Reviewerly/exHarmony huggingface dataset:
Network Security Using Python: An Empirical Analysis of Advance Encryption Standard (AES)

Nowadays, security in network environment is becoming a challenging problem, providing prevention of confidential records arises as a critical task. Modern creation in network can reduce the rate of uncertainty as-well-as at the same time growth of threats increases rapidly in past few decades i.e. vulnerabilities, and potential risks. Confidentiality of crucial informationis one of the main objectives, to protect integrity when its traveling from one node to another, ensure it can’t divulge by malicious insider, anonymous, trust, and malicious attacks.In this research paper, the proposed solution is to design interface where communication can be done in a secure manner, behind it has a mechanism which hide essential information, symmetric key cryptographic algorithm named advance encryption standard (AES) using python with additional library called pycrypto and tries to formulate algorithm efficiency, accuracy, execution time plus system capability.
Toward realizing talking quantum movies: Synchronization of audio and visual content

With the ubiquity and primacy of image- and video-processing, a new subdiscipline ensuring a smooth transition from digital to quantum image-processing has emerged. This study proposes a framework to synchronize audio and visual content to realize talking quantum movies. This framework segments a movie into frame, audio, and time components. A multichannel representation for quantum images (MCQI) is used to encode the still images that make up movie frames. In addition, the amplitude content of the flexible representation of the quantum audio (FRQA) signal is recorded at each instant of time. The synchronization of audio and frames in the movie is accomplished through a quantum sequence of time. The feasibility of this framework is demonstrated by a simple simulation, and some possible applications are discussed.
A FILTER MODEL FOR TEXT CATEGORIZATION AGAINST ONLINE HATE SPEECHES

Text classification is a method of grouping a document text into different predefined categories. This method has been applied in different areas such as classification of scientific articles, spam filtering, and classification of document genre. Text classification is a popular task in data mining because of its level of accuracy and easy application. The Internet is a common message transmission medium among many people, billions of messages move around the internet on a daily basis through different platforms on the internet such as e-mail, Facebook, Twitter, etc. Some of these messages are being transmitted with wrong motives, thus it became imperative to design a model for filtering some of these messages using data mining algorithms to sieve away the unwanted messages from circulation. In the light of this, this paper applied three data mining techniques namely: Support Vector Machine (SVM), Naive Bayes and K-Nearest Neighbour (KNN) to develop models that can be applied to filter messages from Facebook and e-mail to counter circulation of online hate speeches on these platforms. It also compared the performance of these models against collected data to identify the state of the art text classifier. It was observed that the Naive Bayes algorithm performed better than the other two with an accuracy of 61.5 and ROC of 0.66.
Towards a formally verified implementation of the MimbleWimble cryptocurrency protocol

MimbleWimble is a privacy-oriented cryptocurrency technology encompassing security and scalability properties that distinguish it from other protocols of the kind. In this paper we present and briefly discuss those properties and outline the basis of a model-driven verification approach to address the certification of the correctness of a particular implementation of the protocol.
A Lightweight Secure Development Process for Developers

Following a secure development process when developing software can greatly increase the security of the software. Several secure development processes have been developed and are available for com ...
An Exploration of the Pattern of Managing Volunteers in the Construction Jihad

The purpose of this study was to explore the pattern of managing volunteer forces in Construction Jihad. To achieve this, two strategies, namely, the grounded theory and interpretive structural modelling were used. The present research is a developmental one in terms of nature, an idealist and pragmatic in terms of ontology and is based on subjective interpretations and objective knowledge in terms of epistemology. The research population consist two sections of the Construction Jihad organisation managers and employees. The findings of the research led to the identification of 903 primary codes in the form of 82 concepts. In the next step, the concepts were reduced to 17 more abstract categories. Then, the type of relationship between these elements was evaluated in pairs by the experts and based on that, the final pattern of the research was drawn up. In this pattern, the categories of religious values, empathic leadership, and trust are elements that affect other elements; however, they themselves are not affected, which is why they were plotted in the first level.
Perancangan Nganjuk E-Tourism Berbasis Android Dengan Algoritma Dijkstra Untuk Pencarian Jalur Terdekat Objek Pariwisata

