Reviewerly/exHarmony huggingface dataset:
Image denoising via a non-local patch graph total variation

Total variation (TV) based models are very popular in image denoising but suffer from some drawbacks. For example, local TV methods often cannot preserve edges and textures well when they face excessive smoothing. Non-local TV methods constitute an alternative, but their computational cost is huge. To overcome these issues, we propose an image denoising method named non-local patch graph total variation (NPGTV). Its main originality stands for the graph total variation method, which combines the total variation with graph signal processing. Schematically, we first construct a K-nearest graph from the original image using a non-local patch-based method. Then the model is solved with the Douglas-Rachford Splitting algorithm. By doing so, the image details can be well preserved while being denoised. Experiments conducted on several standard natural images illustrate the effectiveness of our method when compared to some other state-of-the-art denoising methods like classical total variation, non-local means filter (NLM), non-local graph based transform (NLGBT), adaptive graph-based total variation (AGTV).
A Depthwise Separable Network for Action Recognition

Action recognition is one important but challenging tasks in computer vision. The 3D convolutional neural network is one mainstream method for action recognition because it can extract temporal and spatial information in the video simultaneously. However, 3D convolutional neural network has a serious drawback which is that its parameter quantity is too large. Depthwise convolution is a form of group convolution, which can effectively reduce the parameter of convolution kernel, and has been widely applied in 2D convolutional neural network. Therefore, we propose to introduce depthwise convolution into the 3D convolutional neural network. We choose 3D resnet as our basic model, and construct our model by replacing the 3D convolution kernel in the baseline with the depthwise convolution, we named our proposed model as depthwise separable network (DSN). We conducted experiments on UCF101 and HMDB51 dataset. The experimental results show that by introducing the depthwise convolution, our DSN network can not only reduce the parameters of the baseline, but also can moderately improve the accuracy.
Dynamic keys generation for internet of things

&lt;span&gt;In several aspects, interest in IoT has become considerable by researchers and academics in recent years. Data security becomes one of the important challenges facing development of IoT environment. Many algorithms were proposed to secure the IoT applications. The traditional public key cryptographic are inappropriate because it requires high computational. Therefore, lattice-based public-key cryptosystem (LB-PKC) is a favorable technique for IoT security. NTRU is one of a LB-PKC that based on truncated polynomial ring, it has good features, which make it to be an effective alternative to the RSA and ECC algorithms. But, there is LLL algorithm can success to attack it under certain conditions. This paper proposes modifications to NTRU public key cryptosystem to be secure against the lattice-based attack by using LLL algorithm, as well as a method for generating a new keys sequence dynamically. The results from simulations show that the performance of these modifications gives more secure from NTRU. &lt;/span&gt;
Gnocchi: Multiplexed Payment Channels for Cryptocurrencies

As the underlying technology of mainstream cryptocurrencies such as Bitcoin and Ethereum, blockchain builds a decentralized payment system. However, the security and consistency of such system derive from the wide replication of transaction data and expensive distributed consensus mechanism, which makes current cryptocurrencies suffer great scalability gap from meeting commercial demand. To date, several solutions have been proposed to solve the scalability bottleneck of blockchain systems such as Bitcoin-NG, sharding mechanism and off-chain payment network. The off-chain payment network is one of the most promising approaches since it could significantly extend the throughput of the system and provide cheap instant micro-payment. In this work, we introduce Gnocchi, a multiplexed off-channel payment channel scheme that offers a novel method to construct an off-channel payment system to allow multi-party payment in one channel. We formally model our payment network and analyze network connectivity of our scheme to show that this newly proposed scheme significantly reduces the routing cost and transfer fee for cross-channel payment in contrast to existing off-chain payment scheme such as Lightning Network. This improves the efficiency of off-chain payment system further. We introduce transaction supervisor in a channel while maintaining the decentralization feature of the system by elaborately designing a fraud proof contract to restrain any adversary in the channel.
Database Secure Manipulation based on Paillier’s Homomorphic Encryption (DSM-PHE)

The objective of this research was to suggest some simple solution to increase database manipulation security. Based on advantage of Homomorphic encryption, user’s data in database is always encrypted by Homomorphic encryption algorithm such as Paillier. User’s data is manipulated or processed on many times such as addition, subtraction, multiplication and division. By the advantage of Homomorphic encryption algorithm, user’s data were no need to decrypt while it was manipulated therefore secrecy of data was still reserved. Unfortunately, Paillier’s Homomorphic encryption is normally coverage only addition and multiplication mathematical data operation. This paper suggests simple technique to enhance Pailler’s encryption algorithm to perform data operation as well in subtraction and division operation. Evaluation in suggested data base manipulation, DSM-PHE, indicates that its operation tasks was five operation times more than ordinary data base manipulation operation tasks, without any encryption. Therefore DSM-PHE should be especially used in more sensitive data.
An Efficient Technique for Optimality Measurement of Approximation Algorithms

