Integer Encoding Research Articles

Extended Addition Protocol and Efficient Voting Protocols Using Regular Polygon Cards

Card-based cryptography is a research field for realizing cryptographic protocols using a deck of physical cards. Shinagawa et al. proposed a regular n-sided polygon card, which can hold a value from 0 to n-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$n-1$$\\end{document}, and constructed an addition protocol over Z/nZ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathbb {Z}/n\\mathbb {Z}$$\\end{document} and a voting protocol with v voters and c candidates when v<n\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$v<n$$\\end{document}. In this paper, we propose an addition protocol over Z/mnZ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathbb {Z}/mn\\mathbb {Z}$$\\end{document} using regular n-sided polygon cards. Technically, we introduce a cyclic integer encoding and a rot-and-shift shuffle to extend the modulus from n to mn. In addition, we construct two voting protocols with v voters and c candidates using regular n-sided polygon cards. Our first voting protocol requires c(⌈v+1n⌉+v+1)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$c(\\lceil \\frac{v+1}{n} \\rceil + v + 1)$$\\end{document} cards and v+1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$v+1$$\\end{document} shuffles without restriction. Our second voting protocol reduces the number of cards to ⌈v+1n⌉n+v+1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\lceil \\frac{v+1}{n} \\rceil n + v + 1$$\\end{document} when v<n\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$v < n$$\\end{document} and c≤n\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$c\\le n$$\\end{document}.

Empirical Analysis for Classification of Fake News through Text Representation

Fake news refers to inaccurate or deceptive information that is portrayed as legitimate news. It is intentionally generated and disseminated to mislead the public. Fake news takes on multiple forms, including altered visuals, invented narratives, and misrepresented accounts of actual occurrences, although this work focuses solely on textual content. Initially, the focus of this work is to evaluate various pre-processing techniques involved in fake news detection, such as TF-IDF, GloVe, and Integer Encoding. Each of these techniques has its own way of converting text to numerical format. Despite numerous studies in this field, there is still a research gap regarding the comparative analysis of TF_IDF (Term Frequency Inverse Document Frequency), Integer Encoding, and GloVe (Global Vector for Word Representation) specifically for fake news tasks. This study aims to bridge this gap by evaluating and comparing the performance of these three popular preprocessing techniques. Next, three RNN variants are used in this experiment for the classification task. They are SimpleRNN (Simple Recurrent Neural Network), LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Unit). The reason behind choosing RNN variants is RNN is capable of capturing long term dependencies. It is proven to be effective in handling sequential data. It consists of memory that stores the previous important content. GloVe showed high accuracy in GRU model, and it also used only less computational resources, but LSTM took more time and required more computational resources. The results produced by GRU and LSTM for GloVe were better than the rest of the combinations. Integer Encoding also produced good results. But TF-IDF gives poor results when fed to Deep Learning models like RNN, LSTM, and GRU, but when it is fed to Machine Learning Model it gives good accuracy. This is due to sparse matrix generation based on the importance of term frequency. The findings highlight the advantages and limitations of each algorithm, providing valuable guidance for researchers and practitioners in choosing the suitable method for their specific needs. The experimental finding of this work is that GloVe with GRU produces the highest accuracy of 92.15%

Enhancing IoT Data Integrity and Effectiveness through hybrid Compression Method: A Step Towards Energy Efficiency

The expansion of the Internet of Things (IoT) has magnified the challenge of managing data generated by IoT devices, notably in meteorological applications like temperature and humidity monitoring. This research addresses the imperative of efficiently reducing IoT data volume while preserving data integrity and underscores the significant implications for energy consumption. Our approach involved a two-fold strategy, employing the DHT11 sensor and ESP32 microcontroller for data collection, followed by an exploration of various data compression algorithms: delta encoding, run-length encoding (RLE), variable-length integer encoding (VLI), and bit-packing. The strategic combination of RLE and delta encoding yielded an exceptional compression rate of 98%. Beyond data reduction, this methodology offers energy savings by minimizing data transmission times, evidenced by the swift 133-microsecond compression process. Furthermore, the seamless transmission of compressed IoT data to Azure Cloud not only reduced cloud storage costs but also optimized storage space, contributing to energy efficiency. This research illuminates the significance of data compression in mitigating the environmental impact of IoT technologies, fostering a greener, more energy-conscious future.

