Reviewerly/exHarmony huggingface dataset:
Klasifikasi Lukisan Karya Van Gogh Menggunakan Convolutional Neural Network-Support Vector Machine

Painting is a work of art with various strokes, textures, and color gradations so that a painting that is synonymous with beauty is created. The various paintings created have characteristics, such as the paintings by Van Gogh, which have tightly arranged strokes, creating a repetitive and patterned impression. This study classifies paintings by Van Gogh or not by using the VGG-19 and ResNet-50 feature extraction methods. The SVM method is used as a classification method with two optimizations, namely random and grid optimization in the linear kernel. The data set used consisted of 124 Van Gogh paintings and 207 paintings by other painters. The use of VGG-19 feature extraction using grid optimization has the best value of 93,28% using the use of random optimization which has a value of 92,89%. The use of ResNet-50 using grid optimization with the best value of 90,28% using the use of random optimization which has a value of 90,15%. The extraction feature of VGG-19 is better than ResNet-50 in paintings by Van Gogh or not.
The emergence of intelligent transportation systems from a continental and technological perspective

This article explores the intertwined nature of technologies and the progress of Intelligent Transportation Systems (ITS) research programs/initiatives in North America and in the European Countries. The first ideas for the intelligent use of existing infrastructure for road traffic were developed in the USA in the late 1960s, whereas the first ITS-related technologies were researched in the early 1960s (sensor technology). Strengthened by international networking and competitive thinking, first enormous efforts in ITS research were conducted in the late 1980s.
Learning from satisfying assignments under continuous distributions

What kinds of functions are learnable from their satisfying assignments? Motivated by this simple question, we extend the framework of [DDS15], which studied the learnability of probability distributions over {0, 1}n defined by the set of satisfying assignments to “low-complexity” Boolean functions, to Boolean-valued functions defined over continuous domains. In our learning scenario there is a known “background distribution” over ℝn (such as a known normal distribution or a known log-concave distribution) and the learner is given i.i.d. samples drawn from a target distribution f, where f is restricted to the satisfying assignments of an unknown low-complexity Boolean-valued function f. The problem is to learn an approximation ′ of the target distribution f which has small error as measured in total variation distance. We give a range of efficient algorithms and hardness results for this problem, focusing on the case when f is a low-degree polynomial threshold function (PTF). When the background distribution is log-concave, we show that this learning problem is efficiently solvable for degree-1 PTFs (i.e., linear threshold functions) but not for degree-2 PTFs. In contrast, when is a normal distribution, we show that this learning problem is efficiently solvable for degree-2 PTFs but not for degree-4 PTFs. Our hardness results rely on standard assumptions about secure signature schemes.
Research on English Grammar Error Correction Technology Based on BLSTM Sequence Annotation

Grammatical errors are one of the most common types of errors in English learning and writing. Intelligent detection and correction of English grammatical errors can effectively help English learners improve their learning efficiency. The research is based on the BLSTM bi-directional and long-short-term memory neural network to construct a sequence labeling model to detect and correct English grammatical errors. The results show that the sequence tagging model based on the BLSTM bi-directional and long-short-term memory neural network has an accuracy of 96.88% for the part-of-speech tagging of special corpora containing grammatical errors, the accuracy rate of error correction is 33.58%, the recall rate is 44.95%, F-measure index value is 38.26%, which is 4.85% higher than the UIUC model and 4.92% higher than the Corpus GEC model. It can automatically detect and correct text grammatical errors with good effect, which provides a new idea for the development of intelligent English grammatical error detection and correction technology.
Getting the most out of Facebook and WhatsApp: social media strategies by female entrepreneurs in the UAE

Purpose This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies. Design/methodology/approach This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context. Findings When organizations conduct research around product testing or new marketing strategies, they will often turn to academic studies to help them verify any courses of action. With good reason, academic studies are seen as trustworthy, and can be used to validate any specific approach. Whether it is choosing materials for a new product because of their proven qualities in a university laboratory or looking at how certain marketing strategies performed during a given study in a specific market, firms will feel confident that any evidence presented to them can be used secure in the knowledge that it has already been validated. Originality/value The briefing saves busy executives and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.
Perception and Testimony as Data Providers

