Reviewerly/exHarmony huggingface dataset:
An Identity-Based Blind Signature Scheme Using Lattice with Provable Security

With the rapid development of quantum computing and quantum information technology, the universal quantum computer will emerge in the near decades with a very high probability and it could break most of the current public key cryptosystems totally. Due to the ability of withstanding the universal quantum computer’s attack, the lattice-based cryptosystems have received lots of attention from both industry and academia. In this paper, we propose an identity-based blind signature scheme using lattice. We also prove that the proposed scheme is provably secure in the random oracle model. The performance analysis shows that the proposed scheme has less mean value of sampling times and smaller signature size than previous schemes. Thus, the proposed scheme is more suitable for practical applications.
Artificial Intelligence for Games

This chapter presents the classical alpha-beta algorithm and several variants, Monte Carlo Tree Search which is at the origin of recent progresses in many games, techniques used in video games and puzzles, and retrograde analysis which performs perfect play in endgames.
REMOVED: PROTOTIPE PAKAIAN ANTI RADIASI UNISEX SPORTSWEAR SMARTPHONE DENGAN PAPARAN RADIASI PLASMA PIJAR KORONA ELEKTRODA TIP- SILINDER

This article has been removed due to some issues in plagiarism
Redesigned Skip-Network for Crowd Counting with Dilated Convolution and Backward Connection

Crowd counting is a challenging task dealing with the variation of an object scale and a crowd density. Existing works have emphasized on skip connections by integrating shallower layers with deeper layers, where each layer extracts features in a different object scale and crowd density. However, only high-level features are emphasized while ignoring low-level features. This paper proposes an estimation network by passing high-level features to shallow layers and emphasizing its low-level feature. Since an estimation network is a hierarchical network, a high-level feature is also emphasized by an improved low-level feature. Our estimation network consists of two identical networks for extracting a high-level feature and estimating the final result. To preserve semantic information, dilated convolution is employed without resizing the feature map. Our method was tested in three datasets for counting humans and vehicles in a crowd image. The counting performance is evaluated by mean absolute error and root mean squared error indicating the accuracy and robustness of an estimation network, respectively. The experimental result shows that our network outperforms other related works in a high crowd density and is effective for reducing over-counting error in the overall case.
PENGEMBANGAN MULTIMEDIA INTERAKTIF MATA DIKLAT MEMPERBAIKI MOTOR LISTRIK GUNA PENINGKATAN HASIL BELAJAR

The purpose of this article is to improve the learning outcomes of students using interactive multimedia in the eyes of training to improve electric motors in SMK 1 Tanjung Raya. This type of research is a quasi-experimental design with a pretest-posttest one group. The subjects of this study were students of class XII TITL of SMK 1 Tanjung Raya who were enrolled in the 2013/2014 school year consisting of 29 students. Data collection in this study used a test of learning outcomes (posttest) in the form of objective questions as many as 20 items. Before the test questions were used then a test was conducted to determine the validity, reliability, level of difficulty of the questions and the different power of the questions. The data obtained were analyzed using the Gain Score. The results obtained indicate that there is an increase in student learning outcomes that can be seen from the results of the pretest and posttest values, the average value of pretest before using interactive multimedia is 64.48 and the average posttest value after using interactive multimedia is 75.07. So it was concluded that the use of interactive multimedia learning media can improve student learning outcomes in the eyes of training to improve the electric motor.
KESIAPSIAGAAN DESA TERHADAP BENCANA TANAH LONGSOR

