Reviewerly/exHarmony huggingface dataset:
The effect of mixing speed to adsorption heavy metal Cu<sup>2+</sup> and color using kepok banana peel waste

Abstract Home industry of screen printing generally disposes wastewater directly into drainage without being processed first. Wastewater from screen printing industry has a high metal content exceeding the quality standards of Minister of Environment Regulation no. 5 of 2014 concerning Waste Water Quality Standards which are being problem for the environment. This study aims to analyze the effect of mixing speed on adsorption of heavy metal Cu 2+ (Copper) using adsorbents from the utilization of kepok banana peel waste in batch reactors and mixed using the jartest tool. In the process of making the adsorbent, the kepok banana peel was dried for 2 days and carbonized by using furnace at 400°C for 2 hours to become powder. After the carbonization process, the size was uniformed into 100 mesh and activated using H 2 SO 4 with an immersion process for 24 hours. The adsorbent powder was then used as much as 10 grams in 250 mL of screen printing wastewater with variations in mixing speed (rpm): 100,150,200 and variations in contact time (minutes): 30,60,90. The result showed that the optimum mixing speed is 100 rpm with a time of 60 minutes. The initial concentration of heavy metals Cu 2+ of 3.52 mg/L and after the adsorption process it becomes 0.0716 mg/L, that meet the quality standards of 0.8 mg/L.
Hierarchical System Decomposition Using Genetic Algorithm for Future Sustainable Computing

A Hierarchical Subsystem Decomposition (HSD) is of great help in understanding large-scale software systems from the software architecture level. However, due to the lack of software architecture management, HSD documentations are often outdated, or they disappear in the course of repeated changes of a software system. Thus, in this paper, we propose a new approach for recovering HSD according to the intended design criteria based on a genetic algorithm to find an optimal solution. Experiments are performed to evaluate the proposed approach using two open source software systems with the 14 fitness functions of the genetic algorithm (GA). The HSDs recovered by our approach have different structural characteristics according to objectives. In the analysis on our GA operators, crossover contributes to a relatively large improvement in the early phase of a search. Mutation renders small-scale improvement in the whole search. Our GA is compared with a Hill-Climbing algorithm (HC) implemented by our GA operators. Although it is still in the primitive stage, our GA leads to higher-quality HSDs than HC. The experimental results indicate that the proposed approach delivers better performance than the existing approach.
Structured Neural Network with Low Complexity for MIMO Detection

Neural network has been applied into MIMO detection problem and has achieved the state-of-the-art performance. However, it is hard to deploy these large and deep neural network models to resource constrained platforms. In this paper, we impose the circulant structure inside neural network to generate a low complexity model for MIMO detection. This method can train the circulant structured network from scratch or convert from an existing dense neural network model. Experiments show that this algorithm can achieve half the model size with negligible performance drop.
A Multi-factor RSA-like Scheme with Fast Decryption Based on Rédei Rational Functions over the Pell Hyperbola

We propose a generalization of an RSA-like scheme based on Rédei rational functions over the Pell hyperbola. Instead of a modulus which is a product of two primes, we define the scheme on a multi-factor modulus, i.e. on a product of more than two primes. This results in a scheme with a decryption which is quadratically faster, in the number of primes factoring the modulus, than the original RSA, while preserving a better security. The scheme reaches its best efficiency advantage over RSA for high security levels, since in these cases the modulus can contain more primes. Compared to the analog schemes based on elliptic curves, as the KMOV cryptosystem, the proposed scheme is more efficient. Furthermore a variation of the scheme with larger ciphertext size does not suffer of impossible group operation attacks, as it happens for schemes based on elliptic curves.
Automated Biometric-EVM Implemented Using Lab-View

In this digitalization era Indian election still uses manual system for vote casting during election. In every election it is considered to introducing Electronic voting machines (EVM) though Evm has many limitations and it is not entirely automated. Hence, in this paper we have designed fully automated biometric-EVM using Lab-VIEW. The system interfaced with the integrated database system containing all voter information. At the end of voting process the number of casted votes will be counted with stipulated time. Hence the process system will improve the voting system by reducing fraudulent activities corruptions, ensuring security, transparency, fairness, accuracy, trustworthy and keeping backup trails of voting system can be seen in this paper. LabVIEW enables the data base storage and file management easy and applicable in all other fields compare to other methods. Data base storage and extraction of the data base at the same time can possible using Lab-VIEW with using internet and it adds many advantages using in rural areas without the broadband connection. This paper presents second stage of verification by proving Aadhar information of the voter extracted by the use of fingerprints and that going to display on screen. The Aadhar information can be monitored. In future IOT can be implemented to send election report etc. Therefore, voting management of the election commission will be improved by ceasing fraudulent activities.
Nonparametric Methods of Authorship Attribution in Classic and Modern Literature

