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A novel binary quantizer for variational autoencoder-based image compressor

International Journal of computers and Applications 2024年第8期46卷 604-620页

作者： Thulasidharan, Pillai Praveen Nath, Keshab Department of Computer Science and Engineering Indian Institute of Information Technology Kottayam India

Neural network-based encoder and decoder are one of the emerging techniques for image compression. To improve the compression rate, these models use a special module called the quantizer that improves the entropy of the generated representation. Here, we propose a novel binary quantizer and an auxiliary support module known as the remapper that co-ordinate for better compression and decompression of an image. The proposed quantizer generates the compressed binary representation whereas the remapper aids in minimizing the reconstruction error during decompression. The combined optimization of the model consisting of the encoder, quantizer, remapper, and the decoder as a single entity is not feasible due to the non-differentiable nature of the quantizer. To mitigate this, we propose a step-by-step training process wherein the encoder and decoder are trained first, then keeping the encoder parameters fixed, the remapper is trained along with the decoder to obtain a fully optimized model. The training is performed with different types of images and the results are compared to understand the best training procedures. Experimental results reveal that the proposed model can compress a (Formula presented.) image to a mere 256 bits, thereby achieving a compression ratio of 6144:1;with excellent perceptual reconstruction during decompression. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Image compression

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L-DTC: Load-based Dynamic Throughput Control for Guaranteed I/O Performance in Virtualized Environments

IEEE Computer Architecture Letters

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IEEE computer Architecture Letters 2025年第1期24卷 145-148页

作者： Kim, TaeHoon No, Jaechun Sejong University Department of Computer Science and Engineering Seoul05006 Korea Republic of

In this letter, we identified an issue where the I/O performance of specific tasks could not be guaranteed during multi-process I/O operations, despite the use of the latest storage technologies in virtualized environments. To address this issue, we propose L-DTC, a novel Load-based Dynamic Throughput Control technique, designed to achieve the guaranteed I/O performance in virtualized environments. Operating at the hypervisor level, L-DTC provides fine-grained throughput control based on I/O queues and ensures the independent I/O performance for each process by allowing users to define maximum and minimum throughput levels for each queue. We conducted an evaluation of L-DTC and confirmed that it successfully guarantees the I/O performance requirements of specific processes in multi-process environments. Furthermore, L-DTC achieved more stable I/O performance compared to existing methods, with improvements in I/O performance of up to 2.1 times, regardless of the I/O scheduler. © 2002-2011 IEEE.

关键词： Virtual environments

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A Robust Decryption Technique Using Letter Frequency Analysis for Short Monoalphabetic Substitution Ciphers

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Journal of Computing science and engineering 2024年第3期18卷 144-151页

作者： Kang, Dayeong Lee, Jiyeon School of Computer Science and Engineering Kyungpook National University Daegu Korea Republic of

Substitution ciphers—where characters are systematically replaced with different ones—have represented a vital tool in safeguarding sensitive information for centuries, eventually laying the foundation for modern encryption techniques. Despite their large key space, substitution ciphers are susceptible to frequency analysis that leverages common English letter patterns. While existing research has suggested certain methods that can improve decryption accuracy using ngram frequencies, these methods face difficulties when used with short ciphertexts due to incomplete letter distribution representation. The present study examines the limitations of current frequency analysis in decrypting short ciphertexts, with the results revealing that deterministic bigram approaches can reduce accuracy in certain cases. To address this shortcoming, we introduce a novel algorithm that uses randomized index selection based on letter distribution to generate multiple candidate keys. We also present a word-level key guessing method using these candidates that maps prominent English words to uncover a secret key. The results of tests with 200 ciphertexts of varying lengths showed an average decryption accuracy of 84.1% for 200-character ciphertexts, an improvement of 147.1% over existing methods. In experiments without dictionary-based decryption, an accuracy of 77.6% was achieved with a decryption time of approximately 0.27 seconds, which is a reasonable completion time. Altogether, these results highlight the efficiency and practicality of our approach for decrypting short ciphertexts. © 2024. The Korean Institute of Information Scientists and Engineers

关键词： Ciphertext

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Enhancing IoT intrusion detection through machine learning with AN-SFS: a novel approach to high performing adaptive feature selection

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Discover Internet of Things 2024年第1期4卷 16页

作者： Walling, Supongmen Lodh, Sibesh Department of Computer Science and Engineering National Institute of Technology Nagaland Dimapur India

The vast volume of redundant and irrelevant network traffic data poses significant hurdles for intrusion detection. Effective feature selection is crucial for eliminating irrelevant information. Presently, most filtering and embedded methods rely on fixed thresholds or ratios, necessitating prior knowledge. Conversely, wrapper methods are computationally intensive, and individual feature selection methods may introduce biases in evaluation. To address these challenges, this study introduces Adaptive Neighborhood based Feature Selection (AN-SFS), a dynamic feature selection approach that adapts to local statistical properties of the data. Unlike traditional methods, AN-SFS adjusts its threshold based on the characteristics of the current feature subset and incorporates statistical measures of neighboring features, capturing subtle relationships and dependencies. This adaptability enables AN-SFS to achieve robust and effective feature selection outcomes. Using NSL-KDD and UNSW-NB15 datasets, our model demonstrates superiority over conventional ML classifiers in detection rate, precision, and recall, achieving outstanding accuracy rates of 99.3% on NSL-KDD and 97.5% on UNSW-NB15, significantly outperforming contemporary methods. To further demonstrate the effectiveness of our feature selection approach, we conducted a series of comparisons with other statistical methods, including ANOVA, Pearson correlation, and the chi-square test. In each comparison, our approach consistently outperformed these alternatives, underscoring its superior efficacy. © The Author(s) 2024.