&#x0D; &#x0D; &#x0D; Title: Village Preparedness Against Landslide Disaster&#x0D; Disasters that have previously occurred can almost be guaranteed that they would most likely reoccur without our knowing when and where they would take place once again. Maximum preparation and understanding about what we should do during events of disaster are substantial measures that we must take. In addition, knowledge or experiences relating to disasters should also be passed down to the next generation. This study employed methods of in-depth interviews and direct observations to acquire data regarding indicators of disasters found in the village of Kebonharjo. The interviews were performed with the villagers, while observations were carried out in a number of locations where landslides had occurred. The acquired results were subsequently analyzed in order to gain an understanding of the most significant indicators that can be used as a standard for the villagers to be prepared in the event of a disaster. The availability of written knowledge and documentation pertaining to these indicators will consequently lead to such knowledge being more widely known by the community and they can also serve as a useful resource for the villagers when they find locations that are considered to show indications of disaster, in which they can then make a quick identification of the situation. Transforming oral knowledge into a written one is admittedly an appropriate choice in order to strengthen the village’s preparedness against disaster.&#x0D; &#x0D; &#x0D;
Application of Differential Evolution in the Solution of Stiff System of Ordinary Differential Equations

In recent times, the adaptation of evolutionary optimization algorithms for obtaining optimal solutions of many classical problems is gaining popularity. In this paper, optimal approximate solutions of initial--valued stiff system of first--order Ordinary Differential Equation (ODE) are obtained by converting the ODE into constrained optimization problem. The later is then solve via differential evolution algorithm. To illustrate the efficiency of the proposed approach, two numerical examples were considered. This approach showed significant improvement on the accuracy of the results produced compared with existing methods discussed in literature.
Penerapan Neural Network untuk Klasifkasi Kerusakan Mutu Tomat

The decrease in quality and productivity of tomatoes is caused by high rainfall, bad weather and cultivation so that the tomatoes become rotten, cracked, and spotting occurs. The government is trying to provide training to improve the quality of tomatoes for farmers. However, the training was not effective so the researchers helped create a system that was able to educate farmers in the classification of damage to tomato quality. This system serves to facilitate farmers in recognizing tomato damage thereby reducing the risk of crop failure. In this study, the classification method used is backpropagation with 7 input parameters. The input consists of morphological and texture features. The output of this classification system consists of 3 classes are blossom end rot, fruit cracking and fruit spots caused by bacterial specks. The best accuracy level of the system in classifying tomato quality damage in the training process is 89.04% and testing is 81.11%.
AN ANALYSIS OF IMPLICATURES FOUND IN WHATSAPP STORIES

This study focuses on implicatures found in the WhatsApp stories. This study aims to find and explain types of implicatures found in WhatsApp stories using the theory proposed by Yule (1996). This study employs qualitative design as the research design. In addition, content analysis is applied to obtain the data in which the writer becomes the key instrument of the study. Interactive data analysis, then, is conducted to analyze and explain the data. The data of the study are taken from WhatsApp stories that appears in the writer’s WhatsApp application. This study shows the findings of the types of implicatures. The data are classified into four types of implicatures: generalized conversational implicatures (5 data), particularized conversational implicatures (7 data), scalar implicature (2 data), and conventional implicatures (1 datum).
Loss-Driven Channel Pruning of Convolutional Neural Networks

The increase in computation cost and storage of convolutional neural networks (CNNs) severely hinders their applications on limited-resources devices in recent years. As a result, there is impending necessity to accelerate the networks by certain methods. In this paper, we propose a loss-driven method to prune redundant channels of CNNs. It identifies unimportant channels by using Taylor expansion technique regarding to scaling and shifting factors, and prunes those channels by fixed percentile threshold. By doing so, we obtain a compact network with less parameters and FLOPs consumption. In experimental section, we evaluate the proposed method in CIFAR datasets with several popular networks, including VGG-19, DenseNet-40 and ResNet-164, and experimental results demonstrate the proposed method is able to prune over 70% channels and parameters with no performance loss. Moreover, iterative pruning could be used to obtain more compact network.
On testing pseudorandom generators via statistical tests based on the arcsine law

