Reviewerly/exHarmony huggingface dataset:
Research on the Translation Teaching System of English Majors Based on Big Data

Abstract In the era of big data, with the rise of online media and the wide application of mobile terminals in education and teaching, more and more students rely too much on convenient mobile devices such as tablets and mobile phones to search for videos, audio and text that match their preferences. The network English translation learning resources, over time, lead to resistance to the traditional translation course teaching methods. Especially when teachers use traditional methods to present teaching resources that are duplicated with online resources in the classroom, students will be more resistant to traditional English translation teaching. In this era, the English professional translation course will usher in a new round of teaching reform, and then the author will carry out detailed analysis.
Supervised Machine Learning based Classification of Video Traffic Types

The main topic of the article is accuracy evaluation of supervised machine learning algorithms performance applied to real network traffic data. The main task to be solved by supervised learning is classification of video traffic type - streaming (real-time) video or on-demand video (a record). The experiment has been performed for the same video fragment with data filtering and without it. The results have been summarized in form of tables with accuracy assessment for multiple commonly used supervised machine learning algorithms.
Research on Knowledge Graph Model for Cybersecurity Logs Based on Ontology and Classified Protection

Abstract In order to audit and analyse cybersecurity events from massive logs, the knowledge graph model for cybersecurity logs is proposed. The data layer and the pattern layer of the knowledge graph model for cybersecurity logs is obtained based on ontology and classified protection. Entity classification of cybersecurity logs is extracted by using classified protection standards in the data layer of the knowledge graph. The relationship and attribute of ontology from cybersecurity logs is defined. The pattern layer of the knowledge graph model for cybersecurity logs is defined, classified protection data is integrated in entity alignment, and classified protection information gain is used in ontology construction. The structure of the knowledge graph for cybersecurity logs is given. So that the efficient association and deep mining analysis of cybersecurity logs are realized, and it can be directly analysed and processed on the data without the need for precise modelling of the problem. The efficiency of logs analysis is improved. The model has heuristic characteristics and generalization abstract ability, and the model is suitable for big data analysis of large-scale cybersecurity logs.
Security Analysis for Machine Learning and Image Processing Related Information Systems

With the exponential increase in data available online, the need for its safeguarding is manifold. Privacy models have been built to give sophistication in the expense of data in every aspect. The information has become the new asset. Machine Learning and Image Processing techniques replace traditional modes of data privacy. There is a need to involve autonomous systems in privacy mechanisms. From the storage of data to its analysis, there is always a threat of data theft or malpractice from intruders. A novel model based on data fragmentation and increased associates in the authentication system which ensures enhanced security of information is proposed. The model brings out layered protection of data using autonomous systems. This paper analyzes key Machine Learning and Image Processing based privacy models and contrasts them. Challenges related to data privacy and security are also discussed.
Impact of Solution Representation in Nature-Inspired Algorithms for Feature Selection

This paper investigates how does the solution representation in nature-inspired algorithms impact the performance of feature selection in classification problems. Four most suitable nature-inspired algorithms for feature selection were considered in the analysis, namely the Differential Evolution, Artificial Bee Colony, Particle Swarm Optimization, and Genetic Algorithm. The binary-coded and real-coded variants of the mentioned algorithms were compared for filter-based and wrapper-based feature selection methodologies on datasets commonly used by the research community. Additionally, the algorithms' performance on reducing the feature subset size regarding different solution representations was compared. Statistical tests were performed for discovering any significant differences in the algorithms' performances.
Hyper-Laplacian Regularized Non-Local Low-Rank Prior for Blind Image Deblurring

Blind deblurring of single image is a challenging image restoration problem. Recent various image priors have been successfully explored to solve this ill-posed problem. In this paper, based on the non-local self-similarity, we propose a novel method for blind image deblurring, which can simultaneously capture the intrinsic structure correlation and spatial sparsity of an image. Specifically, we use the hyper-Laplace prior to model the structure information of non-local similar patches, and embed it into the low-rank model as a smooth term of the energy equation. Since the established energy function is non-convex, an effective iterative optimization scheme is designed to effectively implement the proposed algorithm. In addition, we evaluate the proposed method for non-uniform deblurring problem. Extensive experimental results on both synthetic and real-world images show that the proposed method performs competitively against the state-of-the-art methods.
An operation on intuitionistic fuzzy matrices