Many algorithms have been proposed for the solution of the minimum vertex cover (MVC) problem, but the researchers are unable to find the optimality of an approximation algorithm.In this paper, we have proposed a method to evaluate that either the result returned by an approximation algorithm for the minimum vertex cover problem is optimal or not.The proposed method is tested on three algorithms, i.e., maximum degree greedy (MDG) algorithm, modified vertex support algorithm (MVSA) and clever steady strategy algorithm (CSSA).The proposed method provides an opportunity to test the optimality of an approximation algorithm for MVC problem with low computation complexity.The proposed method has performed well during experimentation, and its results brighten the path of successful implementation of the method for the evaluation of approximation algorithms for the minimum vertex cover (MVC) problem.The testing of the proposed method was carried out on small graph instances.The proposed method has resolved the problem to test the optimality of the approximation algorithm for the minimum vertex cover problem.This technique has digitized the process of finding out the accuracy of the optimal solution returned by approximation algorithms for MVC.
Feature Deep Continuous Aggregation for 3D Vehicle Detection

3D object detection has recently become a research hotspot in the field of autonomous driving. Although great progress has been made, it still needs to be further improved. Therefore, this paper presents FDCA, a feature deep continuous aggregation network using multi-sensors for 3D vehicle detection. The proposed network adopts a two-stage structure with the bird’s-eye view (BEV) map and the RGB image as an input. In the first stage, two feature extractors were used to generate feature maps with the high-resolution and representational ability for each input view. These feature maps were then fused and fed to a 3D proposal generator to obtain the reliable 3D vehicle proposals. In the second stage, the refinement network aggregated the features of the proposal regions further and performed classifications, a 3D bounding boxes regression, and orientation estimations to predict the location and heading of vehicles in 3D space. The FDCA network proposed was trained and evaluated on the KITTI 3D object detection benchmark. The experimental results of the validation set illustrated that compared with other fusion-based methods, the 3D average precision (AP) could achieve 76.82% on a moderate setting while having real-time capability, which was higher than that of the second-best performing method by 2.38%. Meanwhile, the results of ablation experiments show that the convergence rate of FDCA was much faster and the stability was also much better, making it a candidate for application in autonomous driving.
Modeling and Research on Power System of Distributed Sensor Networks for Long Streamers in Marine Seismic Exploration

The electric power system plays an important role in sensor networks. In the marine seismic exploration streamer system (MSESS), an underwater power system transmits high-voltage direct current to all nodes in the streamer through a daisy chain structure. As offshore oil exploration develops toward deep water, it is necessary to study long streamers with large-scale sensor networks for deep water exploration. When the length of a streamer is increased to a certain value, the output current of the power supply increases sharply. This results in the activation of the overcurrent protection and the power supply shuts down. This paper puts forward an accurate model for an underwater power system applied to MSESS. Using the Newton iteration algorithm and a reverse algorithm, equations established by the model are solved and laboratory test results are used to verify the accuracy of the model. Based on simulation and analysis of the model, we explain why the power system crashes when the streamer is too long. Software that can quickly calculate the maximum number of nodes (the maximum length with which the system works normally) is developed and it is significant for the design of MSESS. The method of research could also be applied to relevant work such as large-scale sensor networks with daisy-chaining power supply in land seismic exploration.
Hybrid method for automatic construction of 3D-ASM image intensity models for left ventricle

Active Shape Models (ASMS) play an important role in model based medical image analysis. They utilize point distribution models (PDMs) in which a priori information is encoded into a template so that the objects to be detected can be represented with a fixed topology. One key element in 3D-ASM is the image intensity model (IIM), which is investigated in this work. We propose a hybrid approach to automatically construct 3D-ASM Intensity Models for the left ventricle. To train the IIM, CNN is adopted to obtain the initial shape for 3D-ASM, distance maps for endo and epicardial contours from ground truth are derived, and using PDM, training shapes can be obtained. The training shapes and cardiac images are then used to train an IIM. 1200 cardiac MRI cases from Hubei Cancer Hospital were used in this study. By comparing point-to-surface errors against a proper gold standard, it demonstrates that large-scale cardiac MRIs can be segmented by 3D models trained under this scheme with fair accuracy. Clinical parameters are calculated using the Bland-Altman analysis, and thus we yield biases of 4.8 ml, 2.19 ml, 2.59 ml, 0.96%, 0.69 g and –2.67 g for LVEDV (LV End-diastolic Volume), LVESV (LV End-systolic Volume), LVSV (LV Stroke volume), LVEF (LV Ejection Fraction), LVM-DP (LV mass in diastolic phase) and LVM-SP (LV mass in systolic phase), respectively.
SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN LOKASI PERUMAHAN MENGGUNAKAN METODE PROFILE MATCHING

