, this study provides specific add to the theory of autonomous learning.&#x0D; &#x0D;
Analysis of Fault Tolerance Techniques in Virtual Machine Environment

Cloud computing provides various range of services over the internet such as storage services virtual servers applications infrastructure, etc. Continuously growing cloud data centers are majorly dependent on physical machines and virtualization. The reliability of cloud is calculated based on service availability of its physical and virtual servers. In this paper, a brief analysis study is done on virtualization. Firstly, the concept of failure and its types are presented. Secondly, the existing failure rectification models and strategies are studied and analyzed. Finally, a summary is presented to conclude common failure causes in virtual environment. Thereafter, it is proposed that failure detection can be improved by two-level detection strategy by introducing confidence factor, α, as a counter in heartbeat strategy for fault detection.
Data Hiding in AMBTC Images Using Selective XOR Hiding Scheme

Nowadays since the Internet is ubiquitous, the frequency of data transfer through the public network is increasing. Hiding secure data in these transmitted data has emerged broad security issue, such as authentication and copyright protection. On the other hand, considering the transmission efficiency issue, image transmission usually involves image compression in Internet-based applications. To address both issues, this paper presents a data hiding scheme for the image compression method called absolute moment block truncation coding (AMBTC). First, an image is divided into non-overlapping blocks through AMBTC compression, the blocks are classified four types, namely smooth, semi-smooth, semi-complex, and complex. The secret data are embedded into the smooth blocks by using a simple replacement strategy. The proposed method respectively embeds nine bits (and five bits) of secret data into the bitmap of the semi-smooth blocks (and semi-complex blocks) through the exclusive-or (XOR) operation. The secret data are embedded into the complex blocks by using a hidden function. After the embedding phase, the direct binary search (DBS) method is performed to improve the image quality without damaging the secret data. The experimental results demonstrate that the proposed method yields higher quality and hiding capacity than other reference methods.
A Study on Distortion Estimation Based on Image Gradients

Image noise is a variation of uneven pixel values that occurs randomly. A good estimation of image noise parameters is crucial in image noise modeling, image denoising, and image quality assessment. To the best of our knowledge, there is no single estimator that can predict all noise parameters for multiple noise types. The first contribution of our research was to design a noise data feature extractor that can effectively extract noise information from the image pair. The second contribution of our work leveraged other noise parameter estimation algorithms that can only predict one type of noise. Our proposed method, DE-G, can estimate additive noise, multiplicative noise, and impulsive noise from single-source images accurately. We also show the capability of the proposed method in estimating multiple corruptions.
Audiovisual Media Regulation during the COVID-19 Pandemic - Measures Undertaken by the Romanian Authorities during the State of Emergency

The present study aims to offer a review of measures taken by the Romanian authorities in the field of audiovisual media regulation during the state of emergency instituted in March 2020 following the COVID-19 outbreak. The legal framework has been adjusted, drawing both from extant norms, such as the 2003 Constitution of Romania, and from newly adapted legal norms such as the Presidential Decree declaring the state of emergency. Also, the competent authorities have been invested with additional powers, this being the case of the National Audiovisual Council and the National Authority for Management and Regulation in Communications. These institutions have faced multiple challenges regarding the clash between freedom of opinion and freedom of speech and the right to correct information of the public and the campaigns to counter misinformation.
Digital Art Design System Based on Big Data Technology and Interactive Virtual Technology

With the development of modern information technology and the rapid popularization of computers, the society has entered a comprehensive digital era. Digital art design technology with computer technology as an auxiliary tool has developed rapidly and has been applied in many fields of society. At the same time, digital art design education has developed into a new research hotspot in the field of education. It changes the appearance and connotation of modern display design. In order to better develop digital technology and apply modern technology to the field of art design, this article designs a digital art design system based on big data technology and interactive virtual technology. In the method part, this article introduces big data technology, interactive virtual technology, digital art design and 3ds file format, and introduces the related content of SVM support vector machine in the algorithm. In the part of the research results, this article analyzes several aspects of time-consuming, comparison of accuracy of different methods, the relationship between speedup and data volume, average CPU utilization, gesture recognition, and recognition of various functions. From the test results, in the three functions of gesture action, voice command, and keyboard command, the keyboard command has the best effect, reaching 100% accuracy. The gesture command and voice command have a small margin of error, but the accuracy rate also kept at around 90%. It can be seen that the system designed in this article has a good recognition effect in terms of interaction, and has certain significance for the realization of virtualization, informatization, and digital transformation of artistic design.
Network Embedding Method Based on Semantic Information