Offensive Language Detection for Low Resource Language Using Deep Sequence Model

Social media platforms are heavily used by people to express their views in their native languages. Besides positive views, people often use abusive or offensive language to express their anger or frustration. Resource-rich languages have offensive language detection systems which automatically monitor and block offensive content, however, they are very rare for low-resourced languages. This is because of the nonavailability of datasets for local languages. This article proposes a model which automatically detects offensive language for a very low-resource language, i.e., Pashto. The Roman Pashto dataset is created by picking 60 thousand comments from different social media and labeling them manually. The proposed model is trained and tested using three different feature extraction approaches, i.e., bag-of-words (BoW), term frequency-inverse document frequency (TF-IDF), and sequence integer encoding. Four traditional classifiers and a deep sequence model are used to train on this task. Experimental result shows that the random forest classifier works best and give 94.07 % testing accuracy on a combination of unigrams, bigrams, and trigrams. The same classifier gives maximum accuracy of 93.90 % with TF-IDF. However, the overall highest testing accuracy of 97.21% is achieved by using bidirectional long short-term memory (BLSTM). The corpus created in this work is made available for the researcher working in this domain.

Rethinking the Encoding of Integers for Scans on Skewed Data

Bit-parallel scanning techniques are characterized by their ability to accelerate compute through the process known as early pruning. Early pruning techniques iterate over the bits of each value, searching for opportunities to safely prune compute early, before processing each data value in its entirety. However, because of this iterative evaluation, the effectiveness of early pruning depends on the relative position of bits that can be used for pruning within each value. Due to this behavior, bit-parallel techniques have faced significant challenges when processing skewed data, especially when values contain many leading zeroes. This problem is further amplified by the inherent trade-off that bit-parallel techniques make between columnar scan and fetch performance: a storage layer that supports early pruning requires multiple memory accesses to fetch a single value. Thus, in the case of skewed data, bit-parallel techniques increase fetch latency without significantly improving scan performance when compared to baseline columnar implementations. To remedy this shortcoming, we transform the values in bit-parallel columns using novel encodings. We propose the concept of forward encodings: a family of encodings that shift pruning-relevant bits closer to the most significant bit. Using this concept, we propose two particular encodings: the Data Forward Encoding and the Extended Data Forward Encoding. We demonstrate the impact of these encodings using multiple real-world datasets. Across these datasets, forward encodings improve the current state-of-the-art bit-parallel technique's scan and fetch performance in many cases by 1.4x and 1.3x, respectively.

Cognitive Diagnosis-Based Personalized Exercise Group Assembly via a Multi-Objective Evolutionary Algorithm

Exercise group recommendation plays an important role in many intelligent education tasks. However, existing approaches make recommendations based on the intrinsic features of exercises without considering students' learning abilities, or make selections from several pre-built exercise groups at the expense of flexibility. Furthermore, although many cognitive diagnosis approaches have successfully revealed students' abilities, how to leverage the diagnosis results for exercise group recommendation is hardly explored. To flexibly recommend suitable exercise groups to students, this paper proposes to assemble personalized exercises as a group based on students' abilities, called Personalized Exercise Group Assembly (PEGA). To solve the PEGA task, we first formulate it as a constrained multi-objective problem (CMOP), where three objectives are designed for enabling the assembled exercises to enhance students' abilities on both weak and new knowledge concepts. Then, we devise an extended neural cognitive diagnosis model to learn student's ability/proficiency on all knowledge concepts to compute the weakness consolidation objective. Besides, we propose a dual-encoding and dual-population based co-evolutionary algorithm to tackle the CMOP, where the main population with binary encoding is used to search which exercises are selected, and the auxiliary population with integer encoding is responsible for accelerating the convergence of the main population via guiding offspring generation of the main population. Experiments on three popular datasets demonstrate the effectiveness of exercises assembled by the proposed algorithm compared to state-of-the-art exercise group recommendation approaches, where our assembled exercises can enhance students' proficiency on both poorly mastered and new knowledge concepts.

A Management Method of Multi-Granularity Dimensions for Spatiotemporal Data

To understand the complex phenomena in social space and monitor the dynamic changes in people’s tracks, we need more cross-scale data. However, when we retrieve data, we often ignore the impact of multi-scale, resulting in incomplete results. To solve this problem, we proposed a management method of multi-granularity dimensions for spatiotemporal data. This method systematically described dimension granularity and the fuzzy caused by dimension granularity, and used multi-scale integer coding technology to organize and manage multi-granularity dimensions, and realized the integrity of the data query results according to the correlation between the different scale codes. We simulated the time and band data for the experiment. The experimental results showed that: (1) this method effectively solves the problem of incomplete query results of the intersection query method. (2) Compared with traditional string encoding, the query efficiency of multiscale integer encoding is twice as high. (3) The proportion of different dimension granularity has an impact on the query effect of multi-scale integer coding. When the proportion of fine-grained data is high, the advantage of multi-scale integer coding is greater.