关键词： Network intrusion

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S+E+O-band luminescence and enhancement in Nd3+/Tm3+/Ce3+ doped tellurite glass

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Journal of the Optical Society of America B: Optical Physics 2024年第6期41卷 1432-1442页

作者： Zhou, Dexing Wang, Shuhong Zhao, Dongyi Zhu, Liqiao Zhou, Yaxun Faculty of Electrical Engineering and Computer Science Ningbo University Zhejiang315211 China

A new, to our knowledge, doped combination of Nd3+, Tm3+, and Ce3+ ions was developed in tellurite glass with a fundamental composition of TeO2-ZnO-WO3-Bi2O3, and the structural, thermal, and especially near-infrared (NIR) luminescent properties in O-, E-, and S-bands were explored. The XRD pattern confirmed the amorphous nature of synthesized tellurite glass, the Raman spectrum disclosed different structural units that make up the glass network, the DSC curve confirmed good host thermal stability, while XPS measurement revealed that cerium mainly exists in the trivalent form in a glass network. Upon being pumped with an 808 nm laser, a 1.34 µm band fluorescence from Nd3+ : 4F3/2 level → 4I13/2 level transition and a 1.46 µm band fluorescence from Tm3+ : 3H4 level → 3F4 level transition were observed, which in turn formed a wide luminescence band from 1260 to 1560 nm in the tellurite glass containing both Nd3+ and Tm3+ ions owing to their spectral overlapping. The measured full width at half maximum (FWHM) for the aforementioned wideband luminescence was about 203 nm with Nd2O3 and Tm2O3 concentrations of 0.015 mol. % and 0.5 mol. %, respectively, and exhibited a further ∼60% increase in peak intensity after the incorporation of 0.4 mol. % amount of CeO2, which arises from the phenomena of cross relaxation and energy transfer among the three trivalent rare earth ions. The experimental evidence presented in this work shows that tellurite glass containing Nd3+, Tm3+, and Ce3+ has potential as an active material for S-, E-, and O-band wideband luminescence. © 2024 Optica Publishing Group.

关键词： II-VI semiconductors

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Exploring the Effectiveness of OKRs in Enhancing Company Objectives: A Comparative Study

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IEEE engineering Management Review 2025年第2期53卷 110-121页

作者： Criado, Juan M. Gutierrez, Gema Garzas, Javier Cano, Emilio L. De Lena, M. Teresa Gonzalez Moguerza, Javier M. School of Engineering and Computer Science Statistic and Computing Department Mostoles Madrid28933 Spain

The use of management by objectives (MBOs) methodologies, particularly the objectives and key results (OKRs) framework, has gained widespread attention in recent years as a means of improving organizational performance. This article aims to show that OKRs, a key MBO method, can enhance company performance and employee satisfaction. We conducted a comparative and retroactive analysis of MBO methods used in the 20th and 21st centuries, and surveyed 238 people in nine countries and various companies and positions. We analyzed the results of the survey and compared the variables associated with best practices in the OKR methodology. Our study examined a diverse range of companies, positions, qualifications, and ages, which provides an analysis of whether or not OKRs are being used in the organization and a comprehensive understanding of how OKRs can be implemented across in different contexts. By identifying the best variables associated with optimal performance and employee satisfaction, this article provides practical guidance for organizations seeking to adopt OKR methodology. Furthermore, this article can contribute to the development of best practices in the area of management by objectives, particularly in the context of OKRs, leading to more effective and efficient goal-setting practices in organizations. © 2024 IEEE.

关键词： Object recognition

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Dimensionality reduction using neural networks for lattice-based cryptographic keys

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International Journal of computers and Applications 2024年第10期46卷 889-910页

作者： Wahlang, Roisan Chandrasekaran, K. Computer Science and Engineering Department National Institute of Technology Karnataka Surathkal India

Post Quantum Cryptography has received an increasing amount of active research. This has been made prominent by the ever-growing field of quantum computing which poses a formidable threat to the modern cybersecurity landscape. Recently, post quantum schemes have been standardized for adoption into existing security services and protocols. In comparison to contemporary cryptographic schemes, these approaches are lagging behind in terms of performance with regards to functional speed and package sizes. A study into the various applications of neural networks used in classical and post quantum cryptographic schemes has been explored to demonstrate their different possible applications within the various fields of cybersecurity. The main contribution of this work is to investigate the feasibility of dimensionality reduction using the autoencoder neural network on lattice-based keys generated by the Kyber key encapsulation mechanism scheme. Moreover, this work also presents a comparative analysis of the different implemented autoencoder models to showcase their relative performance. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Dimensionality reduction