Graph embedding is the resultful method to map the graph in the low-dimensional vector space. Now most existing embedding methods to learn nodes representations mainly focus on obtaining nodes adjacent and feature information, but they ignore the state that there is also semantic information between nodes. Therefore, it is proposed a graph embedding method, which introduces point mutual information to compute the semantic similarity between nodes, the basic idea is to count the probability of two nodes appearing simultaneously in a sentence. And it learns representations by modeling the sum of the squares of the difference between point-wise mutual information and the inner product of node vectors, and theoretically shows that using point mutual information can also obtain a log-linear relationship between graph topological by leveraging the invariance property of difference between nodes. Finally, the study selects 5 social networks datasets for node classifications, clustering tasks, and compares them with 6 graph embedding methods and 4 methods based on graph neural network, the result demonstrates that the direct method does not negatively impact accuracy on many downstream applications, and outperforms all the baseline methods. In addition, the computation complexity of our method is lower than the worst-case.
Research on trusted traceability with Block Chain and Handle System Network

With the improvement of people’s quality of life, people pay more attention to the quality of food, drugs and other things. How to build and improve the current product traceability system will become the focus of the society. In view of current situation and problem of traceability technology, a trusted traceability technology with Block Chain and Handle System is presented in this paper. Full processes of data source, association and query during product traceability are protected with our “Block Chain + Handle System” method, which not only solves the problem of data interchange and sharing in whole chain, but also achieves the purpose of data credibility and tamper-proof.
Text Embedding Augmentation based on Retraining with Pseudo-Labeled Adversarial Embedding

Pre-trained language models (LMs) have been shown to achieve outstanding performance in various natural language processing tasks; however, these models have a significantly large number of parameters to handle large-scale text corpora during the pre-training process, and thus, they entail the risk of overfitting when fine-tuning for small task-oriented datasets is conducted. In this paper, we propose a text embedding augmentation method to prevent such overfitting. The proposed method applies augmentation to a text embedding by generating an adversarial embedding, which is not identical to original input embedding but maintaining the characteristics of the original input embedding, using PGD-based adversarial training for input text data. A pseudo-label that is identical to the label of the input text is then assigned to adversarial embedding to conduct retraining by using adversarial embedding and pseudo-label as input embedding and label pair for a separate LM. Experimental results on several text classification benchmark datasets demonstrated that the proposed method effectively prevented overfitting, which commonly occurs when adjusting a large-scale pre-trained LM to a specific task.
Libraries in a World of Data

This chapter discusses five major types of data that libraries may be responsible for the collection, storing, or analyzing of and the threats to privacy faced when dealing with each: web data collection and usage, patron records, learning analytics, research data, and data sharing. It highlights how a lack of clear and consistent policy, improper handling and storage of the data, misuse of the data, and lack of proper informed consent procedures can all compromise patrons' privacy and present threats to patrons' trust in the library. To ensure that data is handled with care, this chapter further introduces four frameworks for ethical data practices, which have recently been proposed in the scholarly literature, and discusses how each may apply to library data issues. By following an ethical framework and developing solid procedures, librarians can ensure private, equitable, and mutually-beneficial data relationships with their patrons, regardless of what new types of data technologies emerge in coming years.
3 Dimensional Dynamic Map on Buildings at University of Darussalam Gontor Based on Augmented Reality

Darussalam Gontor University (Unida Gontor) is a campus with a fairly rapid development of infrastructure. The paper-based building prototype design belonging to UNIDA Gontor is considered not able to visually represent the building that has been or will be built. This research aims to provide a prototype design update using more modern and dynamic technology that can be seen from all sides of the building and does not require physical storage, also can be used anytime and anywhere.The Application content is taken through the visual data collection stage in the form of detailed photos of buildings taken through cameras and drones. This media is based on Android which uses marker based augmented reality technology to implement 3-dimensional visualization of campus building objects, which are created through the steps of the waterfall model. The test is carried out in two stages, namely the suitability of the application with various android devices which shows compatibility with devices with a minimum size of 5 inches. The second test was conducted by distributing questionnaires to the staff of the facilities and infrastructure division of Unida Gontor with an average score of 90%, and the lecturers and multimedia student group 89.52%.
A lightweight optimal scheduling algorithm for energy-efficient and real-time cloud services