Predicting the accuracy of nanofluid heat transfer coefficient's computational fluid dynamics simulations using neural networks

AbstractThis research presents a neural network algorithm to identify the best modeling and simulation methods and assumptions for the most widespread nanofluid combinations. The neural network algorithm is trained using data from earlier nanofluid experiments. A multilayer perceptron with one hidden layer was employed in the investigation. The neural network algorithm and data set were created using the Python Keras module to forecast the average percentage error in the heat transfer coefficient of nanofluid models. Integer encoding was used to encode category variables. A total of 200 trials of different neural networks were taken into consideration. The worst‐case error bound for the chosen architecture was then calculated after 100 runs. Among the eight models examined were the single‐phase, discrete‐phase, Eulerian, mixture, the mixed model of discrete and mixture phases, fluid volume, dispersion, and Buongiorno's model. We discover that a broad range of nanofluid configurations is accurately covered by the dispersion, Buongiorno, and discrete‐phase models. They were accurate for particle sizes (10–100 nm), Reynolds numbers (100–15,000), and volume fractions (2%–3.5%). The accuracy of the algorithm was evaluated using the root mean square error (RMSE), mean absolute error (MAE), and R2 performance metrics. The algorithm's R2 value was 0.80, the MAE was 0.77, and the RMSE was 2.6.

A Hybrid Integer Encoding Method for Obtaining High-Quality Solutions of Quadratic Knapsack Problems on Solid-State Annealers

For formulating Quadratic Knapsack Problems (QKPs) into the form of Quadratic Unconstrained Binary Optimization (QUBO), it is necessary to introduce an integer variable, which converts and incorporates the knapsack capacity constraint into the overall energy function. In QUBO, this integer variable is encoded with auxiliary binary variables, and the encoding method used for it affects the behavior of Simulated Annealing (SA) significantly. For improving the efficiency of SA for QKP instances, this paper first visualized and analyzed their annealing processes encoded by conventional binary and unary encoding methods. Based on this analysis, we proposed a novel hybrid encoding (HE), getting the best of both worlds. The proposed HE obtained feasible solutions in the evaluation, outperforming the others in small- and medium-scale models.

Solving Program Sketches with Large Integer Values

Program sketching is a program synthesis paradigm in which the programmer provides a partial program with holes and assertions. The goal of the synthesizer is to automatically find integer values for the holes so that the resulting program satisfies the assertions. The most popular sketching tool, Sketch , can efficiently solve complex program sketches but uses an integer encoding that often performs poorly if the sketched program manipulates large integer values. In this article, we propose a new solving technique that allows Sketch to handle large integer values while retaining its integer encoding. Our technique uses a result from number theory, the Chinese Remainder Theorem, to rewrite program sketches to only track the remainders of certain variable values with respect to several prime numbers. We prove that our transformation is sound and the encoding of the resulting programs are exponentially more succinct than existing Sketch encodings. We evaluate our technique on a variety of benchmarks manipulating large integer values. Our technique provides speedups against both existing Sketch solvers and can solve benchmarks that existing Sketch solvers cannot handle.

Regularized target encoding outperforms traditional methods in supervised machine learning with high cardinality features

Since most machine learning (ML) algorithms are designed for numerical inputs, efficiently encoding categorical variables is a crucial aspect in data analysis. A common problem are high cardinality features, i.e. unordered categorical predictor variables with a high number of levels. We study techniques that yield numeric representations of categorical variables which can then be used in subsequent ML applications. We focus on the impact of these techniques on a subsequent algorithm’s predictive performance, and—if possible—derive best practices on when to use which technique. We conducted a large-scale benchmark experiment, where we compared different encoding strategies together with five ML algorithms (lasso, random forest, gradient boosting, k-nearest neighbors, support vector machine) using datasets from regression, binary- and multiclass–classification settings. In our study, regularized versions of target encoding (i.e. using target predictions based on the feature levels in the training set as a new numerical feature) consistently provided the best results. Traditionally widely used encodings that make unreasonable assumptions to map levels to integers (e.g. integer encoding) or to reduce the number of levels (possibly based on target information, e.g. leaf encoding) before creating binary indicator variables (one-hot or dummy encoding) were not as effective in comparison.

