Reviewerly/exHarmony huggingface dataset:
Analysis of paddy productivity using normalized difference vegetation index value of sentinel-2 and UAV multispectral imagery in the rainy season

Abstract Rice is one of the primary food sources for people worldwide, especially in Indonesia. In 2018, Indonesia produced approximately 56.5 million tons of unhusked dry rice ready for milling with a total area of around 10.9 million hectares. Until these days, the estimation of rice productivity was done by the Indonesia Statistics Agency (BPS) and the Ministry of Agriculture. Aerial Images and Normalized Difference Vegetation Index (NDVI) were viewed as a tool that helps in monitoring and observing the crops. NDVI was used to analyze the paddy growth from planting until harvesting. Usage of Satellite Sentinel-2 and UAV was meant to compare the accuracy between them. Analysis of paddy productivity was used during the vegetative, reproduction, and maturity phases. The used methods are regression and correlation analysis. The model from the analysis was used to estimate the productivity of paddy. The result shows that the models with the highest correlation from both Sentinel-2 and UAV were obtained from analysis on the vegetative phase. This means the implemented NDVI value was from planting until the peak value in one cultivating season.
Assessing Application Lifecycle Management (ALM) Potentials from an Industrial Perspective

The notion of software lifecycle management requires the incorporation of effective software engineering processes that encompass not only development but also deployment and maintenance. Although software engineering methodologies continue to emerge, software development continues to experience a significant amount of failure rates. To overcome the complexities of modern software development, we envision that software developers should familiarize themselves with emerging tools to make required changes incrementally by rigorously tracking deployment. Application Lifecycle Management (ALM) is an emerging trend that reflects such a novel attitude towards software development, which blends the tasks performed by a firm’s software development and systems operations teams, monitor software artifacts from inception to completion while improvements are documented. The outcome of this study is to provide guidance for practitioners in tailoring ALM practices from the idea phase through the selection of the underlying toolset. Ultimately, an assessment at an industrial scale is conducted to manage the ALM transformation using in a large-scale corporate environment. Overall, these results indicate that participatory action research is a robust approach to investigate complex software engineering issues.
Designing Enterprise Architecture for Distributor of Consumer Product Using TOGAF ADM

Abstract The purpose of this research is to design enterprise architecture for CV. Adiwarna Tunggal Jaya in accordance with its capacity as a distributor of consumer products. CV. Adiwarna Tunggal Jaya does not yet have an enterprise architecture blueprint as a reference for creating integrated information systems. The method used in designing enterprise architecture in this research is The Open Group Architecture Framework Architecture Development Method (TOGAF ADM) as a framework that will help to design a CV. Adiwarna Tunggal Jaya. The steps applied in TOGAF ADM in this research will only use four phases of the nine phases, namely Architecture Vision, Business Architecture, Information Systems Architectures, and Technology Architecture. The output of this research is to provide a blueprint for enterprise architecture guidance that can help CV. Adiwarna Tunggal Jaya to get a clear vision and mission of the company to improve its business processes and supports achieving its strategic goals and as a guide for a future development. The conclusion of this research is that the design of the enterprise architecture using TOGAF ADM can be used by the CV. Adiwarna Tunggal Jaya according to the documentation and processes in progress and can provide a blueprint for the development of information systems to support ongoing business processes.
Body RemiXer

Body RemiXer is a mixed reality installation that connects immersants across the virtual/actual divide through emergent tactility and abstract embodiment. Using a virtual reality headset, Kinect and projections, the installation explores the potential of immersive technology to create copresent experiences that foster intercorporeal relationships between immersants wearing a headset and those using the projections. Immersants' bodies are at the center of the installation, activated as a site for social exchange. Body RemiXer has been exhibited at an art festival and at several smaller events. The authors' observations during these exhibits reveal Body RemiXer's capacity to disrupt social norms and stimulate new connections.
Hardware Implementation of a Fast 3D Anaglyph Image Watermarking Framework for Integration in Consumer Electronics Devices

Due to the unprecedented growth of the internet and portable consumer imaging devices, copyright protection of digital images has become a topical affair. In this paper, a fast Tensor-SVD based red-cyan anaglyph 3D image watermarking has been presented which provides high imperceptibility, and robustness. To integrate the Human Visual System modelling into our framework, integer lifting wavelet transform has been incorporated. After that, the watermark has been infused within the first diagonal matrix generated by Tensor-SVD. The proposed method is free from false positive issues due to its total insertion-based approach. Furthermore, the proposed method is also implemented in a low-power Single Board Computer for seamless integration in personal and industrial consumer imaging devices. Moreover, the watermark's security has been further increased by encrypting it with Arnold's Cat Map based cryptic algorithm. Qualitative and quantitative comparison with various state-of-the-art methods justifies the superiority of the suggested algorithm under most form of signal processing, and geometric impairments.
A Deep Insight into Signature Verification Using Deep Neural Network

