Reviewerly/exHarmony huggingface dataset:
Finding Fast Transients in Real Time Using a Novel Light-curve Analysis Algorithm

Abstract The current data acquisition rate of astronomical transient surveys and the promise for significantly higher rates in the next decade necessitate the development of novel approaches to analyze astronomical data sets and promptly detect objects of interest. The Deeper, Wider, Faster (DWF) program is a survey focused on the identification of fast-evolving transients, such as fast radio bursts, gamma-ray bursts, and supernova shock breakouts. It employs multifrequency simultaneous coverage of the same part of the sky over several orders of magnitude. Using the Dark Energy Camera mounted on the 4 m Blanco telescope, DWF captures a 20 s g -band exposure every minute, at a typical seeing of ∼1″ and an air mass of ∼1.5. These optical data are collected simultaneously with observations conducted over the entire electromagnetic spectrum—from radio to γ -rays—as well as cosmic-ray observations. In this paper, we present a novel real-time light-curve analysis algorithm, designed to detect transients in the DWF optical data; this algorithm functions independently from, or in conjunction with, image subtraction. We present a sample of fast transients detected by our algorithm, as well as a false-positive analysis. Our algorithm is customizable and can be tuned to be sensitive to transients evolving over different timescales and flux ranges.
BlockProof: A Framework for Verifying Authenticity and Integrity of Web Content

In the Literature, we can find several research works to help in the digital crime fight in order to prove integrity and authenticity of a published document, image or video. Among all the crimes, fake news certainly is among the most recurrent ones and needs to be mitigated. There are several Blockchain-based applications in order to make use of the benefits derived from technology, but little is found to verify the authenticity of Web content records as well as the history of all updates that have taken place in each Web content. Such kind of solution has become important nowadays as a way to cover the gap in the combat against fake news, for example. The purpose of this paper is to present BlockProof, a framework for verifying web content authenticity and integrity that offers a solution for content providers to register Web content, regardless of whether the page has dynamic or static content, in addition to enabling the consultation of the history of all records made for a given URL. We understand that such kind of solution may be useful to data producers/providers to provide evidence that they are in compliance with the fight against fake news, for instance.
The use of distance learning technologies in preparing students (future biology teachers)

The current stage in the development of pedagogical education in the world is characterized by a special intensity and scale of transformations due not only to the urgent tasks of forming a young personality, but also to the involvement of the education system in the world integration processes taking place in the context of informatization and globalization. The purposeful acquisition of knowledge, skills and abilities by future biology teachers, an orientation towards the development, development and implementation of effective mechanisms for its formation is an important component of education. The global educational community is faced in 2020 with a global challenge caused by the pandemic caused by the spread of the SARS-CoV-2 coronavirus. The governments of most countries were not ready for such a large-scale pandemic; their practical measures were formed on a situational basis. To counter the spread of COVID-19, educational institutions have been temporarily closed almost all over the world. The purpose of the article is to study the features of the implementation of the distance education system in the process of teaching students - future biology teachers, including the study of the main advantages and disadvantages of this process. For this, the methods of theoretical research of specialized literature were used.
THE USING FEATURES OF THE SCIENCETAINMENT METHOD IN POPULAR SCIENCE JOURNALISM (ON THE EXAMPLES OF THE PROGRAMS OF THE TV CHANNEL “SCIENCE”)

The article is devoted to identifying the using features of the sciencetainment method in Russian popular science TV programs. Currently, the method is actively used on television to attract the attention of viewers and implement several functions of journalism at once – the enlightenment and recreational ones. The study presents the characteristics of sciencetainment as a method of television journalism, describes causes for such a content to be in demand, identifies signs of the presence of sciencetainment in popular science TV projects, defines the advantages and disadvantages of using that method.
The Impact of IT Application Control on the quality of the Audit Evidence: An Application Example

In recent years, firms have started to invest millions of dollars on information technologies for a number of reasons, such as managing their operations more effectively, expanding trade channels, and preventing fraudulent activities. Parallel to these investments in IT, financial reporting processes have undergone serious changes. In addition to the creation of all accounting records that have bases on financial statement items with different IT applications, factors such as the expansion and diversification of companies’ operations have made it difficult to audit accounting records with classical methods. In the past, while accounting records were created by people in the classical way, thousands of records could be created automatically very quickly thanks to IT applications. For this reason, the reliability of audit evidence obtained by ignoring IT application controls has become questionable. In this study, first of all, the effect of IT on audit processes and audit evidence has been explained. Then, IT application control tests were applied in a manufacturing enterprise and the effect of the obtained audit evidence on the audit was investigated. Since our paper is one of the first studies to examine the effects of IT practices on audit quality in the literature, it will lead the further studies.
Modeling and Performance Analysis of Resource Provisioning in Cloud Computing using Probabilistic Model Checking