The main purpose of this work is to determine the most effective method for identifying the author in terms of resource utilization, speed of calculation and complexity of implementation, and accuracy of identification based on nonparametric statistics.
The neural multi-model approach for nonlinear systems identification

This paper describes a multi-model identification of nonlinear system. Indeed, the multi-model approach is based on neural networks, and is obtained by parametric identification. The proposed architecture of the neural model is multilayered perceptron. The effectiveness of the proposed approach is shown through a numerical simulation of a nonlinear system. The obtained results are very satisfactory and show a very good precision relative in to the case of the nonlinearity of the system.
Interpreting Abnormality of a Complex Static Scene using Generative Adversarial Network

Anomaly detection remains a difficult task in the computer vision and image processing field. Although several studies have been done to address this challenge, most of these studies focused on analyzing temporal features to determine abnormality. Examples of temporal features include behavioral changes and new object appearance in the target scene. In this paper, we are interpreting abnormality from a new perspective, which is static and complex image scene that focused on one object (airplane) using generative adversarial networks (GANs). Our interpretation of abnormality in such image intended to test two research hypotheses: 1) whether GANs can capture the cognitive features of abnormality from within a complex scene. 2) whether GANs can be used to generate more reliable datasets of abnormal scenes. In this work, we chose an airplane as the object of our experiment. We defined abnormal and normal scenes as follow: The scene is abnormal if the airplane involved in accidents (such as crash or fire), and normal otherwise (such as flying or landed airplane). A custom dataset is built for this experiment and it consists of two classes; normal and abnormal. We augmented each class to the double of its size using GANs, and then we created three different sets of datasets: (DS1, DS2, and DS3) to test our hypotheses. We applied four different supervised machine learning classifiers on each of these three sets, we repeated this step 3 times as follow: 1) pixel-based, 2) with applying Principal Component Analysis (PCA), and 3) with applying Local Binary Pattern (LBP). The overall results showed that GANs possess the capability of generating images that capture the abnormality features from the static complex scene.
Collaborative Filtering Algorithm Based on Improved Similarity Calculation

With the increasing contradiction between the vast supply of information and the diversity needs of users, it is a wise choice to apply personalized recommendation to relevant areas. Among them, collaborative filtering algorithm is the basic algorithm in personalized recommendation, and similarity calculation is the core of the algorithm. The accuracy of its calculation has a great impact on the recommendation results. In order to solve the problem that the traditional similarity calculation results have great deviation and the recommended results are not ideal, a new similarity calculation method is proposed in this paper. According to the closer the rating time is, the higher the user's similarity is, and the time factor is incorporated into the proposed algorithm. At the same time, in order to avoid the improper contribution of active users and popular item to the similarity calculation, the penalty factor is added respectively. The experiment was carried out on MovieLens data set, and the experimental result showed that the proposed similarity method improved the accuracy of scoring prediction to some extent, and the recommendation effect was better than the traditional one.
Traffic Light Regulatory System Based on Fuzzy Algorithm Using Microcontroller

Abstract Basically, control system for traffic lights only perform control based on a fixed time. In fact, the arrival rate of vehicles at the intersection of the road is not always the same so of course congestion at the crossroads can not be controlled. This study presents a simulation control system which can perform automatic traffic light control based on vehicle arrival rate. In this control, the duration of the flame is not constant but follows the arrival rate of vehicles. The method used to control the flame duration is fuzzy logic algorithm with the Mamdani fuzzy reasoning using Matlab software. Test results based on the simulation of fuzzy logic in fuzzy MatLab toolbox show that the fuzzy logic algorithm can be used to meet the goal of optimal traffic control, the duration of a given time based on the arrival rate of vehicles. The higher rate of arrival of the vehicle, the longer the duration of a given time, and the lower rate of the arrival of the vehicle, the more briefly given time duration.
An Efficient Key-Aggregate Keyword Searchable Encryption for Data Sharing in Cloud Storage

One key focus of cloud storage is keyword search on encrypted data. In a cloud-based secure data sharing system, users need to search keywords on encrypted data in order to data confidentiality and query privacy. Moreover, a large number of trapdoors should be generated by users with his/her keys and submitted to the cloud. Some researches on searchable encryption with key-aggregate have been conducted in recent years, which shortened the number of keys and trapdoors to a constant. However, the computational overhead and communication costs are largely neglected in the literature. In this paper, we propose an efficient key-aggregate keyword searchable encryption (eKAKSE) scheme. Generally, this scheme does not need to generate substantial parameters when the system is established. It dramatically reduces the computational overhead in the case of resisting the eavesdropping problem. We formulate the security definitions for eKAKSE and prove its security. The simulation results show that the computational overhead of the proposed scheme is more efficient than that of other schemes.
Sequence-to-Sequence Models for Grapheme to Phoneme Conversion on Large Myanmar Pronunciation Dictionary