Nganjuk is an area that has a myriad of very beautiful tours. Some of the attractions are Sedudo Waterfall,Embung Estumulyo, Ngetos Temple and many more interesting sights worth visiting. But efforts to promotethe tourism sector from the Nganjuk regency is still limited, such as the distribution of brochures from theDepartment of Tourism in the city center at the time of the Day of Nganjuk Anniversary. Use Dijkstra'sAlgorithm method as the nearest distance counter and use the Google Maps service to present a visual map.Nganjuk Tourism Application can guide the tourists by showing the closest route on the map to the touristlocation. Seeing Nganjuk tourism potential is so great then this thesis makes the application Nganjuk Tourbased on Android. This minithesis hopes to increase the desire of tourists to visit Nganjuk and see Nganjukcultural charm that is so special.Keywords: Android, Tourism, Nganjuk, Dijkstra Algorithm.
Desarrollo de recursos educativos multimedia para fortalecer los procesos de enseñanza-aprendizaje en los grupos de alumnos del Colegio La Inmaculada Jesuitas-Perú, a través de la capacitación de los docentes en el manejo de las TIC

The purpose of this article is to capture the results obtained after having made the analysis of the pedagogical quality assessment of multimedia material that was developed from the detection of needs of the La Inmaculada School in Lima, Peru. Teachers required a ubiquitous learning material, that that it should be available anywhere and at any time. After analyzing the needs of the school, it was agreed to design video tutorials as open multimedia resources so that teachers can master the use of ICT in their personal learning pace.
An Overview of the Ludii General Game System

The Digital Ludeme Project (DLP) aims to reconstruct and analyse over 1000 traditional strategy games using modern techniques. One of the key aspects of this project is the development of Ludii, a general game system that will be able to model and play the complete range of games required by this project. Such an undertaking will create a wide range of possibilities for new AI challenges. In this paper we describe many of the features of Ludii that can be used. This includes designing and modifying games using the Ludii game description language, creating agents capable of playing these games, and several advantages the system has over prior general game software.
Vulnerability Management Procedure

The purpose of this procedure is to outline the steps in IT vulnerability management adhering to the Vulnerability Management Policy, to ensure that appropriate tools and methodologies are used to assess vulnerabilities in systems or applications, and to provide remediation.
Centroid-Based Library Management and Document Clustering

Based on the previous fundamentals and findings, in this chapter, hierarchic and centroid-based document management algorithms will now be presented, which are inspired by the way of how human librarians classify, sort and catalogue incoming documents. For this purpose, they calculate the distance of centroid terms in a local co-occurrence graph as a metric to determine the documents’ semantic closeness, to generate (sub)clusters of documents and to assign them to processing nodes (child nodes which are created for this purpose on-the-fly). These centroid-based library management and clustering algorithms are designed to run decentrally on peers (the librarians) of a P2P-network. Furthermore, this approach is equally used to classify and answer incoming queries as well as to route and forward them to semantically matching child nodes.
Extractive Multi-document Summarization of News Articles

Publicly available data grows exponentially through web services and technological advancements. To comprehend large data-streams multi-document summarization (MDS) can be used. In this research, t ...
Epistemic Sortal Logic

A notion of an ideal sense of knowledge is introduced. A formal language for sortals with a propositional operator formally representing such a notion is then defined. The formal language will contain temporal operators as well. A formal semantics for the language is defined and on its basis three sorts of semantic validity are distinguished, which correspond to nativist and non-nativist views of concept-formation. A formal system for epistemic-radical nativism is stated. Soundness and completeness can be shown for the system. The soundness and completeness proofs for non-nativist as well as partial nativist validity are left as open problems.
Fault Identification in Deadline Sensitive Applications in Cloud Computing

Cloud Computing is a term that has risen exponentially over the last decade. These days when everyone has a handheld device, one wants to lap up the entertainment streaming on to their mobile directly from the cloud. The Service Level Agreement ensures that the quality of the content that is being provided to the consumers is error free. Thus making no compromise on the part of error that could disrupt the ongoing processes of the cloud computing. In case a failure occurs, the main objective is that the system should not stop functioning. The fault-tolerant system is mostly described as a strategy to build a secondary file along with the primary one for any mishap that might occur during the transaction, the secondary could take place of the primary one, and the consumer does not miss out anything. The challenge for the cloud service providers in this era is to provide a seamless and uninterrupted service. In this paper, we have discussed the issues that are generally quite common in terms of fault tolerance and proposed a model that tracks the fault once the task is loaded on to the virtual machine.
Simulating Homomorphic Evaluation of Deep Learning Predictions.