To support ever-chainging user needs such as large storage volumes, web search, and highperformance computing, numerous companies have expanded their systems to cloud computing servers.Cloud environment systems generally consume large amounts of electrical power, leading to tremendously high operational costs.In addition, they require computing infrastructures to run various real-time applications such as financial analysis, cloud gaming, and web-based real-time services.To represent performance guarantees, the negotiated agreements in real-time computing, expressed as deadline (or latency), can be specified by service level agreements of cloud services between users and cloud server providers.Thus, a number of research works have started focusing on reducing the energy consumption and simultaneously satisfying the temporal constraint in a cloud environment.Although we previously proposed an optimal real-time scheduling algorithm for multiprocessors, it is difficult to use it for cloud environments handling a large number of cloud services because of the high computational complexity of (N 3 logN ), where N is the number of tasks.Thus, we introduce a real-time task scheduling algorithm for cloud computing servers, which alleviates the computational complexity of O(N 2 ) from the complexity of the previous algorithm using a novel flow network-based optimization method.To the best of our knowledge, our scheduling algorithm in a cloud environment, which ensures optimality for real-time tasks and achieves energy savings using dynamic power management simultaneously, is the first in the problem domain.We show that the proposed scheduling algorithm guarantees an optimal schedule for real-time tasks and achieves energy savings simultaneously.Our experimental results show that the proposed algorithm outperforms the latest existing algorithms in terms of both time complexity and energy efficiency.
The Improved Decoding Algorithm of the (71, 36, 11) Quadratic Residue Code

In the context of informatization and big data, the demands for the reliability and real-time of information transmission in wireless communication channels are increasing. Quadratic residue (QR) codes have a high prospect on error correction for reliable data conveyance over channel with noise. This paper proposes an optimized algebraic scheme for decoding (71, 36, 11) QR code for correcting up to 5 errors on one-case-by-one-case basis. This scheme is mainly to improve its algebraic decoding algorithm based on the Lin’s algorithm, only in this paper, the Lin’s algorithm is called LTC71 algorithm. The key technology is to use the algebraic structure of QR code and give a new discriminant condition by mathematical derivation, which can quickly detect whether there are four errors in the code. Simulation results show that the proposed decoding scheme achieves the same bit error-rate performance as LTC71 algorithm, and the decoding time is reduced by about 28.72% owing to the reduced complexity of the decoder when there are four errors in the code.
Vector Simulation of Logic Faults based on XOR-Relations

A model of xor-relations between remarkable logical functions of digital circuits is proposed that is convoluted into zero-space, which makes it possible to solve problems of technical diagnosis, machine learning, searching for similarity-difference of processes and phenomena. The advantages of the vector model for a compact description of processes, phenomena, functions and structures are defined. A rejection of the use of analytical logic expressions, requiring algorithmically complex calculators-analyzers, which are replaced by vector data structures for describing functional logic, is made. A vector-deductive method for synthesizing formulas for transporting input fault lists is proposed, which has a quadratic computational complexity of register operations. A coordinate-vector fault model is considered; it is not tied to input variables and can be used for efficient processing complex logic circuits when assessing the quality of synthesized tests.
Generating Weather Comments from Numerical Weather Prediction

本研究では，数値気象予報のシミュレーション結果から天気予報コメントを自動生成するタスクに取り組む. 天気予報コメントの生成タスクには，(i) 様々な物理量の数値変化を考慮する必要がある，(ii) コメントの配信時刻や対象エリアに依存した表現が使われる，(iii) 天気予報コメントにおいて情報の有用性が重要視されている，といった特徴的な課題がある．本研究では，数値気象予報のシミュレーション結果，気象観測値，コメントのメタ情報を入力として， 上記の特徴を捉えた上でテキスト化するための Data-to-Text モデルを提案する．また，天気予報コメントにおける情報の有用性の向上のために，晴天や雨などの気象情報を表す「天気ラベル」を予測する内容選択モデルを導入し，予測結果をテキスト生成時に考慮することで有用な情報を明示的に記述できるようにした. 実験では，自動評価と人手評価を行い，提案モデルはベースラインに対して情報の有用性の観点で最も優れていることを示した．
Deep Multiple Length Hashing via Multi-task Learning

Hashing can compress heterogeneous high-dimensional data into compact binary codes. For most existing hash methods, they first predetermine a fixed length for the hash code and then train the model based on this fixed length. However, when the task requirements change, these methods need to retrain the model for a new length of hash codes, which increases time cost. To address this issue, we propose a deep supervised hashing method, called deep multiple length hashing(DMLH), which can learn multiple length hash codes simultaneously based on a multi-task learning network. This proposed DMLH can well utilize the relationships with a hard parameter sharing-based multi-task network. Specifically, in DMLH, the multiple hash codes with different lengths are regarded as different views of the same sample. Furthermore, we introduce a type of mutual information loss to mine the association among hash codes of different lengths. Extensive experiments have indicated that DMLH outperforms most existing models, verifying its effectiveness.