Reviewerly/exHarmony huggingface dataset:
Reducing Flipping Errors in Deep Neural Networks

Deep neural networks (DNNs) have been widely applied in various domains in artificial intelligence including computer vision and natural language processing. A DNN is typically trained for many epochs and then a validation dataset is used to select the DNN in an epoch (we simply call this epoch ``the last epoch") as the final model for making predictions on unseen samples, while it usually cannot achieve a perfect accuracy on unseen samples. An interesting question is ``how many test (unseen) samples that a DNN misclassifies in the last epoch were ever correctly classified by the DNN before the last epoch?". In this paper, we empirically study this question and find on several benchmark datasets that the vast majority of the misclassified samples in the last epoch were ever classified correctly before the last epoch, which means that the predictions for these samples were flipped from ``correct" to ``wrong". Motivated by this observation, we propose to restrict the behavior changes of a DNN on the correctly-classified samples so that the correct local boundaries can be maintained and the flipping error on unseen samples can be largely reduced. Extensive experiments on different benchmark datasets with different modern network architectures demonstrate that the proposed flipping error reduction (FER) approach can substantially improve the generalization, the robustness, and the transferability of DNNs without introducing any additional network parameters or inference cost, only with a negligible training overhead.
The structural relationship between social support, reflective thinking, and teacher efficacy perceived by early childhood teachers: The mediating effect of reflective thinking

Objectives The purpose of this study is to analyze the structural relationship between perceived social support by early childhood teacher, their reflective thinking, and teacher efficacy.&#x0D; Methods The participants of the study were 404 teachers working at kindergartens and daycare centers in Seoul and Gyeonggi Province. The collected data were analyzed using SPSS and AMOS 23.0 program. We analyzed the structural relationship between social support, reflective thinking, and teacher efficacy by applying a structural equation model. The bootstrapping was conducted to verify the significance of the mediating effect.&#x0D; Results As a result of analyzing the structural relationship between social support, reflective thinking, and teacher efficacy, it was found that social support had a positive effect on reflective thinking and teacher efficacy, respectively, and reflective thinking had a positive effect on teacher efficacy. As a result of verifying the mediating effect of reflective thinking in the relationship between social support and teacher efficacy, it was found that reflective thinking positively mediated social support and teacher efficacy. In particular, it was found that social support had a greater indirect effect on teacher efficacy through reflective thinking than on teacher efficacy.&#x0D; Conclusions The results suggest that it is necessary to establish a social support system that can activate reflective thinking in order to improve the teacher efficacy of early childhood teachers.&#x0D; &#x0D;
Need web data? Here’s how to harvest them

Webscraping is a useful tool for gathering data from public websites, but researchers must develop some fundamental software skills to use it. Webscraping is a useful tool for gathering data from public websites, but researchers must develop some fundamental software skills to use it.
PENGARUH METODE PERKULIAHAN ONLINE TERHADAP EFEKTIVITAS PEMBELAJARAN MAHASISWA ILMU HUKUM ANGKATAN 2019

The purpose of this research was to see how the online lecture method affected the learning effectiveness of students at the Raja Ali Haji Maritime University's Law Science class of 2019. Correlational research is a sort of hypothesis-driven study in which the variables evaluated are categorized according to the type of relationship they have. The authors of this study used samples from students at Raja Ali Haji Maritime University's Law Study Program class of 2019 who had attended online-based lectures throughout the Covid-19 timeframe. The Slovin algorithm was used to calculate the sample size in this study, which yielded 47 responses. Simple linear regression with a data processing package, such as SPSS, was utilized to analyze the data.
High level browser security in cloud computing services from cross site scripting attacks

Getting the public cloud is truly hard for associations. Therefore, IT divisions are searching for a cloud-passed on a security plan that gives satisfactory end-customer security. An attacker can send malicious script to web browser in form of JavaScript, Script, Flash or any other type of code. When such type code executes in the browser, cookies redirects it to attacker location. These types of attacks are known as XSS (Cross Site Script) attacks. Cloud security is a discipline of organization wellbeing focused on getting appropriate processing structures. This consolidates securing data private and across the online-based establishments, applications, and stages. Further requests of client with encrypted cookies are sent to web proxy where web proxy decrypts these requests and forward to the web server. Browser information base store the encoded treat which won’t be valuable for the assailant in the event that it takes to utilizing cross-site prearranging assault.
Video Regeneration and Quality Enhancer using GFP-GAN