Convolutional neural networks (CNNs) is a category of deep neural networks that are primarily used for classifying image data. Yet, their continuous gain in popularity poses important privacy concerns for the potentially sensitive data that they process. A solution to this problem is to combine CNNs with Fully Homomorphic Encryption (FHE) techniques. In this work, we study this approach by focusing on two popular FHE schemes, \(\mathsf {TFHE}\) and \(\mathsf {HEAAN}\), that can work in the approximated computational model. We start by providing an analysis of the noise after each principal homomorphic operation, i.e. multiplication, linear combination, rotation and bootstrapping. Then, we provide a theoretical study on how the most important non-linear operations of a CNN (i.e. \(\max , \mathtt {Abs}, \mathtt {ReLU} \)), can be evaluated in each scheme. Finally, we measure via practical experiments on the plaintext the robustness of different neural networks against perturbations of their internal weights that could potentially result from the propagation of large homomorphic noise. This allows us to simulate homomorphic evaluations with large amounts of noise and to predict the effect on the classification accuracy without a real evaluation of heavy and time-consuming homomorphic operations. In addition, this approach enables us to correctly choose smaller and more efficient parameter sets for both schemes.
PENGARUH KECERDASAN EMOSIONAL TERHADAP KINERJA MELALUI KOMPETENSI DAN MOTIVASI DOSEN KEPERAWATAN DI KOTA MAKASSAR

ABSTRACK This research was conducted on November 3, 2018 until December 3, 2018. The type of research used was quantitative with an associative type, because researchers wanted to know whether there were any influences between the variables through testing the formulated hypothesis, the population in this study amounted to 60 respondents and determining the sample based on total sampling, the tools used in data collection are questionnaire. The results of this study indicate that based on the value of tcount of 0.023 it is smaller than t table 0.256, which means that the mediation parameters have a positive and insignificant effect. This means that emotional intelligence has a positive and not significant effect indirectly on lecturer performance through the competence of nursing lecturers in the city of Makassar. Then the Sixth Hypothesis (H6) is rejected, while the value of t count of 0.363 is greater than ttable 0.256, which means that the parameter the mediation had a positive and significant effect. This means that emotional intelligence has a positive and significant effect indirectly on lecturer performance through the motivation of nursing lecturers in the city of Makassar, so the Sixth Hypothesis (H6) is accepted. Keywords: Emotional Intelligence, Competence, Motivation, Performance
Automatic topic labelling for text document using Ontology of graph-based concepts and dependency graph

Topic labelling is an important task of text mining. It supports assigning proper topic labels to the text documents. In this paper, we present a novel approach of using graph-based concept matching approach in solving automatic topic labelling task. Our proposed model demonstrates that the quality of automatic topic labelling task for text documents can be improved, in comparison with traditional keyword-based concept matching approach. In this paper, we propose a novel approach of automatic ontology-driven topic labelling. Our proposed model is considered as a semi-supervised approach. It uses existed ontologies as the pre-knowledge base for topic identification in text documents. We performed the experiments on the real-world ACM's documents to show the effectiveness of our proposed model in solving topic labelling task. The experimental results on real-world and standard datasets demonstrate that our proposed model can leverage the output accuracy of topic labelling task in text documents.
Telling the untold stories

London Business School ReviewVolume 30, Issue 1 p. 48-49 Original Article Telling the untold stories Rajal Pitroda, Rajal PitrodaSearch for more papers by this author Rajal Pitroda, Rajal PitrodaSearch for more papers by this author First published: 27 June 2019 https://doi.org/10.1111/2057-1615.12289 MOVIE PRODUCER WHOSE FILMS AIM TO CHANCE THE NARRATIVE – AND GIVE VOICE TO THE UNDER-REPRESENTED ALONG THE WAY AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume30, Issue1January 2019Pages 48-49 RelatedInformation
DEM-based Convolutional Neural Network Modeling for Estimation of Solar Irradiation: Comparison of the Effect of DEM Resolutions

DEM-based Convolutional Neural Network Modeling for Estimation of Solar Irradiation: Comparison of the Effect of DEM Resolutions Jae Heo, Jaehoon Jung, Byungil Kim and Sanguk Han Pages 854-861 (2019 Proceedings of the 36th ISARC, Banff, Canada, ISBN 978-952-69524-0-6, ISSN 2413-5844) Abstract: Recently, the use of solar panels has increased because of the growing demand for solar energy. To determine appropriate installation sites of photovoltaic (PV) panels, estimating the available solar energy at a certain area is important to predict the amount of power generation and planning PV plant operations. However, traditional data-driven approaches (e.g., machine learning) do not fully reflect the topographical characteristics of surrounding regions in the solar energy estimation, and the impact of data resolution (e.g., map scales) on the prediction accuracy has rarely been investigated. Thus, this paper presents a solar irradiation prediction model using a convolutional neural network (CNN) designed to process digital elevation map (DEM) images. Furthermore, an analysis of the impact of two different resolutions (i.e., 30 m and 60 m resolutions) on the model performance is also presented. A total of 25,000 DEM images are extracted from the national map of South Korea for both resolutions and then used as an input to train the CNN models. The results show that the CNN-based prediction models can be used to estimate the solar irradiation with high accuracy (e.g., mean square errors of 0.0018 and 0.0032 for 30 m and 60 m resolutions). It was also found that data resolution affects the performance of the CNN-based models. With an accurate estimation of the available solar energy at a certain site, the sites generating more power can potentially be evaluated and selected by searching a DEM on a large scale. Keywords: Solar energy, solar irradiation prediction, GIS, convolutional neural network DOI: https://doi.org/10.22260/ISARC2019/0115 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
Robust Query Execution Time Prediction for Concurrent Workloads on Massive Parallel Processing Databases