Survival prediction model for right-censored data based on improved composite quantile regression neural network.

With the development of the field of survival analysis, statistical inference of right-censored data is of great importance for the study of medical diagnosis. In this study, a right-censored data survival prediction model based on an improved composite quantile regression neural network framework, called rcICQRNN, is proposed. It incorporates composite quantile regression with the loss function of a multi-hidden layer feedforward neural network, combined with an inverse probability weighting method for survival prediction. Meanwhile, the hyperparameters involved in the neural network are adjusted using the WOA algorithm, integer encoding and One-Hot encoding are implemented to encode the classification features, and the BWOA variable selection method for high-dimensional data is proposed. The rcICQRNN algorithm was tested on a simulated dataset and two real breast cancer datasets, and the performance of the model was evaluated by three evaluation metrics. The results show that the rcICQRNN-5 model is more suitable for analyzing simulated datasets. The One-Hot encoding of the WOA-rcICQRNN-30 model is more applicable to the NKI70 data. The model results are optimal for k=15 after feature selection for the METABRIC dataset. Finally, we implemented the method for cross-dataset validation. On the whole, the Cindex results using One-Hot encoding data are more stable, making the proposed rcICQRNN prediction model flexible enough to assist in medical decision making. It has practical applications in areas such as biomedicine, insurance actuarial and financial economics.

An energy-efficient hybrid flow shop scheduling problem in steelmaking plants

Energy consumption is the basic condition for the smooth operation of steelmaking plant production process. Meanwhile, energy consumption is influenced by the production process. There is an increasing awareness in scheduling research of steelmaking plants that energy consumption needs to be considered. This paper studies a steelmaking plant scheduling problem considering energy saving (SPSP-ES) through a two-level strategy to simultaneously enhance production efficiency and reduce energy cost. The energy consumption with regard to the production process is defined, and a multi-objective scheduling model aiming at minimizing the makespan, average charge comprehensive energy consumption and fluctuation of energy consumption and production is developed. To tackle this problem, an improved multi-objective evolutionary algorithm based on decomposition (IMOEA/D) is proposed. Considering the influence of different processing paths on energy consumption, the discrete integer encoding and heuristic decoding based on processing paths are adopted. In addition, to enhance the local search capability, variable neighbourhood search is introduced in the search process. The superior performance of the developed algorithm is verified through numerical experiments. Furthermore, the results of two scheduling mode, which are energy-saving mode and high-efficiency mode, are compared to evaluate the effectiveness of the proposed model. The results show that the proposed model can decrease the comprehensive energy consumption and fluctuation of energy consumption and production up to 4.4% and 56.8% respectively, which strongly confirms that the scheduling considering energy-related objectives can balance the energy consumption and production, and reduce charge comprehensive energy consumption while improving the production efficiency.

Satisfiability modulo theories and chiral heterotic string vacua with positive cosmological constant

We apply Boolean Satisfiability (SAT) and Satisfiability Modulo Theories (SMT) solvers in the context of finding chiral heterotic string models with positive cosmological constant from Z2×Z2 orbifolds. The power of using SAT/SMT solvers to sift large parameter spaces quickly to decide satisfiability, both to declare and prove unsatisfiability and to declare satisfiability, are demonstrated in this setting. These models are partly chosen to be small enough to plot the performance against exhaustive search, which takes around 2 hours 20 minutes to comb through the parameter space. We show that making use of SMT based techniques with integer encoding is rather simple and effective, while a more careful Boolean SAT encoding provides a significant speed-up – determining satisfiability or unsatisfiability has, in our experiments varied between 0.03 and 0.06 seconds, while determining all models (where models exist) took 19 seconds for a constraint system that allows for 2048 models and 8.4 seconds for a constraint system that admits 640 models. We thus gain several orders of magnitude in speed, and this advantage is set to grow with a growing parameter space. This holds the promise that the method scales well beyond the initial problem we have used it for in this paper.

Biased random-key genetic algorithm for cobot assignment in an assembly/disassembly job shop scheduling problem

Abstract Nowadays many manufacturing companies try to improve the performance of their processes by including innovative available technologies such as collaborative robots. Collaborative robots are robots where no safety distance is necessary, through cooperation with human workers they can increase production speed. In this paper we consider the collaborative robot assignment combined with the job shop scheduling problem. To solve this problem, we propose a genetic algorithm with a biased random-key encoding. The objective function for the optimization is a weighted function that factors in production cost and makespan that should be minimized. We propose a special encoding of the solution: the assignment of cobots to workstations, the assignment of tasks to different workstations and the priority of tasks. The results show how much the weighted objective function can be decreased by the deployment of additional collaborative robots in a real-world production line. Additionally, the biased random-key encoded results are compared to typical integer encoded solution. With the biased random-key encoding, we were able to find better results than with the standard integer encoding.

