Integer Data Research Articles

CDNA-CCS: Splitting and Compression Based Chaotic-DNA Cryptography Framework for Cloud Computing Security

Cloud-computing security is a topic of ongoing research, however, when it comes to data sharing, the main disadvantage is that different users may have data overlap because multiple users save their data on cloud servers. So, the proposed work is to create an improved Cloud Computing Security (CSS) model to protect the data from different threats and reduce overlapping possibilities. SHA-512/256 is designed to produce a fixed-length hash from the user identity data. Further, the data is compressed using Deflate compression, thus reducing the storage space consumption by reducing the byte size. Then, the data are moved to encryption processing; here, hybrid Chaotic-DNA (CDNA) encryption is utilized for encryption purposes. In the Chaotic-DNA process, the data are encrypted based on chaotic workflow, but for an effective security process, a DNA-based key generation is provided. According to the data's level of sensitivity, the key for the hybrid encryption scheme was generated by a trusted centre in the cloud. If the user wants to access the stored data from the cloud system, they must cross four authentication barriers: password, user ID, OTP and fingerprint. The proposed model provides a 98% security level and a 2-second authentication process. For evaluating the proposed-model under various data like image data, text data and integer data. In that image, data takes 90sec for encryption and 0.18sec for decryption time, text data takes 149sec for encryption and 0.69sec for decryption time, and integer data takes 101sec for encryption and 1.4sec for decryption time. This experimental study demonstrates that the recommended security method drastically lowers overlap and effectively reduces space utilization.

Evaluation of Programming in C using WSM Method

Programming in C. The machine-oriented programming language C is mostly used to create many applications and operating systems, like Windows, as well as other complicated programmers, such the Oracle database, Kit, the Python interpreter, and games. Computer programmers and low-level programming applications are often written in the procedural or structured programming language C. Concatenation, data hiding, data compression, inheritance, and polymorphism are some extra aspects of C++ despite the fact that it is an object-oriented programming language. Multiple entities of the same type can be grouped together into a larger group using the C concept of an array. These entities or components may be user-defined data types or structures, such as integer, float, double, or float data types. C-written programmers compile and execute far more quickly than those written in other languages. This is because there are no additional processing overheads like garbage collection. As a result, when compared to other programming languages, the language is quick. An algorithm is a series of actions carried out in a preset order to address a challenge or finish a task. A function is a section of code that is called and carried out by other software elements. An operating system is made using it. The "C" programming language is used to create operating systems like Apple's OS X, Microsoft's Windows, and Symbian. It is utilized to create platforms for desktop and mobile devices. It is used to create compilers. One C feature that contains symbols for doing mathematical, relational, bitwise, conditional, or logical manipulations is the C operator. There are many built-in operators in the C programming language that can be used to carry out different tasks as needed by the application. C has an advantage over other dynamic languages since it is a statically typed programming language. Also, C is a compiler-based programming language, in contrast to Python and Java, which are interpreter-based. It expedites the compilation and execution of code. Weighted Sum works by multiplying the designated field values Indian Technical Institution or appraising the alternatives Analysis in Simply types, Enum types, Struct types, Nullable types. Evaluation parameters in Class types, Interface types, Array types, and Delegate types. Simply types, Enum types, Struct types, Nullable types. Class types, Interface types, Array types, and Delegate types. Nullable types got the first rank whereas Simply types have the lowest rank.

Tiny machine learning empowers climbing inspection robots for real-time multiobject bolt-defect detection

Ensuring the structural integrity of steel construction is critical, necessitating effective methods for detecting bolt defects. Traditional inspection methods are reliable but require significant labor and struggle with the accessibility of complex structures. This research introduces a real-time bolt-defect detection system that integrates tiny machine learning (TinyML) with a magnetic climbing robot to enhance inspection efficiency and scope. The system employs the faster objects, more objects (FOMO) algorithm optimized for edge computing on microcontrollers. This approach enables accurate bolt identification, including normal, loose, and missing bolts. The average system accuracy is 82%, with precision and recall values from 0.57 to 0.89 and 0.67 to 0.87, respectively. The system demonstrates balanced detection, evidenced by an F1 score improvement of up to 62%. The FOMO model displays compelling performance on defect detection tasks, achieving an F1 score of approximately 75%, outperforming the MobileNetV2 Single Shot Multibox Detector with Feature Pyramid Networks Lite and You Only Look Once version 5 small. The efficiency of the FOMO model is highlighted by its low hardware requirements at less than 0.1 MB of flash memory and 893.8 KB of random access memory for the 32-bit floating-point data format and is reduced for the 8-bit integer data format, with inference times of 142 ms and 86 ms, respectively. These findings contrast with the higher resource demands and slower inference time of the compared models, indicating the suitability of the FOMO model for low-capacity microcontrollers and its feasibility for real-time applications. The performance analysis across scenarios confirms the high precision (average 0.77) and recall (average 0.76), validating the robustness of the model in diverse environmental conditions. The system offers an advancement over traditional bolt-defect detection methods in accuracy and efficiency by leveraging TinyML and a magnetic climbing robot, setting new benchmarks for real-time inspection technology. The quantitative results underscore the performance of the system, presenting a scalable, cost-effective solution for improving the safety and durability of steel structures.