Grapheme to phoneme conversion is the production of pronunciation for a given word. Neural sequence to sequence models have been applied for grapheme to phoneme conversion recently. This paper analyzes the effectiveness of neural sequence to sequence models in grapheme to phoneme conversion for Myanmar language. The first large Myanmar pronunciation dictionary is introduced, and it is applied in building sequence to sequence models. The performance of four grapheme to phoneme conversion models, joint sequence model, Transformer, simple encoder-decoder, and attention enabled encoder-decoder models, are evaluated in terms of phoneme error rate(PER) and word error rate(WER). Analysis on three-word classes and six phoneme error types are done and discussed details in this paper. According to the evaluations, the Transformer has comparable results to traditional joint sequence model.
Uncertainty-based Decision Making Using Deep Reinforcement Learning

This work proposes an opinion inference algorithm in large graph network data using subjective, uncertain opinions. In the graph network data, an opinion is associated with an edge between two nodes where the edge indicates a known opinion while no edge refers to an unknown opinion for their relationship. The examples include the predictions of a road traffic condition (i.e., an edge indicates a road between two intersections and an opinion represents congested or non-congested) or trust relationships (i.e., an edge refers to a trust relationship between two users where an opinion indicates a user&#x0027;s trust in another user). To derive an unknown opinion between two nodes, we identify a set of best paths in the graph network data that can maximize decision performance (e.g., prediction accuracy). To solve this problem, we formulate each opinion using Subjective Logic (SL) and leverage a policy-based deep reinforcement learning (DRL) technique. We propose three DRL-based schemes combining SL and DRL where a reward is given based on a different type of uncertainty, including vacuity, dissonance, or monosonance. Via extensive simulation experiments, we investigate what type of uncertainty is a more critical factor than others in improving decision performance when a different uncertainty type is considered as a reward in DRL. We validated the outperformance of the proposed DRL-based schemes in terms of belief errors, prediction accuracy, and computation time based on both a semi-synthetic and real world datasets.
Track-Before-Detect Strategy for Radar Detection in Rayleigh-distributed Noise

This paper considers multi-frame radar detection in Rayleigh-distributed noise. Traditional multi-frame track-before-detect (TBD) methods may destroy the radar echo envelope seriously due to its suboptimal accumulation strategy. The integrated envelope may be extended to a large number of cells and cause performance degradation in target detection and parameter estimation. In this paper, we propose a velocity filtering based TBD method to detect and track weak targets for radar systems. Since the radar measurements are complex-valued and the inter-frame accumulation may suffer adversely from the random phase, the envelope detection is used to extract amplitude information firstly. Then, each resolution cell is predicted to the last frame of the processing batch according to an assumed velocity. Amplitude value of the cell is added onto the cell closest to the predicted position for energy integration. The procedure for multi-frame accumulation is presented. The target integrated envelope and false alarm probability of the proposed method in Rayleigh-distributed noise are derived theoretically. The detection threshold is investigated at a constant false alarm rate. Simulation experiments demonstrate the superiority of the proposed method in terms of output envelope focusing, detection performance and estimation accuracy.
SnapCode - A Snapshot Based Approach to Code Stylometry

Artificial neural networks have seen significant advancements in recent times with the growing popularity of deep learning. Deep learning allows us to learn representations that are otherwise difficult to extract and helps in better classification tasks. Images, videos and speech processing are the major areas where deep learning is applied. Our work is related to the application of deep learning to source codes. Previous works in this domain have failed to easily capture structural and behavioral aspects of the code. Thereby relying on manual feature engineering for applications like author identification, code quality analysis, cyber-attack investigation, malware recognition and plagiarism detection. We propose a novel approach to capture these feature representations by processing snapshots of code instead of processing source code token by token. We, therefore, propose SnapCode, a snapshot-based approach to extract deep convolutional features from text which would otherwise be impossible using currently known approaches. SnapCode uses a deep convolutional neural network coupled with transfer learning to learn the structural representation of the source code. We show that simple networks fail to learn these features and deep network coupled with transfer learning gives us the best results. SnapCode can capture behavioral aspects of source code as we employ it to the task of author detection, also known as "code stylometry". We choose author detection to validate our approach as it requires most number of manual and complicated features. Although source code is simply text, we aim to process text data in a way similar to humans and show that we could learn meaningful representations.
GRAFT: Unsupervised Adaptation to Resizing for Detection of Image Manipulation