Blind face restoration usually relies on facial priors, such as facial geometry prior or reference prior, to restore realistic and faithful details. However, very low-quality inputs cannot offer accurate geometric prior while high-quality references are inaccessible, limiting the applicability in real-world scenarios. In this work, we propose GFP-GAN that leverages rich and diverse priors encapsulated in a pretrained face GAN for blind face restoration. This Generative Facial Prior (GFP) is incorporated into the face restoration process via spatial feature transform layers, which allow our method to achieve a good balance of realness and fidelity. Thanks to the powerful generative facial prior and delicate designs, our GFP-GAN could jointly restore facial details and enhance colors with just a single forward pass, while GAN inversion methods require image-specific optimization at inference. Extensive experiments show that our method achieves superior performance to prior art on both synthetic and real-world datasets.
Achieving quantum control through messenger flying excitons

High energy acoustic waves may hold the key to long-range transfer of quantum information on a photonic chip.
Teaching multiplication for dyscalculia second graders by using teaching software

Dyscalculia is a kind of learning disabilities. It involves grasping mathematical concepts, and symbols, the difficulty performing calculations and solving problems. Nowadays, the results of research in the field of medicine have not been able to solve the problems of brain nerve function for students with dyscalculia. However, the supporting methods in teaching "Easy Math" can reduce the effects of abnormal cognitive function in learning. The article presents the experimental results of teaching multiplication for second-grade dyscalculia students by using "Easy Math" software. The first step experiment using this teaching software has obtained positive results, which is the basis for further exploitation and further development of dyscalculia students.
Big Data: A Transition from Conventional Databases

The primary programming language created to manage data contained in database systems is called Structured Query Language (SQL). SQL was initially solely used with relational database management systems (RDBMS), but as additional categories of database systems have emerged, its use has substantially expanded. When processing Big Data, or datasets with great volume, velocity, and variety, highly distributed and scalable systems have been proven to be particularly effective at using SQL as a query language. Although conventional relational databases currently only make up a small portion of the database systems landscape, the majority of database courses that cover SQL only take traditional relational systems into account when using SQL. In this study, we suggest that SQL be taught as a general language, which can be applied to a variety of database systems, ranging from conventional RDBMSs to Big Data systems. In the context of emerging database systems like MapReduce, NoSQL, and NewSQL, this paper offers well-organized suggestions for introducing SQL. The description of a variety of course tools, such as virtual machines, sample projects, and in-class exercises, to provide hands-on experience with SQL across a wide range of contemporary database systems is a fundamental addition of this work. Descriptors for Categories and Subjects General terms for computer and information science education in K.3.2 [Computers and Education] Creating and experimenting Keywords Curriculums for databases; SQL; Query Language; Big Data 1.
The 25th Edition of the International Symposium on Wearable Computers

This year marks the 25th edition of ISWC—International Symposium on Wearable Computers—which is the leading research venue for all the topics related to wearables. The conference was held September 21st—24th, 2021, with workshops dedicated to specialized research topics further extending into the 26th. ISWC was co-hosted with the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing, as has been the case for a few years.
NetInfra - A Framework for Expressing Network Infrastructure as Code

NetInfra is a framework designed to manage both DHCP and DNS services. It does this through single source of truth formatted to be consumed by configuration management software which reduces duplication of efforts and unifies the configuration of these interrelated services across otherwise unrelated software systems. In this paper, we review a production deployment of managing DHCP and DNS services via the NetInfra framework and discuss the strengths and weaknesses of the the framework itself.
The Current Situation, Problems and Solutions of China's Network Development

With the arrival of the era of big data, Internet technology continues to upgrade and mature, and mobile terminal smart devices are common and widely used. At present, China's network has achieved full coverage, and the continuous innovation of 5G technology has laid a solid foundation for the development of China's network, and provided bigger space for opportunities and development. With the rapid development of China, our per capita income is constantly increasing, the people's quality of life has been improved significantly. The consumption patterns and concepts of people keep pace with the times.With the development of network technology,the national networking of our people has become the norm and is gradually improving. But at the same time, information technology security, negative energy dissemination and other issues are becoming more and more serious, in order to achieve the sustainable development of the network ,the current network needs to be optimized. We should create a new network full of positive energy, containing correct values and benefiting the people.
Penerapan Konsep Pewarnaan Graf Dalam Penyusunan Jadwal Kegiatan Belajar Mengajar Di SMKN