In this paper, we define an operation on the intuitionistic fuzzy matrices called the Gödel implication operator as an extension to the definition of this operator in the case of ordinary fuzzy matrices due to Sanchez and Hashimoto. Using this operator, we prove several important results for intuitionistic fuzzy matrices. Particularly, some properties concerning pre-orders, sub-inverses, and regularity . We concentrate our discussion on the reflexive and transitive matrices. This studying enables us to give a largest sub-inverse and a largest generalized inverse for a reflexive and transitive intuitionistic fuzzy matrix. Also, we obtain an idempotent intuitionistic fuzzy matrix from any given one.
Probabilistic Neighbourhood Component Analysis: Sample Efficient Uncertainty Estimation in Deep Learning

While Deep Neural Networks (DNNs) achieve state-of-the-art accuracy in various applications, they often fall short in accurately estimating their predictive uncertainty and, in turn, fail to recognize when these predictions may be wrong. Several uncertainty-aware models, such as Bayesian Neural Network (BNNs) and Deep Ensembles have been proposed in the literature for quantifying predictive uncertainty. However, research in this area has been largely confined to the big data regime. In this work, we show that the uncertainty estimation capability of state-of-the-art BNNs and Deep Ensemble models degrades significantly when the amount of training data is small. To address the issue of accurate uncertainty estimation in the small-data regime, we propose a probabilistic generalization of the popular sample-efficient non-parametric kNN approach. Our approach enables deep kNN classifier to accurately quantify underlying uncertainties in its prediction. We demonstrate the usefulness of the proposed approach by achieving superior uncertainty quantification as compared to state-of-the-art on a real-world application of COVID-19 diagnosis from chest X-Rays. Our code is available at this https URL
Private Reinforcement Learning with PAC and Regret Guarantees

Motivated by high-stakes decision-making domains like personalized medicine where user information is inherently sensitive, we design privacy preserving exploration policies for episodic reinforcement learning (RL). We first provide a meaningful privacy formulation using the notion of joint differential privacy (JDP)--a strong variant of differential privacy for settings where each user receives their own sets of output (e.g., policy recommendations). We then develop a private optimism-based learning algorithm that simultaneously achieves strong PAC and regret bounds, and enjoys a JDP guarantee. Our algorithm only pays for a moderate privacy cost on exploration: in comparison to the non-private bounds, the privacy parameter only appears in lower-order terms. Finally, we present lower bounds on sample complexity and regret for reinforcement learning subject to JDP.
Effective Link Prediction with Topological and Temporal Information using Wavelet Neural Network Embedding

Abstract Temporal networks are networks that edges evolve over time, hence link prediction in temporal networks aims at inferring new edges based on a sequence of network snapshots. In this paper, we propose a graph wavelet neural network (TT-GWNN) framework using topological and temporal features for link prediction in temporal networks. To capture topological and temporal features, we develope a second-order weighted random walk sampling algorithm. It combines network snapshots with both first-order and second-order weights into one weighted graph. Moreover, it incorporates a damping factor to assign greater weights to more recent snapshots. Next, we adopt graph wavelet neural networks to embed the vertices and use gated recurrent units for predicting new links. Extensive experiments demonstrate that TT-GWNN can effectively predict links on temporal networks.
Implementation of Prototyping Method on Smart Village Application

Abstract Software development has many methods which are divided into traditional and agile categories. Traditional methods are known as heavy, rigid methods and have a lot of documentation, while agile is known as a lightweight, fast and a little documentation method. But there is a traditional method that has the flexibility and speed like the agile method, namely the prototyping method. Several prototyping methods are often used in building an application. The research aims to choose the right prototyping method for building a smart village application. The smart village application can be categorized as an application that requires a lot of communication with the user, where the user is a village community who has a certain culture and has a variety of educational backgrounds. The results of this study are that prototyping methods that are suitable for building smart village applications are evolutionary prototyping.
Deep Tech and Artificial Intelligence for Worker Safety in Robotic Manufacturing Environments

The main objective of the work is to prevent hazardous situations for workers in automated and robotized agile-production environments by means of a new open-source FIWARE-based Smart Factory Service. New system will implement Deep Learning models based on Convolutional Neural Networks, Deep Reinforcement Learning (Double Deep Q Networks) and Recurrent Neural Networks for the detection of anomalous behavior patterns and the prediction of the workers’ level of attention, fatigue and distraction. For this purpose, FIWARE IoT Agents will gather video, audio and ECG data coming from camera and microphone arrays deployed in the environment, as well as ECG incorporated in wearables.
An Edge Framework for the Application of Privacy Enhancing Technologies in IoT Communications