Cloud computing consists of an advanced set of technologies that allow cloud providers to offer computing resources such as infrastructure, platforms and applications to be accessible over the Internet as services. Cloud computing relies on virtualization of resources in the cloud data centers, where a set of Virtual Machines (VMs) are deployed on Physical Machines (PMs) to provision and serve user requests. Due to the dynamic nature of cloud environments and complexity of resources virtualization, as well as the diversity of user’s requests, developing effective techniques to evaluate and analyze the performance of cloud centers has become highly required. In this paper, we propose the use of probabilistic model checking as an effective framework for the evaluation and the performance analysis of resource provisioning in the cloud. Based on an analytical model for resource provisioning in Infrastructure-as-a-Service (IaaS) cloud, we build a stochastic model using the probabilistic model checker PRISM and analyze it against a useful set of probabilistic and reward properties that help to measure and analyze cloud performance in an efficient way
Intelligent Interactive English Teaching Discrete Data Modeling and Simulation

This study attempts to explore the discrete data modeling and simulation of smart interactive English teaching based on mobile APP, expecting to explore the effective teaching strategies for deep integration of mobile APP technology and smart interactive English teaching, and to conduct discrete data modeling and simulation of English teaching. In this study, we propose a discrete data modeling algorithm for smart interactive English teaching based on discrete data points and normal vectors using B splines. By adding normal vector constraints, this method can maintain good fitting results even when the data points are discrete. The method consists of four parts, namely, discrete point parameterization, B-spline dominant point selection, node vector determination, and least-squares fitting, including both data point error and normal vector error. The mobile APP-based initial intelligent interactive English teaching should adhere to the four principles of autonomy of English course learning, adhere to teacher dominance in the language classroom, grasp the humanistic nature of English course content, and focus on the practical nature of English course implementation. The interactive learning environment based on mobile APP can promote students’ in-depth understanding of knowledge. The higher the student’s interest in learning, the deeper the degree of understanding; the more diverse the learning methods, the more it can promote students’ in-depth understanding of knowledge; the stronger the degree of interaction, the higher the level of understanding. The discrete data modeling of smart interactive English teaching proposed in this study provides a theoretical reference for the design and production of interactive learning resources for English courses, according to which the interactive learning resources for English courses are produced to make up for the lack of English course resources.
Design of a 3D reconstruction model of multiplane images based on stereo vision

Aiming at the problem of poor application effect of traditional image 3D reconstruction methods, a multi plane image 3D reconstruction model based on stereo vision is designed in this study. To obtain more useful image feature information, first, the features of multi plane images are collected to realize the arbitrary angle cutting of multi plane images, in which the polar coordinate form is added, and the three-bit coordinate conversion algorithm is designed. Then, based on the running process of the algorithm under stereo vision, the effective results of three-dimensional reconstruction of multi plane images are obtained. The experimental results show that the multiplane image 3D reconstruction model based on stereo vision is feasible, effective and flexible.
Conceptual framework of computer application for promoting tsunami evacuation route

Disaster resilient is one of development priority in Indonesia. Programs related disaster risk reduction have been considered as important agenda by government and community, especially in cities that hit by earthquake and tsunami. Serious games have been used in many fields, including disaster management. This paper aims to introduce the conceptual framework of a computer application for promoting tsunami evacuation route in Baitussalam, Aceh Besar, Indonesia. The computer application is built based on serious games concept. Activity Theory-based Model of Serious Games (ATMSG), which consist of the gaming activity, the learning activity and the instructional activity in designing process, employed as the methodology to build the framework. The result present ini Serious Game diagram which explain the activities in the game. The activities deliver through scenarios which categorized based on land used and activities of the local community.
Generative Adversarial Classification Network with Application to Network Traffic Classification

Large datasets in machine learning often contain missing data, which necessitates the imputation of missing data values. In this work, we are motivated by network traffic classification, where traditional data imputation methods do not perform well. We recognize that no existing method directly accounts for classification accuracy during data imputation. Therefore, we propose a joint data imputation and data classification method, termed generative adversarial classification network (GACN), whose architecture contains a generator network, a discriminator network, and a classification network, which are iteratively optimized toward the ultimate objective of classification accuracy. For the scenario where some data samples are unlabeled, we further propose an extension termed semi-supervised GACN (SSGACN), which is able to use the partially labeled data to improve classification accuracy. We conduct experiments with real-world network traffic data traces, which demonstrate that GACN and SS-GACN can more accurately impute data features that are more important for classification, and they outperform existing methods in terms of classification accuracy.
Towards Inclusive and Sustainable Infrastructure Development through Blockchain-enabled Asset Tokenization: An Exploratory Case Study