Testing the quality of pseudorandom number generators is an important issue. Security requirements become more and more demanding, weaknesses in this matter are simply not acceptable. There is a need for an in-depth analysis of statistical tests – one has to be sure that rejecting/accepting a generator as good is not a result of errors in computations or approximations. In this paper we propose a second level statistical test based on the arcsine law for random walks. We provide upper bounds for the approximation of the arcsine distribution, what allows us to perform a detailed error analysis of the proposed test.
Airplane Trajectory Planning Using Artificial Immune Algorithm

This paper presents a path planning algorithm for aircraft in charge of autonomous Terrain-Following, Terrain-Avoidance, and Threat-Avoidance (TF/TA2). In TF/TA2 problem, the aircraft is required to search for a collide-free path on its own flyability and operation before any of them reaches the target. The problem formulation is based on Isaacs’ Target Guarding problem, but extended to the case of multiple evaders. The proposed path planning method is based on the idea of dynamic programming methods and is capable of producing trajectories within minutes to an accomplished task. Simulations are carried out to demonstrate that the resulting trajectories approach the optimal solution produced by nonlinear programming based Artificial Immune Algorithm (AIA). Experiments are also conducted on aircraft to show the feasibility of implementing the proposed path planning algorithm on physical applications.
K-Means Clustering Analysis pada PersebaranTingkat Pengangguran Kabupaten/Kota di Sulawesi Selatan

This study aims to determine the distribution of districts in South Sulawesi based on unemployment rate using clustering analysis. The unemployment rate indicators are districts minimum wage (UMK) and human development index growth rate (IPM). The algorithm used in this study is k-means clustering. The results of k-means clustering analysis showed that of 24 districts in South Sulawesi are divided into two clusters, namely the high and low unemployment rate. The high employment rate cluster consists of 6 districts, namely Pangkep, Sidrap, Luwu Timur, Palopo, Parepare, and Makassar. The rest, 18 districts are in the low employment rate cluster.
Extreme learning machine with feature mapping of kernel function

Kernel-based extreme learning machine (KELM) solves the problem of random initialisation of extreme learning machine (ELM), and it has a faster learning speed and higher learning accuracy. However, when it comes to a scenario in which the dimensionality of kernel function mapping space is less than the number of samples, the kernel function theoretically cannot be introduced into ELM. To solve this problem, ELM with feature mapping (FM) of kernel function (FM-KELM) is proposed in this study, in which the random FM between the input layer and hidden layer of ELM is replaced with the FM of the kernel function. Moreover, the authors prove that when the regularised parameter C is close to zero, the solution of introduced kernel function is approximately equal to the correct solution. The proposed algorithm is more robust than KELM for the parameter C. Several experimental results show that the proposed algorithm in this study achieves higher classification accuracy without excessive parameter tuning, and the duration of the training and testing process is significantly reduced.
Enactment Ranking of Supervised Algorithms Dependence of Data Splitting Algorithms: A Case Study of Real Datasets

We conducted comparative analysis of different supervised dimension reduction techniques by integrating a set of different data splitting algorithms and demonstrate the relative efficacy of learning algorithms dependence of sample complexity.The issue of sample complexity discussed in the dependence of data splitting algorithms.In line with the expectations, every supervised learning classifier demonstrated different capability for different data splitting algorithms and no way to calculate overall ranking of techniques was directly available.We specifically focused the classifier ranking dependence of data splitting algorithms and devised a model built on weighted average rank Weighted Mean Rank Risk Adjusted Model (WMRRAM) for consent ranking of learning classifier algorithms.
Deep Binary Classification via Multi-Resolution Network and Stochastic Orthogonality for Subcompact Vehicle Recognition