IoT devices generate large amounts of data that is often processed in cloud backends. This data, however, is often personal and sensitive. At the same time, IoT devices often communicate via edge devices that allow to pre-process the devices' data before it is sent to the cloud. To facilitate the privacy-preserving communication between IoT devices and cloud backends, we propose a framework that can be deployed on edge devices and which allows the application of Privacy Enhancing Technologies (PETs) and other computational tasks. It is designed as a practical tool for service providers supporting the privacy-friendly design and operation of edge-based services. It supports various stakeholder requirements, e.g. extendibility and auditability, as well as legal requirements which result from the General Data Protection Regulation (GDPR), e.g. data minimization. We also present an example application using the AWS IoT service and its Greengrass software to show how the framework can be used in a car-sharing service.
Grid Parity Analysis of Rooftop Photovoltaic in Jakarta and Surabaya

The growth of rooftop photovoltaic users in Indonesia has been very rapid since 2017, which around 85 percent of rooftop photovoltaic users in Indonesia are scattered in Jakarta, West Java and East Java regions. In order to deal with the trend of rooftop photovoltaic in Indonesia, the study related to the analysis of rooftop photovoltaic grid parity was carried out specifically for the Jakarta region, considering that the region includes the most rooftop users, while East Java represented by Surabaya is chosen because it has the highest solar power potential compared the two regions others. Grid Parity is the time when the electricity price equals the levelised cost of photovoltaic energy generation. To analyse the dynamics of the generation costs of a rooftop photovoltaic we use the Levelised Cost of Electricity (LCOE). To determine the average rooftop photovoltaic energy production in 2018, a model of 3 years Automatic Weather System (AWS) history data was used. For the same calculation in the next year, it is corrected by the derating factor of the system assuming a 13 percent decrease from the previous energy production each year. For the scenario of a battery-free rooftop photovoltaic system, the grid parity condition in Surabaya is expected to be achieved as early as 2021 for a capacity of 3,500 Wp and 5,500 Wp, while in Jakarta with the same capacity it will only be reached by 2022. For the scenario of a rooftop photovoltaic system with battery storage, the grid parity condition in Surabaya is expected to be achieved as early as 2024 for a capacity of 3,500 Wp, 4.400 Wp and 5,500 Wp, while in Jakarta with the same capacity it will only be reached by 2025. The Grid Parity in Surabaya is faster than in Jakarta because LCOE projections for Surabaya is cheaper than LCOE in Jakarta due to the average energy production in one year for the Surabaya region is higher than in Jakarta.
Forward-looking content aware encoding for next generation UHD, HDR, WCG, and HFR

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">As we envision a world of life-like TV, enhanced in all possible degrees of freedom, immersive UHD-2/8K high dynamic range (HDR) wide color gamut (WCG) high frame rate (HFR) are knocking at our doors. Although technology is improving at a constant pace, processing and transmitting such an amazing amount of information remains an incredible challenge. The British Broadcasting Corp. (BBC) transmitted an ultrahigh-definition (UHD) HDR live feed of the Fédération Internationale de Football Association (FIFA) 2018 World Cup at 36 Mb/s, however, the ATEME TITAN Live encoder does it already at 22 Mb/s and lower. Even though the future International Telecommunication Union – Telecommunication (ITU-T) International Organization for Standardization/International Electrotechnical Commission’s (ISO/IEC) versatile video coding (VVC) is promising a 50% coding efficiency gain compared to high-efficiency video coding (HEVC), compression will not be enough, thus leading to strategies like UHD forum Phase B guidelines recommending content-aware encoding. This paper shows how the future challenges of video compression can be met by leveraging both expertise in optimized compression and the latest advances in artificial intelligence, thus spending exactly the right amount of bits on each piece of video information. A practical implementation of content-aware encoding adapted to upcoming video formats is described, and results and analysis are provided</i> .
Families In Wild Multimedia (FIW-MM): A Multi-Modal Database for Recognizing Kinship