Implementation and Evaluation of SIMD Instructions using RISC-V

This thesis introduces and explores the design and verification of a packed Single Instruction, Multiple Data (SIMD) instruction set for a RISC-V processor, known as the RISC-V P extension. This extension enhances the RISC-V instruction set architecture by providing packed SIMD support for 8-bit, 16-bit, and 32-bit integer data types. The significance of this architecture lies in its potential to empower developers in constructing more efficient and powerful data-parallel programs for RISC-V processors. This contribution enhances the overall capabilities of the RISC-V ecosystem, providing a valuable extension to the instruction set architecture for data-parallel applications. Keywords: Data-parallel Programs, instruction set architecture, RISC-V, P extension, Packed SIMD

Semi-Additive Integer-Valued Production Technology for Analyzing Public Hospitals in Mashhad

Conventional Data Envelopment Analysis (DEA) models assume real-valued input-output data and ignore the collaboration among decision-making units (DMUs) in the analysis of efficiency. This paper proposes a novel DEA production technology that is capable of dealing with the collaboration concept and resource sharing for both integer and real-valued data in the efficiency measurement and target setting. This is accomplished by way of a mixed integer linear programming (MILP) efficiency analysis model. We also deal with the computational aspect of the proposed model and invent a MILP with the computational complexity of [Formula: see text] rather than [Formula: see text]. We explain the proposed models by numerical examples and graphical illustrations. We apply our models for efficiency analysis of 15 governmental (public) hospitals in Mashhad City in Iran and demonstrate the practical relevance and advanced future of the proposed methods.

Three etudes on compiling, or How to use the Julia language to study generated machine code

The article demonstrates the ability of the Julia programming language to output an assembly text for any function written in Julia, which is interesting for education. The executable code for three illustrative examples is examined in detail: calculation by formula, branching and cyclic fragments. It is shown that Julia compiler really generates a carefully optimized program, trying to use the fastest executing instructions of the Intel processor. It successfully tries to do without time-consuming arithmetic operations (if possible, the values are calculated in advance, multiplication is replaced by shift and so on). The results for integer data are especially impressive. It is possible to see a whole range of optimization techniques, including those that take into account the features of the algorithm itself: for example, the compiler is able to predict the results of the simplest loop and exclude this loop from the final program. The problem of overflow control in a program is also discussed.The consideration carried out convincingly confirms the usefulness of theoretical knowledge for developing an effectively working program. The described analysis technique is interesting for education, because the importance of a deep understanding by modern specialists of the programming process essence cannot be overestimated. The material can be useful in teaching programming ideas at different levels. The detailed presentation makes the article readable without special preparation.

Ensuring Data Integrity And Security In Diverse Cloud Environments To Prevent Duplicacy.