Abstract Infrastructure development is a key to supporting the economy and building social resilience. Unfortunately, existing infrastructure financing models struggle with multiple issues to close the widening financial gap and integrate environmental, social, and governance (ESG) factors to improve resilience and achieve sustainable growth. With the emergence of innovative technology, blockchain-enabled asset tokenization shows the potential to create a new economic model to integrate non-financial values, such as positive social and environmental impacts, into tradable cryptographic tokens. Tokenization offers opportunities to generate long-term positive social and environmental impacts, along with better financial returns, which further improves the profitability and bankability of infrastructure projects. SolarCoin, WePower Token, and ZiyenCoin are analyzed in this exploratory case study research to demonstrate how blockchain-enabled asset tokenization can be applied to infrastructure development. It is identified that tokenization promotes inclusiveness and sustainability through shareholder empowerment, incentive monetization, and finance optimization. Obstacles hindering the broader adoption of tokenization and policy implications are also discussed.
RETRACTED ARTICLE: Investigation of facial aggression detection techniques based on facial action units using machine learning

In recent years, Facial Expression Recognition (FER) became a major interest for researchers because of its importance in reflecting the non-verbal sentiments of people. The expression recognition depends on the extraction of facial landmarks and tracking their changes in response to stimuli or feelings. The landmarks that depend on the precise movement of facial muscles are called Action Units (AUs) and can be used to recognize several facial expressions such as anger, happiness, sadness, fear, surprise, disgust, or neutral. Particularly, Aggressive facial expression attracted more interest than others because of its contribution to the detection of the tendency to threaten or cause physical harm to society. Therefore, it is important to design a system with high accuracy for predicting aggressive behavior. However, not all Action Units contribute to the facial aggression detection process. This paper provides a comparative approach by experimentally assessing the performance of four machine learning algorithms: Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), Decision tree (DT), and Naïve Bayes (NB) using facial features that are selected using different feature selecting algorithms. Chi-square, ReliefF, and F test methods are used for creating distinctive facial feature sets. Then, the machine learning algorithms are used for finding the optimum feature combination by reaching the highest accuracy. The numerical results indicated that the best performance is achieved by using the Chi-square selection algorithm with the Support Vector Machine (SVM) classifier. The results also showed that there are features that impose a negative influence on calculating the detection accuracy and consume resources without producing satisfying results. Removing these features increased the overall detection performance by the means of offloading the system from undesired features.
Semantic Shilling Attack against Heterogeneous Information Network Based Recommend Systems

The recommend system has been widely used in many web application areas such as e-commerce services. With the development of the recommend system, the HIN modeling method replaces the traditional bipartite graph modeling method to represent the recommend system. But several studies have already showed that recommend system is vulnerable to shilling attack (injecting attack). However, the effectiveness of how traditional shilling attack has rarely been studied directly in the HIN model. Moreover, no study has focused on how to enhance shilling attacks against HIN recommend system by using the high-level semantic information. This work analyzes the relationship between the high-level semantic information and the attacking effects in HIN recommend system. This work proves that attack results are proportional to the high-level semantic information. Therefore, we propose a heuristic attack method based on high-level semantic information, named Semantic Shilling Attack (SSA) on a HIN recommend system (HERec). This method injects a specific score into each selected item related to the target in semantics. It ensures transmitting the misleading information towards target items and normal users, and attempts to interfere with the effect of the recommend system. The experiment is dependent on two real-world datasets, and proves that the attacking effect is positively correlate with the number of meta-paths. The result shows that our method is more effective when compared with existing baseline algorithms.
Vantablack Shielding of Superconducting Qubit Systems