Signature is an essential biometric modality used in daily life to authenticate one person on his claim. Due to the widespread usage of stamps in office work, banks, forensic documents, etc., it captures the great interest of the researchers for the past many years. With the advancement in the technology and internet, automatic signature verification considered with renewed interest by the researchers. In this paper, we present the state-of-the-art signature verification mechanism and a novel method to perform signature verification in the RGB images. We train our model using advanced deep learning technique (Deep Neural network (DNN)) to differentiate between genuine and forged classes of signatures. The proposed algorithm evaluated on 4NSigComp2010 and 4NSigComp2012 datasets with the same experimental setup. The results showed that the proposed algorithm outperforms the systems presented in the competitions with an EER of 12 and 11.75, respectively.
Direct interferometric test of the nonlinear phase-shift gate

Probabilistic quantum gates permeate nearly all aspects of modern linear optical quantum computing. This is largely due to the measurement induced nonlinearities of which they are capable. The tradeoff between the cost of resources required to boost success probabilities to scalable levels and the realization of effective photon-photon interactions required grows ever more favorable to this paradigm with rapidly evolving advancements in integrated nanophotonics. As quantum circuits generating cluster states become increasingly complex, component analysis is critical. In this paper, we propose and demonstrate the experimental viability of testing a probabilistic gate. Specifically, as an illustrative example of the technique, we propose a direct test of the nonlinear phase-shift gate (NLPSG), an essential component of a Knill-Laflamme-Milburn (KLM) controlled-not (cnot) gate. We develop our analysis for both the case of the original bulk optical KLM NLPSG and the case of the scalable integrated nanophotonic NLPSG based on microring resonators (MRRs) that we have proposed very recently. We consider the interference between the target photon mode of the NLPSG along one arm of a Mach-Zehnder interferometer (MZI) and a mode subject to an adjustable linear phase along the other arm. Analysis of triple-photon coincidences between the two modes at the output of the MZI and the success ancillary mode of the NLPSG provides a signature of the conditionally successful operation of the NLPSG. We examine the triple coincidence results for experimentally realistic cases of click or no-click detection with subunity detection efficiencies. Further, we compare the case for which the MZI input modes are seeded with weak coherent states with that for which the input states are those resulting from colinear spontaneous parametric down conversion (cl-SPDC). In particular, we show that, though more difficult to prepare, cl-SPDC states offer clear advantages for performing the test, especially in the case of relatively low photon detector efficiency. We develop the interferometric analysis in terms of a general four-mode unitary so that comparison and contrast of the signatures of the specific NLPSG implementation (i.e., KLM and MRR) can be carried out easily by substituting in the appropriate unitary matrix elements at the end of the calculation. The analysis is readily extendable to other several-mode low photon number quantum circuits the successful operation of which is heralded by the measurement of ancilla photons, under nonideal detection conditions germane to laboratory experiments.
A joint likelihood approach to the analysis of length of stay data utilising the continuous-time hidden Markov model and Coxian phase-type distribution

The Coxian phase-type distribution is a special case of phase-type distribution which represents the time to absorption of a finite Markov chain in continuous time. The distribution is able to capture subjects’ flow through a system but is unable to highlight if there are different pathways caused by an underlying latent factor. Identifying these different pathways will give healthcare providers a deeper insight and understanding of patient flow and allow them to identify and change any potential issues. This paper combines the Coxian phase-type distribution with the continuous-time hidden Markov model to highlight these paths. The theory of combining the Coxian phase-type distribution with the continuous-time hidden Markov model shall be presented along with a simulation study and an application using Italian healthcare data.
Managing Security in Client-Server Network Infrastructure

In today’s scenario, Networking has become an integral part of daily life and with it, Security is a major concern. There is constant threat because it is almost impossible to be isolated from Internet. When we talk about Networking, Server-Client Infrastructure is to be considered. With such an active and important role in our life, Networking is a major area and we need to make sure that the network is not hacked by anyone i.e security is to be ensured. In this paper, first we discuss about Networking, then Server-Client Network Infrastructure and finally at the last we describe a way to manage security in Server-Client Network Infrastructure.
SPrune: A Code Pruning Tool for Ethereum Solidity Contract Static Analysis

The following topics are dealt with: learning (artificial intelligence); wireless LAN; theorem proving; optimisation; security of data; cloud computing; formal verification; formal specification; traffic engineering computing; data privacy.
Robust self-test of high-dimension mutually unbiased bases with commercial multi-core fiber