Data deduplication is a valuable technique for compressing and minimizing data duplication during data transfers, especially in cloud environments. By eliminating redundant data, it optimizes transmission capacity and reduces memory usage. To ensure the integrity of sensitive data, encryption is applied throughout the deduplication process. The SHA algorithm is commonly used for storing text data during deduplication. It generates security bits by padding the text and computes a hash consisting of hexadecimal, string, and integer data. Hash-based deduplication involves hashing the entire file and treating the hash values of text data as unique identifiers. This allows clients to identify duplicate data within the cloud. The Proof-of-Work (PoW) algorithm is widely utilized in blockchain networks like Bitcoin and Ethereum. Its primary function is to verify transactions through a process called mining. Miners engage in competition to solve complex mathematical problems, and the first one to find a solution is granted the right to add a new block to the blockchain. PoW relies on cryptographic hashing, such as the SHA-256 hashing function, to validate and secure transactions. On the other hand, the Proof of Retrievability (PoR) algorithm finds application in cloud computing systems. It serves as a consensus mechanism, ensuring that cloud storage providers store and retrieve data accurately. PoR enables cloud providers to demonstrate to consumers that their files can be fully recovered. This algorithm incorporates cryptographic proofs that validate the integrity and availability of the stored data. In cloud storage, deduplication is often implemented using the Memory mapping technique (MPT). This allows multiple data owners to store the same data in a single copy, enhancing storage efficiency. To maintain data security, encryption is applied both before and during the deduplication process, ensuring that sensitive information remains protected. In summary, data deduplication is a powerful method for compressing data, minimizing duplication, and optimizing transmission capacity. With encryption techniques and hash-based identification, it provides secure and efficient deduplication in cloud environments, while memory deduplication and MPT support further enhance performance and storage efficiency.

Security enhancement in a cloud environment using a hybrid chaotic algorithm with multifactor verification for user authentication

A hybrid chaotic-based DNA and multifactor authentication strategy are created to improve the protection of the cloud environment. Initially, multimodal data are collected from the data owner then the information is compressed by utilizing the deflate compression approach. The data is then encrypted using hybrid chaotic-based DNA cryptography to increase the security of data. In this hybrid algorithm DNA is used for the key generation and chaotic algorithm is utilized for the encryption process. On the other hand, a multifactor authentication method is created to access data from the cloud to block access by unauthorized users. In that technique, users are requested to enter the registered Password with the generated OTP from the mobile number. Then, the device serial number is another factor to verify the accessing device. Likewise, the user's fingerprint and iris recognition are also validated for accessing the data. The cloud-based data can be accessible following users' successful authentication. The simulation analysis shows that the encryption and decryption time reached for image, string and integer data is 24, 0.065, 37, 0.14, 28 and 0.14 s, respectively, for the cloud security algorithm. The proposed algorithm effectively mitigates space consumption and provides improved data security in a cloud environment.

Number of Solutions for Some Special Logical Analysis Problems of Integer Data

Number of Solutions for Some Special Logical Analysis Problems of Integer Data

Number of Solutions for Some Special Logical Analysis Problems of Integer Data

In the class of discrete enumeration problems, an important place belongs to the problems of searching for frequently and infrequently occurring elements in integer data. Questions on the effectiveness of such a search are directly related to the study of the metric (quantitative) properties of sets of frequent and infrequent elements. It is assumed that the initial data are presented in the form of an integer matrix, whose rows are descriptions of the studied objects in the given system of the numerical characteristics of these objects, called attributes. The case is considered when each attribute takes values from the set {0,1,...k-1}, k2. Asymptotic estimates for the typical number of special, frequent fragments of object descriptions, called correct fragments, and estimates for the typical length of such a fragment are given. We also present new results concerning the study of the metric properties of the minimal infrequent fragments of descriptions of objects.

Optimasi Pengurutan Data Bilangan dengan Menggabungkan Algoritma Selection Sort Hybrid dan Bucket Sort

Sorting algorithms are crucial in data processing, particularly for integer data. However, as the number of integers to be sorted increases, the sorting algorithm takes longer to complete, especially for algorithms with O(n2) complexity. This article discusses optimizing integer data sorting by combining the Selection Sort Hybrid and Bucket Sort algorithms. The study aims to test the performance of the Selection Sort Hybrid and Bucket Sort algorithms and compare them with other data sorting algorithms. The research method used is experimental quantitative research, using randomly generated data using Python. The data were tested using the Combined Selection Sort Hybrid with Bucket Sort algorithm, Selection Sort Hybrid, Quick Sort, and Merge Sort. Data analysis was done by calculating the execution time of each sorting algorithm. The results show that the Selection Sort Hybrid and Bucket Sort algorithms are faster than other sorting algorithms in testing with large and complex integer data. Therefore, combining Selection Sort Hybrid and Bucket Sort algorithms can improve the efficiency and speed of sorting complex integer data.