A large number of methods for forensics of image manipulation relies on detecting fingerprints in residuals or noises. Therefore, these detection methods are bound to be sensitive to noise generated by the image acquisition process, as well as any <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pre-processing</i> . We show that a difference in pre-processing pipelines between training and testing sets induces performance losses for various classifiers. We focus on a particular pre-processing: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">resizing</i> . It corresponds to a typical scenario where images may be resized ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , downscaled to reduce storage) prior to being manipulated. This performance loss due to pre-resizing could be troublesome but has been rarely investigated in the image forensics field. We propose a new and effective adaptation method for one state-of-the-art image manipulation detection pipeline, and we call our proposed method Gaussian mixture model Resizing Adaptation by Fine-Tuning (GRAFT). Adaptation is performed in an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">unsupervised</i> fashion, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , without using any ground-truth label in the pre-resized testing domain, for the detection of image manipulation on very small patches. Experimental results show that the proposed GRAFT method can effectively improve the detection accuracy in this challenging scenario of unsupervised adaptation to resizing pre-processing.
Verifiable Homomorphic Tallying for the Schulze Vote Counting Scheme

The encryption of ballots is crucial to maintaining integrity and anonymity in electronic voting schemes. It enables, amongst other things, each voter to verify that their encrypted ballot has been recorded as cast, by checking their ballot against a bulletin board. We present a verifiable homomorphic tallying scheme for the Schulze method that allows verification of the correctness of the count—on the basis of encrypted ballots—that only reveals the final tally. We achieve verifiability by using zero knowledge proofs for ballot validity and honest decryption of the final tally. Our formalisation takes places inside the Coq theorem prover and is based on an axiomatisation of cryptogtaphic primitives, and our main result is the correctness of homomorphic tallying. We then instantiate these primitives using an external library and show the feasibility of our approach by means of case studies.
Multiple Classifier Framework System for Fast Sequential Prediction of Breast Cancer using Deep Learning Models

A multiway classification model based on CNN is proposed in this paper for Breast Cancer Histopathalogy images of varied magnification level (40X, 100X, 200X, 400X) consisting of total 8 sub-classes (4 Benign and 4 malignant). The study describes the performance metrics when the Dynamic Selection of Classifiers (DSC) methodology was used in the Multiple Classifier Framework System (MCFS) instead of the traditional way of approaching the problem which treats all the sub-classes individually. The obtained metrics suggest that the DSC approach is safer as it firstly classifies the histopathology image as Benign or Malignant which significantly reduces the risk of misclassification. The next classifier then classifies the image into one of subsequent sub-classes in benign and malignant category to provide a secondary opinion to the pathologist such that relevant pathology tests can be performed. Furthermore, the data augmentation strategies applied (such as -Horizontal flip, Vertical Flip, Random Zoom, Random Rotations) to the existing dataset for class balancing seems to show no improvement when the models were trained using the augmented dataset. For evaluating their performances various metrics such as precision, sensitivity, F-score etc. are used.
Efficient Implementation of Cryptography on Points of an Elliptic Curve in Residue Number System

The article explores the question of the effective implementation of arithmetic operations with points of an elliptic curve given over a prime field. Given that the basic arithmetic operations with points of an elliptic curve are the operations of adding points and doubling points, we study the question of implementing the arithmetic operations of adding and doubling points in various coordinate systems using the weighted number system and using the Residue Number System (RNS). We have shown that using the fourmodule RNS allows you to get an average gain for the operation of adding points of the elliptic curve of 8.67% and for the operation of doubling the points of the elliptic curve of 8.32% compared to the implementation using the operation of modular multiplication with special moduli from NIST FIPS 186.
A Chinese Question Answering System based on GPT

The Chinese question-answering system needs to select the most appropriate answer from the answer library for user according to the given question on the natural language form. Previous question-answering systems required modeling for specific task characteristics and designing multiple modules. This paper first proposes to use the Generative Pre-trained Transformer (GPT) to implement the Chinese question-answering system. To optimize and improve the model, this Chinese model pays more attention to the contextual content and semantic characteristics, and we designed a new method to train this model. This model reduces the number of modules in the question-answering system. This paper evaluates the model on the Document-Based Chinese Question and Answer (DBQA) dataset and achieves a 2.5% improvement in MRR/MAP over the latest lattice convolutional neural networks (Lattice CNNs).