We perform a robust self-testing certification of two four-dimensional mutually unbiased bases in a high-quality optical configuration built with commercial multicore fiber. We also evaluate the randomness produced. Our results represent an important step towards practical self-testing in high-dimensional quantum information technologies.
Exploring Temporality in Horacio Vaggione’s Compositional Thought

This article aims to elaborate Horacio Vaggione’s theoretical approach towards electronic music composition and his understanding of the musical structure, and to discuss how some of his key concepts come into presence during the compositional experience of temporality. Following the introduction of object-oriented composition and musical networks, I will discuss the concept of morphology alongside an investigation of how these ideas relate to temporality. In addition to this inquiry, I will briefly explore the possibilities of an ontological discussion on Vaggione’s compositional mindset and how his temporal perspective differs from some of his colleagues.
Evaluation of Data Protection On Students Academic Information System Universitas Terbuka

Academic Information System (SIA) Universitas Terbuka (UT) is a system that supports student registration processes in which there are many data information contain student's personal data and courses grade. Today, hacking methods are becoming more sophisticated. Securing data is a priority that requires organizations to take more proactive protection measures. In the education sector, the threat of violations regarding data security more than doubled by 103%. Therefore, it is necessary to evaluate data security to protect SIA UT using Carnegie Mellon University's Data Protection Assessment Guidelines. The results are 52.38% of 84 required controls, 21.43% of 14 recommended controls, and 0% of 1 control optional rating has been done. The rest of controls that has not been done yet will become challenge for developing and implementing a Data Protection controls in UT.
Design and simulation of vehicle controllers through genetic algorithms

Genetic Programming (GP) is a population-based evolutionary technique, which, unlike a Genetic Algorithm (GA) does not work on a fixed-length data structure, but on a variable-length structure and aims to evolve functions, models or programs, rather than finding a set of parameters. There are different histories of driver development, so different proposals of the use of PG to evolve driver structures are presented. In the case of an autonomous vehicle, the development of a steering controller is complex in the sense that it is a non-linear system, and the control actions are very limited by the maximum angle allowed by the steering wheels. This paper presents the development of an autonomous vehicle controller with Ackermann steering evolved by means of Genetic Programming.
Design of Reservation Information System

Abstract The objective of this research is to design reservation information system. The system approach method was used to analyze the system is an object-oriented approach and the system development method used is the waterfall. The system was designed not only as a promotional medium but also as an information system that allows customers to make a reservation for event locations and payments through the website. Therefore, the result of this study can help customer to have adequate information about the tourist site and make a reservation anytime and anywhere.
Using Graph Convolution Network for Predicting Performance of Automatically Generated Convolution Neural Networks

In this paper, we propose a model using a graph convolution network for predicting the accuracy of the automatically generated convolution neural network (CNN). In recent years, there have been many excellent results in the automatic designing of deep learning network structures and their hyperparameters. These works mainly use evolutionary algorithms and reinforcement learning methods. But to verify the accuracy of newly generated neural network samples, it requires a lot of computational time and large scale of hardware. Therefore, how to reduce training time and improve the utilization of samples has become an important problem of automatic design for deep learning networks. On the other hand, the effectiveness of the graph convolution network (GCN) has been shown in processing graph data on the tasks of graph node classification, link prediction, and clustering. We are considering using GCN to predict the accuracy of the automatically designed neural networks. We compare GCN, a multi-layer neural network(NN), and CNN on the accuracy prediction task of automatically generated CNN for image classification tasks on the CIFAR-10 dataset. Our experiment demonstrates the advantages of GCN on small datasets compared to the multi-layer neural network and CNN, while GCN achieves the equivalent accuracy to the other methods on large datasets.
Method for classifying images in databases through deep convolutional networks

Since 2006, deep structured learning, or more commonly called deep learning or hierarchical learning, has become a new area of research in machine learning. In recent years, techniques developed from deep learning research have impacted on a wide range of information and particularly image processing studies, within traditional and new fields, including key aspects of machine learning and artificial intelligence. This paper proposes an alternative scheme for training data management in CNNs, consisting of selective-adaptive data sampling. By means of experiments with the CIFAR10 database for image classification.
Velocity and attitude estimation of a small unmanned aircraft with micro Pitot tube and Inertial Measurement Unit (IMU)