To encourage people to save energy, subcompact cars have several benefits of discount on parking or toll road charge. However, manual classification of the subcompact car is highly labor intensive. To solve this problem, automatic vehicle classification systems are good candidates. Since a general pattern-based classification technique can not successfully recognize the ambiguous features of a vehicle, we present a new multi-resolution convolutional neural network (CNN) and stochastic orthogonal learning method to train the network. We first extract the region of a bonnet in the vehicle image. Next, both extracted and input image are engaged to low and high resolution layers in the CNN model. The proposed network is then optimized based on stochastic orthogonality. We also built a novel subcompact vehicle dataset that will be open for a public use. Experimental results show that the proposed model outperforms state-of-the-art approaches in term of accuracy, which means that the proposed method can efficiently classify the ambiguous features between subcompact and non-subcompact vehicles.
The role of noisy channels in quantum teleportation

In quantum information theory, effects of quantum noise on teleportation are undeniable. Hence,we investigate the effect of noisy channels including amplitude damping, phase damping, depolarizing and phase ip on the teleported state between Alice and Bob where they share an entangled state by using atom-eld interaction state. We analyze the delity and quantum correlations as a function of decoherence rates and time scale of a state to be teleported. We observe that the average delityand quantum correlations accurately depend on types of noise acting on quantum channels. It is found that atom-eld interaction states are affected by amplitude damping channel are more useful for teleportation than when the shared qubites are affected by noisy channels such as AD channel and phase ip. We also observe that if the quantum channels is subject to phase ip noise, the average delity reproduces initial quantum correlations to possible values. On the other hand,not only all the noisy quantum channels do not always destroy average delity but also they can yield the highest delity in noisy conditions. In the current demonstration, our results provide that the average delity can have larger than 2/3 in front of the noise of named other channels with increasing decoherenc strength. Success in quantum states transfer in the present noise establishes the important of studing noisy channels.
Manipulation And Persuasion Through Language Features In Fake News

This study attempts to describe the use of language features in hoaxes to manipulate facts and describe their persuasive power in influencing readers. This research is a qualitative descriptive study. This research data is documented from turnbackhoax.id. This site was chosen because it is one of the most popular hoax repellent sites in Indonesia. Documentation was the method of data collection. Data were collected then classified, organized, and then analyzed using theoretical concepts. Theory used to analyze language manipulation in this research is Barton and Lee's theory of language features and Searle theory of speech acts. Data and analysis are presented in tables with brief descriptions. The results of this study indicates that hoax maker use language features in manipulating facts, those are (1) acronyms and initialisms, (2) word reduction, (3) letters or numbers, (4) stylized/unconventional spelling, (5) emoticons, (6) stylized/unconventional punctuation, and (7) images or photographs. Furthermore, it is found the use of assertive, expressive and directive speech acts to build persuasive power to influence the reader. The results of this study can be used as a reference in seeing the characteristics of language manipulation on hoaxes so that people can maximize the use of their logic and intellectual as a system of verifying information to determine the truth of information, knowing the information is fact or fake, distinguishing an information is a hoax or fact.&#x0D;
Structured Light 3D Reconstruction System Based on a Stereo Calibration Plate

Calibration is a critical step in structured light 3D imaging systems. However, in the traditional calibration process, since the calibration plate is based on a two-dimensional model, the flatness of the calibration plate and the angle of the photo will affect the subsequent stitching steps based on the feature points. The number of photos also affects the calibration results. To improve the calibration accuracy, multiple photos need to be taken. The primary objective of this study was to achieve the simple and fast calibration of system parameters, so a method obtaining a large number of calibration data by homography matrix is presented, and a corresponding stereo target is designed in symmetry. First, using the relationship between the corner coordinates of the left and right parts of the stereo calibration plate and the coordinates of the world coordinate system, the homography matrix of the left and right calibration plates from the image coordinates to the world coordinates is calculated. Second, all the pixels in the stereo calibration plate are matched to the world coordinate system by using the homography matrix. In addition, we also compared the results of this method with those of traditional calibration methods. The experimental results show that the 3D geometric surface of the reconstruction result is smooth, it avoids the missing parts and the visual effect is excellent. Furthermore, the error range of small and complex objects can be reduced to 0.03 mm~0.05 mm. This method simplifies the calibration steps, reduces the calibration costs and has practical application value.
A Cue Adaptive Decoder for Controllable Neural Response Generation