Analisis Dan Implementasi Algoritma Rivest Code-5 Pada Keamanan Data

The RC-5 algorithm is an algorithm with the encryption method using the symmetric method and processing in the form of a cipher block, the same keywords are used for the encryption and decryption processes. The testing and analysis phase consists of a). Testing the execution time of the key generation process (set up key) b). Testing the success of the encryption and decryption process on the client-server integration method c). Testing the success of the encryption and decryption process using the file operation method d). Comparison of overhead/file size before and after passing through the system. The testing phase and execution time analysis are carried out when using the file operation method where the test will be divided into 3 parts, namely the execution time for the key setup process, the encryption process, and the decryption process. Testing the success of the encryption and decryption process on the client-server integration method is divided into 2 parts, namely testing the encryption process and the decryption process. In testing the encryption process, the characters that will be sent to the server will be encrypted first with a key to becoming a ciphertext in the form of integer data. The test results obtained are 1). The execution time for key generation (set up key) is very fast, around 9-10 ns. 2). In the encryption and decryption process, the execution time depends on the size of the plaintext file. The larger the plaintext file size, the longer the execution time. Where for a file with a size of 1 kb requires an execution time of 50.6 ms in the encryption process. 3). There is no difference between the size of the file before entering encryption and after decryption. Where for files with a size of 500 bytes before entering the encryption process, a file of 500 bytes will be generated from the decryption process.

Average-profit efficiency and optimal scale sizes with integer data and undesirable outputs

PurposeData envelopment analysis (DEA) is a significant method for measuring the relative efficiency of decision making units (DMUs) that use the least inputs, produce the most desirable outputs and emit the least undesirable outputs in order to maximize their profits. In DEA, detecting an optimal scale size (OSS) is also vital and could be more applicable in economic activities when there are integer and undesirable measures. The purpose of this research is to measure average-profit efficiency (APE) and OSSs with integer data and undesirable outputs.Design/methodology/approachThis study presents an alternative concept of APE using the concepts of most productive scale size (MPSS), profit efficiency and scales, containing desirable and undesirable outputs along with integer and non-integer measures. In fact, the OSS minimizes APE as the optimal scale, which is the ratio of the profit efficiency to the radial average output. Considering the prices of the inputs and desirable outputs, as well as the lack of any specific weight for the undesirable outputs, a two-step model for the numerical calculation of OSS is presented. In addition, the proposed approach is applied to a real data set of Iranian gas companies while there are integer measures and undesirable outputs.FindingsThe results show the introduced approach is beneficial to estimate OSSs from the aspect of maximizing profits of firms with undesirable outputs and integer values.Originality/valueEstimating OSSs is the significant issue for managers, but its investigation in the presence of integer measures and undesirable outputs is presently under-considered.

Using slacks-based model to solve inverse DEA with integer intervals for input estimation

This paper deals with an inverse data envelopment analysis (DEA) based on the non-radial slacks-based model in the presence of uncertainty employing both integer and continuous interval data. To this matter, suitable technology and formulation for the DEA are proposed using arithmetic and partial orders for interval numbers. The inverse DEA is discussed from the following question: if the output of DMU_o increases from Y_o to beta _o, such the new DMU is given by (alpha _o^*,beta ) belongs to the technology, and its inefficiency score is not less than t-percent, how much should the inputs of the DMU increase? A new model of inverse DEA is offered to respond to the previous question, whose interval Pareto solutions are characterized using the Pareto solution of a related multiple-objective nonlinear programming (MONLP). Necessary and sufficient conditions for input estimation are proposed when output is increased. A functional example is presented on data to illustrate the new model and methodology, with continuous and integer interval variables.

Predicting Integer Overflow Errors via Supervised Learning

An integer overflow error occurs when an integer operation in computer software evaluates a value out of the integer range. It can lead to a fatal system failure. The existing approaches to detecting integer overflow errors rely on data/control-flow analysis of the code or execution of the code with test cases. This paper presents a supervised learning approach to predicting whether each method in a given Java program has an integer overflow error by treating the source code as text. Built upon real-world programs, our Java dataset covers all integer data types and operations in Java, the methods for preventing integer overflow errors, and adversarial samples. We have evaluated six classification models, BERT, DistilBERT, codeBERT, Code2Vec, fastText, and NBSVM. They represent different text embedding techniques for dealing with source code. The experiment results show that BERT and its variants have outperformed other models. We have applied the resultant BERT model to eleven real-world projects, including JDK13.0 and ten top-ranked GitHub projects, and revealed 181 integer overflow errors. In addition, we have evaluated the classification models with a C/C++ dataset. The result is similar to that of the Java dataset.