Unmanned Aircraft Systems (UAS) have gained impetus in the last decade from increasing civil, scientific, military, commercial and recreational applications (e.g. urban traffic monitoring, war zone defense and monitoring, archaeological site prospection, inspection of electrical power lines, etc.). Autonomous flight, management and control of Unmanned Aerial Vehicles (UAVs) are important issues in many professional and research applications. This paper focuses on trajectory measurement and control using two sensors (Angle of Attack, AOA and Angle of Sideslip, AOS) for speed and attitude estimation. The sensors involved in the analysis are a pressure (static and dynamic) sensor and an Inertial Measurement Unit (IMU). Kalman Filtering (KF) has been applied for measurement noise removal and data fusion. The theoretical analysis of the KF shows that global exponential stability of the estimation error is achieved under these conditions. The method has been tested using experimental data from a small quadrotor, with a legacy autopilot to provide basic estimates of UAV velocity and attitude and comparing them with the sensors data. AOA and AOS have been validated via correlation between the AOA estimate and vertical accelerometer measurements, since lift force can be modeled as a linear function of AOA in normal flight. Results from several flight tests confirm the validity of the approach to trajectory determination.
SKCN: Practical and Flexible Digital Signature from Module Lattice

The lattice-based signature scheme Dilithium is one of the most promising signature candidates for the post-quantum era, for its simplicity, efficiency, small public key size, and resistance against side channel attacks. Whether better trade-offs on the already remarkable performance of Dilithium can be made is left in Dilithium as an interesting open question. In this work, we provide new insights in interpreting the design of Dilithium, in terms of key consensus previously proposed in the literature for key encapsulation mechanisms (KEM) and key exchange (KEX). Based on the deterministic version of the optimal key consensus with noise (OKCN) mechanism originally developed for KEM/KEX, we present signature from key consensus with noise (SKCN), which could be viewed as generalization and optimization of Dilithium. The construction of SKCN is generic, modular and flexible, which in particular allows a much broader range of parameters for searching better tradeoffs among security, computational efficiency, and bandwidth. For example, on the recommended parameters, compared with Dilithium our SKCN scheme is more efficient both in computation and in bandwidth, while preserving the same level of post-quantum security. Also, our three sets of parameters are chosen to be as unified as possible, so as to simplify the implementation in practice. Finally, using the same routine of OKCN for both KEM/KEX and digital signature eases (hardware) implementation and deployment in practice, and is useful to simplify the system complexity of lattice-based cryptography in general.
Natural Language Inference in Ordinary and Support Verb Constructions

The family of clause types known as ‘support (or ‘light’) verb construction’ (SVC) manifests a peculiar syntax-semantics interface if compared with ordinary verb constructions (OVC). If, in e.g. She laughed, the verb licenses an argument and assigns it a semantic role, syntacticians of every stripe nowadays agree that it is the noun laugh, in She gave a laugh, which fulfils the same function. The differences between the two types have been extensively discussed in the linguistics literature (systematic research started in the 1970s), less so in Computational Linguistics. This paper has two objectives. First, it will propose an innovative type of semantic role, which is termed Cognate Semantic Role (CSR) because the verb employed in the notation is etymologically related to the predicate licensing arguments. She laughed and She gave a laugh therefore express the same role >the-one-who-laughs<, assigned by laughed and a laugh respectively. Second, it will introduce a tool capable of extracting CSRs automatically from both OVCs and SVCs; thus a device will be used for detecting the construction type. CSRs offer a number of advantages for the formalization of entailments and paraphrases and for Machine Translation.
MPC for Securing Internet Infrastructure

Secure multiparty computation (MPC) allows multiple entities to perform joint computation over their private inputs, revealing only the output. Although it was considered to be efficient enough for many years, recent advances have shown that secure computation can be practical for specific applications. These applications have ranged from privacy-preserving auctions to private machine learning. In this work we explore the use of MPC for securing Internet infrastructure. We show that basic Internet systems, such as routing and DNS, rely on centralised authorities. Nevertheless, vulnerabilities as well as conflicting interests often make this requirement for trust not suitable for practical purposes. In this work, we set forth to explore replacement of trust in centralised authorities in Internet infrastructure with secure MPC.
PeBAO: A Performance Bottleneck Analysis and Optimization Framework in Concurrent Environments

When multiple queries are concurrently executed, the database management systems often encounter performance bottlenecks which lead to the excessive execution time of query statements. Analyzing the execution process of these queries is conductive to study on the influence of query statements on database performance. In this paper, we proposed a performance bottleneck analysis and optimization framework named PeBAO to solve the database performance bottleneck problems in a multitasking concurrent execution environment. This framework quantifies the bottleneck impact factors of querying tasks and operations based on cost strategy. Then we designed a bottleneck optimization module based on heuristic rules. Finally, we solve the bottleneck purposefully and improve the performance of database system by combining the bottleneck impact factor with query optimization suggestions. We use the datasets of TPC-H to evaluate the accuracy of bottleneck judgement and bottleneck impact factor quantifying method. Our experimental results show that PeBAO provides effective optimization suggestions for database performance bottlenecks in a multitasking concurrent executive environment.