Circuit quantum electrodynamics (cQED) experiments on superconducting qubit systems typically employ radiation shields coated in photon absorbing materials to achieve high qubit coherence and low microwave resonator losses. In this work, we present preliminary results on the performance of Vantablack as a novel infrared (IR) shielding material for cQED systems. We compare the coherence properties and residual excited state population (or effective qubit temperature) of a single-junction transmon qubit housed in a shield coated with a standard epoxy-based IR absorbing material, i.e. Berkeley Black, to the coherence and effective temperature of the same qubit in a shield coated in Vantablack. Based on a statistical analysis of multiple qubit coherence measurements, we find that the performance of the radiation shield coated with Vantablack is comparable in performance to the standard coating. However, we find that in the Vantablack coated shield the qubit has a higher effective temperature. These results indicate that improvements are likely required to optimize the performance of Vantablack as an IR shielding material for superconducting qubit experiments and we discuss possible routes for such improvements. Finally, we describe possible future experiments to more precisely quantify the performance of Vantablack to improve the coherences of more complex cQED systems.
Design of deep convolution feature extraction for multimedia information retrieval

Purpose The analysis of multimedia content is being applied in various real-time computer vision applications. In multimedia content, digital images constitute a significant part. The representation of digital images interpreted by humans is subjective in nature and complex. Hence, searching for relevant images from the archives is difficult. Thus, electronic image analysis strategies have become effective tools in the process of image interpretation. Design/methodology/approach The traditional approach used is text-based, i.e. searching images using textual annotations. It consumes time in the manual process of annotating images and is difficult to reduce the dependency in textual annotations if the archive consists of large number of samples. Therefore, content-based image retrieval (CBIR) is adopted in which the high-level visuals of images are represented in terms of feature vectors, which contain numerical values. It is a commonly used approach to understand the content of query images in retrieving relevant images. Still, the performance is less than optimal due to the presence of semantic gap among the image content representation and human visual understanding perspective because of the image content photometric, geometric variations and occlusions in search environments. Findings The authors proposed an image retrieval framework to generate semantic response through the feature extraction with convolution network and optimization of extracted features using adaptive moment estimation algorithm towards enhancing the retrieval performance. Originality/value The proposed framework is tested on Corel-1k and ImageNet datasets resulted in an accuracy of 98 and 96%, respectively, compared to the state-of-the-art approaches.
China’s New Developments in Crackdown on Criminal Copyright Infringement in the Digital Era

Abstract Under the external pressure mainly in the frame of the TRIPS and out of the intrinsic motivation to strengthen intellectual property protection from the height of national strategy, China raised the protective level under criminal law and introduced punitive damages in all intellectual property infringement cases. Rulemaking activities enlarged the scope of criminal offences of Internet piracy, and law enforcement became more active than before. Specifically, tricks for pirating online games, including setting up a private service and hacking games to cheat can be crimes. Additionally, the unauthorised dissemination of copyrighted works through information networks (peer-to-peer file-sharing technology or leeching websites) and circumvention or disability of technological protection measures have been criminalised. The Internet service provider may face a criminal sanction when it fails to delete or block pirated content. The commercial scale, a constructive element of criminal copyright infringement, gained a measuring standard in online piracy cases.
Reverse Social Engineering to Counter Social Engineering in Mobile Money Theft: A Tanzanian Context

Social engineering entails deception where one manipulates individuals into divulging confidential or any personal information that may be used for fraudulent purposes. In mobile money theft, the attackers plan a “lure” to tempt a victim directly, via mobile phones; mostly to gain money by proposing a phony undertaking. The victim is often asked to pay some money to facilitate a lucrative undertaking, which in reality is phony. Once the victim has paid the money, the attackers become inaccessible. Reverse social engineering entails deception of the predator by the pray in order to capture or discourage the predator. Through a case study, this paper investigates and presents an incident where a victim of mobile social engineering attempts to reverse the process in order to arraign the attackers.
Secure Cryptography with ngDH protocol along with RSA &amp; AES Algorithm

Abstract—RSA cryptography is an asymmetric communication protocol, and, AES is the most used symmetric-key cryptography protocol, both are facing cryptanalysis attacks.. The most famous key exchange protocol is Diffie-Hellman; but it has an issue of Logjam attack that allows man-in-middle attack in Diffie-Hellman. Thus, we combine RSA, AES, and Diffie-Hellman algorithm to provide security on the key exchange protocol, called next generationDH or ngDH. Our key objective is to provide security to the Diffie-Hellman Algorithm. Therefore, ngDH does not share the data publicly with the intended party. Instead, ngDH encrypts all shareable data in the time of key exchange by encrypting using the AES algorithm. ngDH uses the RSA algorithm and retrieves the public key to avoid a man-in-the-middle attack. Thus, we demonstrate how to provide security to the Diffie-Hellman algorithm to defeat various kinds of attacks. Index Terms—Cryptography, RSA cryptography, Diffie-Hellman, Symmetric, Asymmetric, Public key, Encryption, Attacks, Security, Networking, Communication.