The first part of this chapter articulates an answer in terms of a re-interpretation of perception and testimony as data providers rather than full-blown cases of knowledge. But then, if perception and testimony are correctly understood as data providers, how is one supposed (to apply this analysis in order) to understand the semantic value of the data provided by such processes? The second part of the chapter argues in favour of a constructionist hypothesis about how data may become meaningful for human cognitive agents through a process of <italic>repurposing</italic> of natural data/signals. The conclusion of the chapter is that, from a non-naturalistic, constructionist perspective, human agents are natural-born data hackers.
Network and web app attacks increase

Figures from a new report by Amazon Web Services (AWS) show that network and web application security events rose by nearly a quarter (23%) in the first quarter of 2020, compared to the same period in 2019.
M-Arg: Multimodal Argument Mining Dataset for Political Debates with Audio and Transcripts

Argumentation mining aims at extracting, analysing and modelling people's arguments, but large, high-quality annotated datasets are limited, and no multimodal datasets exist for this task.In this paper, we present M-Arg, a multimodal argument mining dataset with a corpus of US 2020 presidential debates, annotated through crowd-sourced annotations.This dataset allows models to be trained to extract arguments from natural dialogue such as debates using information like the intonation and rhythm of the speaker.Our dataset contains 7 hours of annotated US presidential debates, 6527 utterances and 4104 relation labels, and we report results from different baseline models, namely a text-only model, an audio-only model and multimodal models that extract features from both text and audio.With accuracy reaching 0.86 in multimodal models, we find that audio features provide added value with respect to text-only models.
Cluster Analysis

This chapter covers a number of aspects of cluster analysis. Initially, it presents clustering manually, using standardized data.
Keyframe-Based Vehicle Surveillance Video Retrieval

This article describes how due to the diversification of electronic equipment in public security forensics, vehicle surveillance video as a burgeoning way attracts us attention. The vehicle surveillance videos contain useful evidence, and video retrieval can help us find evidence contained in them. In order to get the evidence videos accurately and effectively, a convolution neural network (CNN) is widely applied to improve performance in surveillance video retrieval. In this article, it is proposed that a vehicle surveillance video retrieval method with deep feature derived from CNN and with iterative quantization (ITQ) encoding, when given any frame of a video, it can generate a short video which can be applied to public security forensics. Experiments show that the retrieved video can describe the video content before and after entering the keyframe directly and efficiently, and the final short video for an accident scene in the surveillance video can be regarded as forensic evidence.
Release cycle management: an action research study into a software company

Software is increasingly developed in a recurrent fashion. Still, we lack theoretical knowledge and conceptual models for continuously improving software release management in such dynamic contexts. In response, we conducted an action research study to improve Software Inc.'s capability to recurrently release a specific software service, Secure-on-Request. Drawing on contextualist inquiry as theoretical frame, we addressed an initial problematic situation with a recent software acquisition, complexity of service delivery, new engineering and product management teams and low process maturity to improve the organisation's approach to software releases. As a result, we offer a detailed account of the process of, the content of and the context for transforming release management at Software Inc. through the phases of diagnosing, establishing, acting and learning. Based on these insights, we theorise release cycle management (RCM) as a key practice that integrates software process improvement into recurrent delivery of software. Moreover, we offer a conceptual model for RCM that software managers can use to implement such practices into their organisations.
What the profession of cyber security needs to know and do

This chapter argues that the profession must find ways to ensure that the real-world practice of cyber security is based on the creation and adoption of a complete, correct, and highly effective set of well-defined and commonly accepted controls; ones that are capable of closing off every feasible type of adversarial action. To be completely effective, the solution must amalgamate all of the essential concepts of cyber defence into a single unifying practice model, one that has real-world currency. Professional societies should help to serve as the developers of and final sanction for the fundamental ideas in their respective fields and the creation of the CSEC2017 document provides an authoritative statement of the elements of the field of cyber security for a broad array of practitioners. This chapter discusses the CSEC2017 thought model and outlines the eight knowledge areas specified for the discipline to represent the complete body of knowledge within the field.
CT image reconstruction from sparse projections using adaptive total generalised variation with soft thresholding

CT imaging plays a vital role in non-invasive diagnosis and in surgical planning of critical diseases. It is essential to reduce radiation dose during CT imaging as excessive exposure may cause harm to human tissues. To reduce radiation dose, CT image is acquired using limited number of X-ray projections and then to reconstruct the image an adaptive total generalised variation (TGV) minimisation method has been proposed. The simulation results have been compared with the existing TV, TGV and TGV with hard thresholding methods. Typically two types of noises, Gaussian and Poisson distributed noises, are introduced during CT imaging process. So, these two types of noises have been added in measured samples. It is found that after applying soft thresholding and FISTA algorithm with the proposed method, better results have been obtained in noisy imaging environment. The reconstructed CT image quality has been compared using parameters like FSSIM, PSNR, NRMSE and MAE.
Kernel Based K-Nearest Neighbor Method to Enhance the Performance and Accuracy of Online Signature Recognition