Kinship, a soft biometric detectable in media, is fundamental for a myriad of use-cases. Despite the difficulty of detecting kinship, annual data challenges using still-images have consistently improved performances and attracted new researchers. Now, systems reach performance levels unforeseeable a decade ago, closing in on performances acceptable to deploy in practice. Similar to other biometric tasks, we expect systems can benefit from additional modalities. We hypothesize that adding modalities to FIW, which contains only still-images, will improve performance. Thus, to narrow the gap between research and reality and enhance the power of kinship recognition systems, we extend FIW with multimedia (MM) data (i.e., video, audio, and text captions). Specifically, we introduce the first publicly available multi-task MM kinship dataset. To build FIW MM, we developed machinery to automatically collect, annotate, and prepare the data, requiring minimal human input and no financial cost. The proposed MM corpus allows the problem statements to be more realistic template-based protocols. We show significant improvements in all benchmarks with the added modalities. The results highlight edge cases to inspire future research with different areas of improvement. FIW MM provides the data required to increase the potential of automated systems to detect kinship in MM. It also allows experts from diverse fields to collaborate in novel ways.
Insider Threat Detection Based on Anomalous Behavior of User for Cybersecurity

In today’s competitive world, business security is essential. To secure the business processes and confidential data, organizations have to protect the system by implementing new policies and techniques to detect the threats and control it. Threats for cybersecurity are classified into two types, outsider and insider threats. Both threats are very harmful to the organization. These may convert into a severe attack on the systems upon future. Outsider threats have to take more effort to break the security system. But inside users are those who are privileged to access the system within the organization. As data form is digital, it is straightforward to transfer from one to another. Data leakage, theft, and sharing are easy for insiders. Therefore, there is a need to research in this domain. In this proposed paper, a study of insider threat detection based on the anomalous behavior of the user for cybersecurity is presented. The data processing and anomaly detection algorithms are performed for insider threat detection by researchers. This research paper presented a study on insider threat detection based on the anomalous behavior of the user for cybersecurity.
Computing the 2-Adic Complexity of Two Classes Generalized Cyclotomic Sequences

This paper contributes to analyze the 2-adic complexity of a class of Ding-Helleseth generalized cyclotomic sequences and a class of Whiteman generalized cyclotomic sequences of periods of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N=pq$ </tex-math></inline-formula> , where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula> are two odd distinct primes with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathrm {gcd}(p-1,q-1)=2$ </tex-math></inline-formula> satisfying <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p\equiv q\equiv 3\pmod 4$ </tex-math></inline-formula> . The results show that the 2-adic complexity of these sequences is at least <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$pq-p-q-1$ </tex-math></inline-formula> . Then it is large enough to resist the attacks of rational approximation algorithm.
After the iron horse

column After the iron horse: Covid-19 responses in education Share on Author: Jonathan Grudin Microsoft MicrosoftView Profile Authors Info & Claims InteractionsVolume 27Issue 4July - August 2020 pp 32–34https://doi.org/10.1145/3403580Online:09 July 2020Publication History 0citation250DownloadsMetricsTotal Citations0Total Downloads250Last 12 Months165Last 6 weeks16 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Building an Internet of Secure Things

A chief concern regarding the security and privacy of the Internet of Things is its lack of openness about connected devices' security capability. This article discusses standardized compliance programs for consumer product security.
Deep Network Ensemble Learning applied to Image Classification using CNN Trees.

Traditional machine learning approaches may fail to perform satisfactorily when dealing with complex data. In this context, the importance of data mining evolves w.r.t. building an efficient knowledge discovery and mining framework. Ensemble learning is aimed at integration of fusion, modeling and mining of data into a unified model. However, traditional ensemble learning methods are complex and have optimization or tuning problems. In this paper, we propose a simple, sequential, efficient, ensemble learning approach using multiple deep networks. The deep network used in the ensembles is ResNet50. The model draws inspiration from binary decision/classification trees. The proposed approach is compared against the baseline viz. the single classifier approach i.e. using a single multiclass ResNet50 on the ImageNet and Natural Images datasets. Our approach outperforms the baseline on all experiments on the ImageNet dataset. Code is available in this https URL
Analysis of Breast Cancer for Histological Dataset Based on Different Feature Extraction and Classification Algorithms

Breast cancer is the most common type of cancer that occurs in females. Histological images show noteworthy notch in the medical domain. Feature extraction and classification in computer-assisted diagnosis of breast cancer with histological images is an essential aspect. In literature, there exist variety of feature extraction and classification algorithms employed on different datasets and domains. This article presents different feature extraction algorithms in combination with different classification algorithms to diagnose breast cancer using large-scale standard histological dataset known as BreaKHis. The experimental results are analyzed with respect to five different evaluation metrics: accuracy, F-measure, precision, recall, and G-mean.