Reliable query execution time prediction is a desirable feature for modern databases because it can greatly help ease the database administration work and is the foundation of various database management/automation tools. Most exiting studies on modeling query execution time assume that each individual query is executed as serialized steps. However, with the increasing data volume and the demand for low query latency, large-scale databases have been adopting the massive parallel processing (MPP) architecture. In this paper, we present a novel machine learning based approach for building a robust model to estimate query execution time by considering both query-based statistics and real-time system attributes. The experiment results demonstrate our approach is able to reliably predict query execution time in both idle and noisy environments at random levels of concurrency. In addition, we found that both query and system factors are crucial in making stable predictions.
Regulatory and Market Challenges of Initial Coin Offerings

This article analyzes the main problems and the solutions adopted in the market for Initial Coin Offerings (ICO), an alternative financing solution that has experienced spectacular growth and notoriety in recent years. This market relies on the use of Blockchain protocols and is, therefore, characterized as disintermediated, decentralized and unregulated. The problems we identify in this article, their severity, and the solutions currently being adopted to address them, lead us to conclude that it is unlikely that either of these characteristics will survive in the near future. Our results also indicate that the concerns expressed by regulators and other market agents regarding ICO markets are well founded. We find it particularly disturbing that such a new, revolutionary market already displays many of the problems of traditional financial markets, and that these problems were exactly the ones that occurred at the genesis of the last financial crisis.
CobWeb: A Research Prototype for Exploring User Bias in Political Fact-Checking

This publication is by UT affiliates that was featured in the September Good Systems Network Digest in 2020.
RDAP jCard Profile

The Registration Data Access Protocol (RDAP) is used by RegionalInternet Registries (RIRs) and Domain Name Registries (DNRs) toprovide access to their resource registration information. RDAP usesjCard to convey information about individuals and other entities: forexample, for the technical contact for a domain name. In practice,server operators are only using a small subset of jCard'sfunctionality, so in an effort to simplify the requirements on theclient side, this document defines a jCard profile for use with RDAP.
Overcoming Catastrophic Interference with Bayesian Learning and Stochastic Langevin Dynamics

Neural networks encounter serious catastrophic forgetting when information is learned sequentially. Although simply replaying all previous data alleviates the problem, it may require large memory to store all previous training examples. Even with enough memory, joint training can be infeasible if access to past data is limited. We developed generative methods for preventing catastrophic forgetting that do not require the presence of previously used data. Developed methods are based on activation maximization of output neurons and on sampling of posterior probability of data distribution. The methods can work for regular feedforward networks. The proof of concept experiments were performed on publicly available datasets.
High-dimensional temporal mode propagation in a turbulent environment

Temporal modes of photonic quantum states provide a new framework to develop a robust free-space quantum key distribution (QKD) scheme in a maritime environment. We show that the high-dimensional temporal modes can be used to fulfill a persistent communication channel to achieve high photon-efficiency even in severe weather conditions. We identify the parameter regimes that allow for a high-fidelity quantum information transmission. We also examine how the turbulent environment affects fidelity and entanglement degree in various environmental settings.
Asymmetric Message Franking: Content Moderation for Metadata-Private End-to-End Encryption.

Content moderation is crucial for stopping abusive and harassing messages in online platforms. Existing moderation mechanisms, such as message franking, require platform providers to be able to associate user identifiers to encrypted messages. These mechanisms fail in metadata-private messaging systems, such as Signal, where users can hide their identities from platform providers. The key technical challenge preventing moderation is achieving cryptographic accountability while preserving deniability.
An Analysis of a Large Scale Wireless Image Distribution System Deployment

In this paper, we describe two setups of a wireless image distribution system using different network architectures. In the first setup, commercial grade network equipment was used in the network infrastructure of the system. In the other setup, the network infrastructure consists of a wireless mesh of NICT NerveNet nodes. For the first setup, results showed that the choice of hardware and network equipment used were sufficient to support the load of the system in an auditorium with a capacity of about 160 people. For the NerveNet setup, it superseded the first setup in terms of quick and clean setup, leaving the performance aspect to be further improved.