Signature recognition is a significant among the most fundamental biometrics recognition techniques, is a key bit of current business works out, and is considered a noninvasive and non-undermining process. For online signature recognition, numerous methods had been displayed previously. In any case, accuracy of the recognition framework is further to be enhanced and furthermore equal error rate is further to be decreased. To take care of these issues, a novel order method must be proposed. In this paper, Kernel Based k-Nearest Neighbor (K-kNN) is presented for online signature recognition. For experimental analysis, two datasets are utilized that are ICDAR Deutsche and ACT college dataset. Simulation results show that, the performance of the proposed recognition technique than that of the existing techniques in terms of accuracy and equal error rate.
Neural Networks

The fundamental algorithms in data mining and machine learning form the basis of data science, utilizing automated methods to analyze patterns and models for all kinds of data in applications ranging from scientific discovery to business analytics. This textbook for senior undergraduate and graduate courses provides a comprehensive, in-depth overview of data mining, machine learning and statistics, offering solid guidance for students, researchers, and practitioners. The book lays the foundations of data analysis, pattern mining, clustering, classification and regression, with a focus on the algorithms and the underlying algebraic, geometric, and probabilistic concepts. New to this second edition is an entire part devoted to regression methods, including neural networks and deep learning.
Introduction

This chapter introduces the basic concepts of computer organization and code compilation, along with reasons why assembly language is relevant. It also discusses the representation of textual and numerical data, with emphasis on radix complement math.
On Supervised Change Detection in Graph Streams

Many forms of social, information, and communication network activity create large volumes of graph stream data.In such cases, it is often desirable to track interesting properties of the underlying nodes, as they change over time.These dynamic properties can often be represented in the form of time-dependent labels associated with the nodes.Dynamic changes in such node labels may be indicative of important events or patterns of activity.This paper will study the problem of differential classification in graph streams, in which we predict significant classification events; i.e. the changes in classification labels of the nodes.Different from the static collective classification problem, this approach focusses on dynamic and real-time detection of changes in node classification, as opposed to the actual classification of nodes.The differential stream classification problem can also be considered a general form of the nodecentric event detection problem, in which node labels are used in order to supervise the detection process.We present experimental results illustrating the effectiveness of our method.
The Scenario of Rainfall-Discharge for Supporting the Optimization of Irrigation Reservoir Operation to Anticipate Extreme Flood

This research intends to build a scenario of mathematical modeling that can follow up the information of extreme climate does not give the same impact (mainly rainfall), so the water volume when the extreme rainfall will be happened, can be used for the irrigation and the water that cannot be used can be removed from the reservoir before flooding is happened, therefore, the function of reservoir to reduce flood can be increasing. The methodology consists of to obtain the rainfall due to the weather anomaly, the relation between rainfall and reservoir inflow, the decreasing of irrigation water requirement when the rainfall is high, the usage of crop intensity increasing, design flood, and the outlet capacity, the pattern of reservoir operation, the comparison of crop intensity, and the decreasing of flood risk. The result is hoped to be able to increase the available infrastructure (dam) by increasing the function of flood control and it remains to give priority for irrigation through the reservoir
View Synthesis: LiDAR Camera versus Depth Estimation

Depth-Image-Based Rendering (DIBR) can synthesize a virtual view image from a set of multiview images andcorresponding depth maps. However, this requires an accurate depth map estimation that incurs a high compu-tational cost over several minutes per frame in DERS (MPEG-I’s Depth Estimation Reference Software) even byusing a high-class computer. LiDAR cameras can thus be an alternative solution to DERS in real-time DIBR ap-plications. We compare the quality of a low-cost LiDAR camera, the Intel Realsense LiDAR L515 calibrated andconfigured adequately, with DERS using MPEG-I’s Reference View Synthesizer (RVS). In IV-PSNR, the LiDARcamera reaches 32.2dB view synthesis quality with a 15cm camera baseline and 40.3dB with a 2cm baseline.Though DERS outperforms the LiDAR camera with 4.2dB, the latter provides a better quality-performance trade-off. However, visual inspection demonstrates that LiDAR’s virtual views have even slightly higher quality thanwith DERS in most tested low-texture scene areas, except for object borders. Overall, we highly recommend usingLiDAR cameras over advanced depth estimation methods (like DERS) in real-time DIBR applications. Neverthe-less, this requires delicate calibration with multiple tools further exposed in the paper.
Future of Rankings in Higher Education