Analysis of Variable-Length Codes for Integer Encoding in Hyperspectral Data Compression with the k2-Raster Compact Data Structure

This paper examines the various variable-length encoders that provide integer encoding to hyperspectral scene data within a k 2 -raster compact data structure. This compact data structure leads to a compression ratio similar to that produced by some of the classical compression techniques. This compact data structure also provides direct access for query to its data elements without requiring any decompression. The selection of the integer encoder is critical for obtaining a competitive performance considering both the compression ratio and access time. In this research, we show experimental results of different integer encoders such as Rice, Simple9, Simple16, PForDelta codes, and DACs. Further, a method to determine an appropriate k value for building a k 2 -raster compact data structure with competitive performance is discussed.

On Designing an Expert Diagnostic System of the Aircraft Turbine Engine Functional Units

Abstract The paper presents issues related to the design of an expert diagnostic system of turbine engine functional units. Dedicated diagnostic stations and on-board flight data recorders are the sources of diagnostic signals. The signals were parameterized or identified dynamic models to get a compact representation in the form of a set of parameters. The set of diagnostic parameters was subjected to integer encoding. On this basis, a multi-valued diagnostic model describing the relationship between the set of faults and the set of symptoms (code values of diagnostic parameters) was determined. The proposed approach can be used in the design of expert diagnostic systems for propulsion units of any aircraft.

A genetic algorithm rooted in integer encoding and fuzzy controller

The premature convergence is the essential problem in genetic algorithms and it is strongly related to the loss of genetic diversity of the population. In this study, a new sexual selection mechanism which utilizing mate chromosome during selection proposed and then technique focuses on selecting and controlling the genetic operators by applying the fuzzy logic controller. Computational experiments are conducted on the proposed techniques and the results are compared with some other operators, heuristic and local search algorithms commonly used for solving benchmark problems published in the literature.

Practical integer-to-binary mapping for quantum annealers

Recent advancements in quantum annealing hardware and numerous studies in this area suggests that quantum annealers have the potential to be effective in solving unconstrained binary quadratic programming problems. Naturally, one may desire to expand the application domain of these machines to problems with general discrete variables. In this paper, we explore the possibility of employing quantum annealers to solve unconstrained quadratic programming problems over a bounded integer domain. We present an approach for encoding integer variables into binary ones, thereby representing unconstrained integer quadratic programming problems as unconstrained binary quadratic programming problems. To respect some of the limitations of the currently developed quantum annealers, we propose an integer encoding, named bounded- coefficient encoding, in which we limit the size of the coefficients that appear in the encoding. Furthermore, we propose an algorithm for finding the upper bound on the coefficients of the encoding using the precision of the machine and the coefficients of the original integer problem. Finally, we experimentally show that this approach is far more resilient to the noise of the quantum annealers compared to traditional approaches for the encoding of integers in base two.

A decision variable-based combinatorial optimization approach for interval-valued intuitionistic fuzzy MAGDM

Some interval-valued intuitionistic fuzzy information MAGDM problems with combinatorial optimization characteristics have so many feasible alternatives that they can hardly be enumerated and evaluated. Moreover, due to the lack of quantitative functions, they are difficult to be solved with traditional combinatorial optimization methods. Therefore, by combining the MAGDM with combinatorial optimization, we propose a decision variable-based combinatorial optimization approach for the purpose of solving the interval-valued intuitionistic fuzzy MAGDM problems in this paper. Firstly, the utility evaluation matrix of decision maker is defined and the interval-valued intuitionistic fuzzy weighted average operator is used to aggregate utility evaluation matrices with decision makers’ preference information. Then, the 0–1 integer decision variable is introduced and a combinatorial optimization model is established for the interval-valued intuitionistic fuzzy MAGDM problem. For the purpose of comparison, a multi-attribute decision-making method based on the weighted relative distance is adopted to rank different alternatives. Besides, an artificial bee colony algorithm improved by integer encoding, ranking selection and elite guidance is used to solve the combinatorial optimization model. Finally, two applications of the approach are proposed: research group dispatch and group weapon-group target assignment. Through the comparison of the algorithms, the feasibility of the proposed approach was verified.