The problem of scheduling learning activities at SMKN 4 Mataram is the same teachers are scheduled at the same time in two class or more. The coloring graph concept can be used to find out the causes of the scheduling problems at SMKN 4 Mataram. Based on result of the application coloring graph concept generated to the chromatic number is 23 with slots number is 46 which is smaller than 55 or time slots at SMKN 4 Mataram. Because slots of chromatic number are smaller than the time slots at SMKN 4 Mataram, it is known that the causes of scheduling problems at SMKN 4 Mataram are wrong scheduling way. One of solution to this problem is to compile a schedule based on the coloring graph concept by mapping 419 subjects with 84 teachers and 55 time slots spread from Monday to Saturday. In applying the coloring graph concept, the Largest Degree Ordering (LDO) algorithm is used. It is because this algorithm is simpler than other algorithms.
AJFCode: An Approach for Full Aspect-Oriented Code Generation from Reusable Aspect Models

Model-driven engineering (MDE) and aspect-oriented software development (AOSD) contribute to the common goal of development of high-quality code in reduced time.To complement each approach with the benefits of the other, various methods of integration of the two approaches were proposed in the past.Aspect-oriented code generation, which targets obtaining aspect-oriented code directly from aspect models, offers some unique advantages over the other integration approaches.However, the existing aspect-oriented code generation approaches do not comprehensively address all aspects of a model-driven code generation system, such as a textual representation of graphical models, conceptual mapping, and incorporation of behavioral diagrams.These problems limit the worth of generated code, especially in practical use.Here, we propose AJFCode, an approach for aspect-oriented modeldriven code generation, which comprehensively addresses the various aspects including the graphical models and their text-based representation, mapping between visual model elements and code, and the behavioral code generation.Experiments are conducted to compare the maintainability and reusability characteristics of the aspect-oriented code generated using the AJFCode with the most comprehensive object-oriented code generation approach.AJFCode performs well in terms of all metrics related to maintainability and reusability of code.However, the most significant improvement is noticed in the separation of concerns, coupling, and cohesion.For instance, AJFCode yields significant improvement in concern diffusion over operations (19 vs 51), coupling between components (0 vs 6), and lack of cohesion in operations (5 vs 9) for one of the experimented concerns.
Satellite-to-Ground Multi-dimensional Quantum Teleportation via Orbital Angular Momentum

Quantum information transfer via high-dimensional encoding offers many potential advantages relative to the widely adopted two-dimensional encoding scenario. In this work, we investigate the feasibility of performing satellite-to-ground quantum entanglement distribution using photonic high-dimensional encoding. Specifically, we consider pairs of photons entangled in the Orbital Angular Momentum (OAM) discrete-variable bases. Through detailed simulations of the anticipated satellite-to-ground channel, we first investigate the performance of OAM entanglement distribution of varying dimensions. We then consider an application of such OAM distributed entanglement, namely, high-dimensional OAM quantum teleportation. Our work outlines, for the first time, the range of channel conditions and system configurations under which high-dimensional quantum teleportation via OAM encoding in the satellite-to-ground channel is possible.
Surveillance video analysis and compression based on edge computing

Directly uploading monitoring video data to cloud servers can cause network congestion, reduce server computing efficiency, and waste storage space. This paper proposes a lossy compression model suitable for edge computing. The distributed processing method is used to extract high-density data from the edge-device video stream so as to reduce the network transmission pressure, relieve the processing burden placed on the central server, and minimize the processing delay of video surveillance data. A momentum change analysis algorithm is deployed in the preprocessing stage, then an optimized lightweight neural network is used to detect objects at the edge for analysis and compression of the video stream content Experimental results show that the proposed method can extract valuable objects from 98% or more of a given scene. The execution effect on the edge device is equivalent to the video time, thus allowing for fast analysis and compression of video stream content.
MSFM: Multi-view Semantic Feature Fusion Model for Chinese Named Entity Recognition