Models for Integer Data

Over the past few years, interest has increased in models defined on positive and negative integers. Several application areas lead to data that are differences between positive integers. Some important examples are price changes measured discretely in financial applications, pre- and posttreatment measurements of discrete outcomes in clinical trials, the difference in the number of goals in sports events, and differencing of count-valued time series. This review aims at bringing together a wide range of models that have appeared in the literature in recent decades. We provide an extensive review on discrete distributions defined for integer data and then consider univariate and multivariate time-series models, including the class of autoregressive models, stochastic processes, and ARCH-GARCH– (autoregressive conditionally heteroskedastic–generalized autoregressive conditionally heteroskedastic–) type models.

An Order-Preserving Encryption Scheme Based on Weighted Random Interval Division for Ciphertext Comparison in Wearable Systems.

With the rapid development of wearable devices with various sensors, massive sensing data for health management have been generated. This causes a potential revolution in medical treatments, diagnosis, and prediction. However, due to the privacy risks of health data aggregation, data comparative analysis under privacy protection faces challenges. Order-preserving encryption is an effective scheme to achieve private data retrieval and comparison, but the existing order-preserving encryption algorithms are mainly aimed at either integer data or single characters. It is urgent to build a lightweight order-preserving encryption scheme that supports multiple types of data such as integer, floating number, and string. In view of the above problems, this paper proposes an order-preserving encryption scheme (WRID-OPES) based on weighted random interval division (WRID). WRID-OPES converts all kinds of data into hexadecimal number strings and calculates the frequency and weight of each hexadecimal number. The plaintext digital string is blocked and recombined, and each block is encrypted using WRID algorithm according to the weight of each hexadecimal digit. Our schemes can realize the order-preserving encryption of multiple types of data and achieve indistinguishability under ordered selection plaintext attack (IND-OCPA) security in static data sets. Security analysis and experiments show that our scheme can resist attacks using exhaustive methods and statistical methods and has linear encryption time and small ciphertext expansion rate.

Measuring and analyzing the impact of population growth on the labor force and unemployment in Iraq During the period(1990 - 2020)

The relationship between population growth and the labor force has become of great importance, as it affects development, because the high population growth that is accompanied by an increase in the numbers of the human force, which contributes to the high rate of unemployment as well as impeding the process of economic and social development, and from this point of view revolves the subject of the current study about Population growth and its impact on the labor force and unemployment. To achieve this, the standard analytical method was relied on using a set of models (CONNT INTEGER DATA, FMOLS), and in the end, a conclusion was reached, which is that population growth has a negative impact on the labor force and unemployment. Accordingly, it is necessary for the government and the concerned authorities to pay great attention to the issue of high population growth and to limit it.

Visual Attention-Aware High Dynamic Range Quantization for HEVC Video Coding

Emerging HDR videos enable the recording of adequate luminance information and representing realistic scenes to the audience. Due to the high precision of the HDR data recorded in the floating-point format, a quantization process is required to convert HDR data to integer data for compatibility with current transmission and display systems. In this study, a novel attention-aware quantization method is presented that attempts to preserve the contrast details in the region of interest of the human visual system. This method was applied in the context of HDR video coding. The proposed coding solution was compared with the current anchor solution in terms of the quality of the reconstructed video. Experimental results show that the proposed solution is able to improve the visual quality of encoded video with respect to the anchor solution. Additionally, the proposed solution achieves a bit-rate gain over the anchor with reference to the objective evaluation results.

Assessing the sustainability of transport supply chains by double frontier network data envelopment analysis

The transport industry is one of the main contributors to environmental pollution. Sustainable supply chain management (SSCM) is an essential subject in the transportation industry. Today, one of the important goals of organizations is to evaluate the sustainability of supply chain (SC) because assessing the efficiency of SCs helps organizations to enhance their awareness of performance and develop managerial strategies. Data envelopment analysis (DEA) is a common technique to assess the sustainability. The objective of this paper is to propose a Malmquist productivity index (MPI) based on network data envelopment analysis (NDEA) model in the presence of integer data, undesirable outputs, and non-discretionary inputs. The NDEA models deal with the internal structure of decision making units (DMUs). The MPI reflects the productivity change over time. The proposed model can fully rank DMUs. To prove the applicability of the proposed model, the sustainability of intercity passenger transportation is evaluated.