Abstract: In the business of property developers choosing a location to be an important decision for developers as a factor of attractiveness in housing marketing, if lack of observance in choosing the location of the location will result in slow sales, so that the company's profits become not optimal. For this reason, a decision support system is needed that can help developers in recommending housing locations that are in accordance with the wishes of the developer. The profile matching method is chosen because it can provide the closest value recommendation, so if there is no land the exact location in accordance with the wishes of the developer but the profile maching is able to provide the selection of the location with the closest profile value to the developer desired. Keywords: Decision support system, Developer, Property, Location Land, Profile matching. Abstrak: Lokasi merupakan faktor terpenting dalam usaha developer property, karena mempengaruhi tingkat pemasaran perumahan. Pemilihan lokasi menjadi keputusan penting, sehingga dibutuhkan suatu sistem pendukung keputusan yang tepat. Metode profile matching merupakan pilihan yang dapat memberikan rekomendasi. Hasil dari penelitian ini adalah memberikan rekomendasi dengan nilai terdekat, jadi walaupun tidak ada lahan lokasi yang sesuai persis keinginan developer tapi profile matching akan memberikan rekomendasi pemilihan lahan lokasi dengan profil yang terdekat sesuai harapan developer. Maka, dapat disimpulkan profile matching merupakan pilihan tepat dalam penentuan keputusan lokasi perumahan.
A Single-Shot Approach Using an LSTM for Moving Object Path Prediction

This work presents an analysis of predicting the future path of moving objects from a moving camera on traffic scenes with an LSTM architecture in a single-shot manner. Path prediction allows us to estimate the future locations of an object in a given space and is useful in important applications such as surveillance, abnormal behaviour detection, crowd behaviour analysis, traffic control and currently in driver assistance (ADAS) or collision avoidance systems. Normal approaches use the last tobs positions of an object observed in video frames to predict its future path as a sequence of position values. This can then be treated as a time series. LSTM architectures are known for reaching good performance when dealing with time series. We evaluate path prediction across three types of objects (pedestrians, vehicles and cyclists), four prediction horizons (5, 10, 15 and 20 frames ahead) and two different perspectives (image coordinate and birds-eye view). The approach described in this work reached an Average Displacement Error (ADE) of 0.01m for pedestrians, 0.06m for vehicles and 0.02m for cyclists and an average Final Displacement Error (FDE) of between 0.016m and 0.15m for near-future prediction using an LSTM architecure with relative tracklet positioning.
Teachers' perceptions toward EFL textbook

Teachers have an important role in determining a good textbook for students. However, it seems difficult because the teachers should have academic and non-academic considerations. This qualitative research aims to investigate 10 teachers’ perceptions toward the use of EFL textbook and the strength and weakness of the ’English Ok’ textbook used in 5 schools in Bandung. Through the questionnaire, interview and document analysis, 80% of teachers said that the use of the textbook was helpful and important for them. They used the textbook as the source of learning and made an effective time in transferring the knowledge. Meanwhile, 20% of teachers perceived that they preferred to use adapting materials from other resources as the materials than the book. In terms of practical consideration (accessibility, attractiveness, accessibility, and publication), layout and design, content (materials, practice, and activities), learners’ need and language accuracy, the ‘English ok’ book was considered as a good textbook. The weaknesses of the textbook were the unavailability of the audiocassette, answer sheet and exercise preparation for the national examination. Involving more participants are recommended to find out the perceptions of anti-textbook teachers.
Use My Digital Forensics Tool...It’s Shiny!

The tendency to use technologies without fully understanding the potential ramifications extends to all reaches of our lives.Digital forensics is not immune from this phenomenon.This paper discusses some past scenarios in which conclusions were drawn before all of the testing was complete.Digital forensics tools are then discussed including tool capabilities, tool analysis, and associated challenges.It identifies some potential issues and ramifications that may not be given appropriate consideration by digital forensic examiners or those who rely on these tools when weighing evidence.It concludes with some suggestions for future research directions that could answer some important questions about using digital forensics tools effectively.
FPGA BASED K QUBIT DIGITAL QUANTUM COPROCESSOR