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High utility itemsets mining based on hybrid harris hawk optimization and beluga whale optimization algorithms

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 7567-7602页

作者： Gao, Zhihui Han, Meng Liu, Shujuan Li, Ang Mu, Dongliang School of Computer Science and Engineering North Minzu University Ningxia Yinchuan China

The commonly used high utility itemsets mining method for massive data is the intelligent optimization algorithm. In this paper, the WHO (Whale-Hawk Optimization) algorithm is proposed by integrating the harris hawk optimization (HHO) algorithm with the beluga whale optimization (BWO) algorithm. Additionally, a whale initialization strategy based on good point set is proposed. This strategy helps to guide the search in the initial phase and increase the diversity of the population, which in turn improve the convergence speed and algorithm performance. By applying this improved algorithm to the field of high utility itemsets mining, it provides new solutions to optimization problems and data mining problems. To evaluate the performance of the proposed WHO, a large number of experiments are conducted on six datasets, chess, connect, mushroom, accidents, foodmart, and retail, in terms of convergence, recall rates, and runtime. The experimental results show that the convergence of the proposed WHO is optimal in five datasets and has the shortest runtime in all datasets. Compared to PSO, AF, BA, and GA, the average recall rate in the six datasets increased by 32.13%, 49.95%, 12.15%, and 16.24%, respectively. © 2024 - IOS Press. All rights reserved.

关键词： Particle swarm optimization (PSO)

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Farm-Level Smart Crop Recommendation Framework Using Machine Learning

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Annals of Data science 2025年第1期12卷 117-140页

作者： Bhola, Amit Kumar, Prabhat Department of Computer Science and Engineering National Institute of Technology Patna Bihar Patna India

Agriculture is the primary source of food, fuel, and raw materials and is vital to any country’s economy. Farmers, the backbone of agriculture, primarily rely on instinct to determine what crops to plant in any given season. They are comfortable following customary farming practices and standards and are oblivious to the fact that crop yield is highly dependent on current environmental and soil conditions. Crop recommendations involve multifaceted factors such as weather, soil quality, crop production, market demand, and prices, making it crucial for farmers to make well-informed decisions. An improper or imprudent crop recommendation can affect them, their families, and the entire agricultural sector. Modern technologies like artificial intelligence, machine learning, and data science have emerged as efficient solutions to combat issues like declining crop production and lower profits. This research proposes a Smart Crop Recommendation framework that leverages machine learning to empower farmers to make informed decisions about optimal crop selection. The framework consists of two phases: crop filtration and yield prediction. Crops are filtered in the first phase using an artificial neural network based on local input parameters. The second phase estimates yield for filtered crops, considering the season, farm area, and location data. The final recommendation provides farmers with crops aimed at maximizing profit. The remarkable 99.10% accuracy of the framework is demonstrated through experimentation using artificial neural networks and the 0.99 R2 error metric for the random forest. The uniqueness of this framework lies in its distinctive focus on the farm level and its consideration of the challenges and various agricultural features that change over time. The experimental results affirm the effectiveness of the framework, and its lightweight nature enhances its practicality, making it an efficient real-time recommendation solution. © The Author(s), under exclusi

关键词： Crops

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Deep Reinforcement Learning for Task Offloading and Resource Allocation in UAV Cluster-Assisted Mobile Edge Computing

IEEE Journal on Miniaturization for Air and Space Systems

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IEEE Journal on Miniaturization for Air and Space Systems 2024年第2期6卷 92-102页

作者： Hu, Zhijuan Liu, Shuangyu Zhou, Dongsheng Xu, Fei Ma, Jiajun Ning, Xin Xi'an Technological University School Of Computer Science And Engineering Xi'an710021 China

The combination of mobile edge computing (MEC) and unmanned aerial vehicles (UAVs) has important implications for the future development of the Internet of Things (IoT). Additional computing power and extensive network coverage enable users to experience better quality of service even when terrestrial base stations (BSs) scarce or destroyed. In this paper, computational offloading and resource allocation for a UAV cluster-assisted MEC system are investigated. The cluster consists of a mobile UAV as the cluster head (UCH) and multiple fixed-position UAVs as cluster members (UCMs), where the UCH offloads the computational tasks generated by BS and assigns them to the UCM for collaborative processing. Since the positions of user equipment (UE) and UAV, as well as the speed and angle of UCH flight, are highly continuous, we construct a Markov Decision Process (MDP) and propose an offloading algorithm that combines a deep deterministic policy gradient algorithm with a Priority Experience Replay mechanism (PER-DDPG) in order to jointly optimise the user association and UE task offloading rate to minimise the system cost. Simulation results show that compared with the computational unloading algorithms based on actor-critical (AC), Deep Q Network (DQN), and Deep Deterministic Policy Gradient (DDPG), respectively, the proposed PER-DDPG algorithm has good convergence and robustness, and can obtain an optimal unloading strategy with low latency and low power consumption. © 2019 IEEE.

关键词： Quality of service

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