In the late 1990s, some institutions started discussing the idea of comparing universities and educational institutions according to certain criteria. Since then, the rankings of universities have become widespread. With the rapid spread, extended and developed scientific progress and technological development every day as we have not seen before, and with the increasing spread of the Internet, these sites have become dependent on the Internet to obtain the data they rely on in the ranking of these universities. Because the ranking of universities, educational institutions, higher education institutions, colleges, and institutes is one of the main elements that have been used in the past two centuries, and because the ranking of universities has become one of the most important ways and means to measure the development or decline of universities, it was important to clarify the mechanisms of the ranking of universities in This period of the twenty-first century and the expected perception for tanking of university for the future period. The aim of this research paper is to present a study on the methods and methodologies that can be used to measure the ranking of universities, taking into account the technological development that has taken place over the past period and to determine what is the possibility of relying on the ranking in the future as a tool to measure the progress and development of universities and the possibility of relying on the Internet as a reliable means of ranking. Observations regarding the educational institutions' perception of ranking are also discussed.
Sketch to Photo Conversion using Cycle-Consistent Adversarial Networks

It is proposed to use the cycle-consistent adversarial network as a way to convert images of sketches to images of photos. The network learns to perform the mapping from the domain of sketches to the domain of photos and due to its architecture also learns the inverse mapping from the domain of photos to the domain of sketches. The network converts sketches to photos by reducing a weighted sum of the validity, reconstruction and identity losses. The advantage of using a cycle-consistent adversarial network over other network architectures is that it is not mandatory to have aligned image pairs as its training set and works in an unpaired setting.
Hybrid CNN-LSTM Model for Answer Identification

User quest for information has led to development of Question Answer (QA) system to provide relevant answers to user questions. The QA task are different than normal NLP tasks as they heavily depend to semantics and context of given data. Retrieving and predicting answers to verity of questions require understanding of question, relevance with context and identifying and retrieving of suitable answers. Deep learning helps to produce impressive performance as it employs deep neural network with automatic feature extraction methods. The paper proposes a hybrid model to identify suitable answer for posed question. The proposes power exploits the power of CNN for extracting features and ability of LSTM for considering long term dependencies and semantic of context and question. Paper provides a comparative analysis on deep learning methods useful for predicting answer with the proposed method .The model is implemented on twenty tasks of babI dataset of Facebook .
Presence, flow, and narrative absorption: an interdisciplinary theoretical exploration with a new spatiotemporal integrated model based on predictive processing

<ns4:p>Presence, flow, narrative absorption, immersion, transportation, and similar subjective phenomena are studied in many different disciplines, mostly in relation to mediated experiences (books, film, VR, games). Moreover, since real, virtual, or fictional agents are often involved, concepts like identification and state empathy are often linked to engaging media use. Based on a scoping review which identified similarities in the wording of various questionnaire items conceived to measure different phenomena, we categorize items into the most relevant psychological aspects, and use this categorization to propose an interdisciplinary systematization. Then, based on a framework of embodied predictive processing, we present a new cognitive model of presence-related phenomena for mediated and non-mediated experiences, integrating spatial and temporal aspects and also considering the role of fiction and media design. Key processes described within the model are: selective attention, enactment of intentions, and interoception. We claim that presence is the state of perceived successful agency of an embodied mind able to correctly enact its predictions. The difference between real-life and simulated experiences (“book problem,” “paradox of fiction”) lays in the different precision weighing of exteroceptive and interoceptive signals.</ns4:p>
Digital citizenship

This chapter reviews essential topics such as how to behave online, the consequences of sharing personal information, searching for viable information, and working online. According to the Organisation for Economic Co-operation and Development, “empowering an active and ethical digital generation means equipping children to actively, positively, and responsibly engage in society, whether this takes place on or offline.” In a digital classroom, it is important to explore how digital citizenship can be taught in class. Digital citizenship involves competent and positive engagement with digital technology (access and skills) and active and responsible participation (empowerment and etiquette) Another part of being digital is planning time spent online when studying. The Pomodoro Technique, with the use of different apps, can facilitate students learning. If we don’t teach students how to use social media to make a difference and if we don’t teach students how to organize people around a good cause, we should not be surprised if they only use their devices for entertainment.