In open-domain dialogue systems, dialogue cues such as emotion, persona, and emoji can be incorporated into conversation models for strengthening the semantic relevance of generated responses. Existing neural response generation models either incorporate dialogue cue into decoder’s initial state or embed the cue indiscriminately into the state of every generated word, which may cause the gradients of the embedded cue to vanish or disturb the semantic relevance of generated words during back propagation. In this paper, we propose a Cue Adaptive Decoder (CueAD) that aims to dynamically determine the involvement of a cue at each generation step in the decoding. For this purpose, we extend the Gated Recurrent Unit (GRU) network with an adaptive cue representation for facilitating cue incorporation, in which an adaptive gating unit is utilized to decide when to incorporate cue information so that the cue can provide useful clues for enhancing the semantic relevance of the generated words. Experimental results show that CueAD outperforms state-of-the-art baselines with large margins.
AN EFFICIENT METHOD TO REDUCE LZW ALGORITHM OUPUT CODE LENGTH

Text compression techniques are essential techniques to use for data transmission from one location to another location and also used to keep the more data.LZW is an lossless text compression technique to using by most of people.This paper proposes to decrease the length of the LZW algorithm output, thus we can compress more data at a time.
Autonomous Mobile Robot based on BehaviourBased Robotic using V-REP Simulator–Pioneer P3-DX Robot

This article describes the design and implementation of behavior-based robotic (BBR) algorithm on a wheeled mobile robot (WMR) Pioneer P3-DX in a maze exploration mission using V-REP simulator. This robot must trace and search for targets placed randomly on a labyrinth. After successfully meeting the objective, robot runs back to home position using the nearest path. Robot navigation system applies BBR algorithm to reach the target using behavior modules which work simultaneously to obtain the desired robot’s trajectory. The most fundamental behavior which is highly affordable to build on the robot system is a wall-following behavior. To make the robot could follow the wall in a safe, smooth and responsive condition, proportional-integral-derivative (PID) controller is applied. PID controller runs by utilizing the reading of sixteen proximity sensors carried on Pioneer P3-DX robot toward the expected wall distance while the robot is exploring the labyrinth. To ensure the designed system works properly, several tests were conducted, including BBR test and PID controller test. BBR test shows that the system can choose the shortest track when returning to home position. The PID controller test produces robot movement with maximum deviation and settling time for about 0.013 m and 30 seconds, respectively.
A PROPOSED SYSTEM FOR SECURING CRYPTOCURRENCY VIA THE INTEGRATION OF INTERNET OF THINGS WITH BLOCKCHAIN

Nowadays the world discuss the problem of the cryptocurrency and shows several risks in dealing with this new investment or banning it by governments and the main issue of this paper is how can the third part ex: (exchange market for trading cryptocurrencies) avoid cybercriminal activities, In this paper, we will define only two of cybercriminals that related to the third part directly, First: Ransomware attacks and how the process of storing cryptocurrencies can be secured. Second: Black net in which many illegal trades could be done and how can the third part (governments) monitor the relationship between buyers and sellers to avoid this problem. In this paper, we propose a framework that proposed in anew exchange market by integrating blockchain technology and IOT (internet of things) to increase the security in the two processes that consider the main issues for the exchange market: first: storing the cryptocurrencies securely and avoid ransomware attacks, second: monitoring the transactions and avoid illegal business (black net) could happen through it. And we can implement this framework for special types of partially decentralized cryptocurrencies ex: NEO and Libra coin.Keywords: Cryptocurrency, Hyperledger, Ransomware, Black Net, Blockchain, Internet of ThingsJEL Classifications: E58, M15, K22DOI: https://doi.org/10.32479/ijefi.9130