Named entity recognition (NER) is an important basic task in the field of Natural Language Processing (NLP).Recently deep learning approaches by extracting word segmentation or character features have been proved to be effective for Chinese Named Entity Recognition (CNER).However, since this method of extracting features only focuses on extracting some of the features, it lacks textual information mining from multiple perspectives and dimensions, resulting in the model not being able to fully capture semantic features.To tackle this problem, we propose a novel Multi-view Semantic Feature Fusion Model (MSFM).The proposed model mainly consists of two core components, that is, Multi-view Semantic Feature Fusion Embedding Module (MFEM) and Multi-head Self-Attention Mechanism Module (MSAM).Specifically, the MFEM extracts character features, word boundary features, radical features, and pinyin features of Chinese characters.The acquired font shape, font sound, and font meaning features are fused to enhance the semantic information of Chinese characters with different granularities.Moreover, the MSAM is used to capture the dependencies between characters in a multi-dimensional subspace to better understand the semantic features of the context.Extensive experimental results on four benchmark datasets show that our method improves the overall performance of the CNER model.
Disinformation as a Democratic Collective Action Problem or Why a Legal Solution Is Warranted

Abstract In this chapter we explore how false election information violates democratic values; in other words, we examine the extent to which and the manner in which false election information impugns the legitimacy of Australian elections, and in particular, the democratic legitimacy criteria of ‘effective participation’ and ‘enlightened understanding’. These criteria are central pillars of the free speech condition that enables any authentic democracy to function properly. Because there are few incentives to desist from polluting the election information environment and also because of the significant social costs it entails, the problem should be approached as a collective action problem rather than as an issue of individualised rights. This distinction is consistent with jurisprudence on the freedom of political communication implied in the Australian Constitution and endorsed in multiple judgements, as we show in detail in Chapter 5 . We conclude this chapter by arguing that compulsory voting places an extra duty on the Australian state to ensure that voting takes place in a relatively clean information environment.
SDP-CycleGAN: CycleGAN Based on Siamese Data Pairs for Unraveling Data Imbalance Problems in Anomaly Detection

Data imbalance problem in anomaly detection domain is a key issue that affect the performance of the classification algorithm. The potential of generative adversarial network (GAN) as a data synthesized method on minority category data has been studied recently. We propose a new data balancing method called cycle GAN with siamese data pairs (SDP-CycleGAN). It well utilizes the siamese data pair structure and CycleGAN to to synthesize effective balance data. We conduct classification experiments on two real data sets to evaluate its data balance performance, and compare it with the classical method SMOTE. The existing experimental results show that the data balance performance of SDP-CycleGAN in a variety of classification models is basically similar to smote, and better than SMOTE in some cases.
Efficient Illegal Contents Detection and Attacker Profiling in Real Environments

With the development of over-the-top (OTT) services, the demand for content is increasing, and you can easily and conveniently acquire various content in the online environment.As a result, copyrighted content can be easily copied and distributed, resulting in serious copyright infringement.Some special forms of online service providers (OSP) use filtering-based technologies to protect copyrights, but illegal uploaders use methods that bypass traditional filters.Uploading with a title that bypasses the filter cannot use a similar search method to detect illegal content.In this paper, we propose a technique for profiling the Heavy Uploader by normalizing the bypassed content title and efficiently detecting illegal content.First, the word is extracted from the normalized title and converted into a bit-array to detect illegal works.This Bloom Filter method has a characteristic that there are false positives but no false negatives.The false positive rate has a trade-off relationship with processing performance.As the false positive rate increases, the processing performance increases, and when the false positive rate decreases, the processing performance increases.We increased the detection rate by directly comparing the word to the result of increasing the false positive rate of the Bloom Filter.The processing time was also as fast as when the false positive rate was increased.Afterwards, we create a function that includes information about overall piracy and identify clustering-based heavy uploaders.Analyze the behavior of heavy uploaders to find the first uploader and detect the source site.
A Novel Dynamic Optimization Technique for Finding Optimal Trust Weights in Cloud

Cloud Computing permits users to access vast amounts of services of computing power in a virtualized environment.Providing secure services is essential.There are several problems to real-world optimization that are dynamic which means they tend to change over time.For these types of issues, the goal is not always to identify one optimum but to keep continuously adapting to the solution according to the change in the environment.The problem of scheduling in Cloud where new tasks keep coming over time is unique in terms of dynamic optimization problems.Until now, there has been a large majority of research made on the application of various Evolutionary Algorithms (EAs) to address the issues of dynamic optimization, with the focus on the maintenance of population diversity to ensure the flexibility for adapting to the changes in the environment.Generally, trust refers to the confidence or assurance in a set of entities that assure the security of data.In this work, a dynamic optimization technique is proposed to find an optimal trust weights in cloud during scheduling.