It is shown that true quantum computers are analog and probabilistic computers and, in fact,they play the role of coprocessors in relation to classical computers. A digital quantum coprocessor consists of digital qubits. Each digital qubit is a finite state machine, the state changes of which is described in the same way as the state changes of an analog qubit. A digital qubit is a chain of digital quantum gates. Each digital quantum gate is digital unit, the state changes of which is described in the same way as the state changes of an analog quantum gate. The main difference between digital qubit and digital quantum gate from analog ones is the presence of memory (more precisely, the possibility of its introduction into their circuit) and its use for organizing calculations. A digital qubit without feedbacks - a digital quantum gate that can be controlled using a classic computer, is a single step of a pipeline with a measurement unit (comparator), a pipeline register and a pipeline trigger at the output. The quantum Fourier transform (QFT) is part of many quantum algorithms, in particular the Shor'sfactorization algorithm. The structures of digital quantum gates, digital qubits, and a digital quantumcoprocessor capable of performing the QFT are presented. The results of the QFT of a multi-qubit digital quantum coprocessor implemented on one FPGA are presented. For this, a IP core generator of multi-qubit quantum coprocessors was created, cores work was simulated. Models were implemented in FPGAs and their time and hardware characteristics were determined. The execution time of one quantum Fourier transform does not depend on the number of qubits in the digital quantum coprocessor and is commensurate with the time of change of the electron spin. The hardware complexity linearly depends on the number of qubits. With an increase in the number of qubits, the rate of true results of QFT decreases, at 64 qubits it is 1.5%, the decrease is nonlinear
Multi-Pose Face Recognition Using Pairwise Supervised Dictionary Learning

A major challenge in face recognition is handling large pose variations.Here, we proposed to tackle this challenge by a three step sparse representation based method: estimating the pose of an unseen non-frontal face image, generating its virtual frontal view using learned view-dependent dictionaries, and classifying the generated frontal view.It is assumed that for a specific identity, the representation coefficients based on the view dictionary are invariant to pose and view-dependent frontal view generation transformations are learned based on pair-wise supervised dictionary learning.Experiments conducted on FERET and CMU-PIE face databases depict the efficacy of the proposed method.
The Javanese Letters Classifier with Mobile Client-Server Architecture and Convolution Neural Network Method

&lt;p class="0abstract"&gt;the rapid development of mobile technologies allows platform devices to perform sophisticated tasks, including character recognition. These identification systems are notable techniques that required high computation cost, in order to achieve acceptable accuracy resulting from diversity in alphabet shape and method of writing, especially for the non-Latin alphabet, e.g., Javanese letter. In addition, numerous studies have attempted to address these issues by employing a Convolution Neural Network (CNN) due to its ability to provide high accuracy in character detection. However, the performance on mobile devices is possibly faced with problems resulting from the limitation of computation resource on the platform that also affect computation cost. This study, therefore, proposes a 2-tier architecture by placing the mobile app as a client that invokes a Javanese letters classifier service, which is based on CNN, and implemented in the web-server through the Application Program Interface (API). The results show that the letter classification was successfully implemented in a mobile platform, with an accuracy rate of 86.68%, utilizing training for 50 epochs, and an average time of 1935 ms.&lt;/p&gt;
Integrating IoT Modern Communication Architectures into the New Generation of VR/MR Environments

This paper presents how modern IoT communication architectures can be successfully used in building both virtual and mixed reality scenarios.The IoT architecture enables the use of LPWAN technologies with a limited Maximum Transmission Unit for an efficient IoT data provision.Based on the IoT communication architecture, this paper will present two study cases, where IoT data can be used in developing complex Mixed Reality (MR) / Virtual Reality (VR) environments.First study case will present how Mixed Reality can be applied for the development of an application based on Microsoft HoloLens© headset.The MR application will allow mixing virtual and real elements for creating an improved context related to possible indoor emergencies getting information at real-time from distributed IoT devices.The MR application that will be presented will cover four topics: virtual evacuation drills, media content display, real-time sensors information access and domotic actions.Second study case will present a solution for building VR environments used for overhead crane training operations based on shipping ports sensors information This paper will emphasize on the fact that based on IoT sensor information it is possible to move from the development of static MR/VR environments, where real, external conditions don't influence at all the virtual context, to the creation of dynamically generated environments, based on IoT data.This work is developed in the ongoing "Smart City 3D simulation and monitoring platform" (CITISIM) Itea3 project where MR/VR services are being developed in the smart city domain.
PENGAMANAN DATA TEKS MELALUI PERPADUAN ALGORITMA BEAUFORT DAN CAESAR CIPHER

The importance of maintaining data integrity and security is one of the challenges in the current digital era. One method that can be used to face this challenge is through cryptography. In cryptography there are several algorithms that can be used, one of which is the Caesar cipher algorithm. This algorithm has several disadvantages, including a limited number of characters of 26 characters. This can make the encryption results easily recognizable by other parties. This study aims to design a proposal for maintaining data security through cryptographic techniques, while addressing the problems inherent in the Caesar cipher algorithm. The combination of Caesar and Beaufort algorithm is done to overcome the existing problems. In addition, a character list of 94 characters was determined to be used in the process of encryption and decryption of text data. The result, through the integration of these two algorithms, the text cipher becomes more difficult to solve. There are two stages of the encryption process by using two different types of Keys for each stage in securing data