Abstract In this paper, a searchable encryption scheme for cloud data is proposed to address the limitations of existing schemes, which suffer from inefficient index construction and search process, as well as a lack of support for dynamic updates or the complexity and inefficiency of update operations. Firstly, the efficiency and flexibility of index construction are improved by designing a reverse index based on B+ trees, coupled with Bloom filters. Vector matching ideas are employed to enhance the efficiency of searching encrypted cloud data. Secondly, leveraging the bitmap index, a combination design of forward and reverse indices is utilized to improve update efficiency, concealing update types to protect access patterns. Finally, the security analysis and comparative experimental results collectively demonstrate the feasibility, efficiency, and security of the proposed scheme, verifying its potential in practical applications.
XIAO Chaoen, LI Zifan, ZHANG Lei, WANG Jianxin, QIAN Siyuan
In differential analysis-based cryptographic attacks, Bayesian optimization is typically used to verify whether the partially decrypted data exhibit differential characteristics. Currently, the primary approach involves training a differential distinguisher using deep learning techniques. However, this method has a notable limitation in that, as the number of encryption rounds increases, the accuracy of the differential characteristics decreases linearly. Therefore, a new differential characteristic discrimination method is proposed based on the attention mechanism and side-channel analysis. Using the difference relationship between multiple rounds of the ciphertext, a difference partition for the SPECK32/64 algorithm is trained based on the transformer. In a key recovery attack, a novel scheme is designed based on the previous ciphertext treatment to distinguish the most influential features of the ciphertext. In the key recovery attack of the SPECK32/64 algorithm, 2<sup>6</sup> selected ciphertext pairs are used. Using the 20th round ciphertext pairs, the 65 536 candidate keys of the 22nd round can be screened within 17 on average, and the key recovery attack of the last two wheels can be completed. The experimental results show that this method achieves a success rate of 90%, effectively addressing the challenge of recognizing ciphertext differential features caused by an increase in the number of encryption rounds.
Nishat Raihan, Mohammed Latif Siddiq, Joanna C. S. Santos
et al.
Large language models (LLMs) are becoming increasingly better at a wide range of Natural Language Processing tasks (NLP), such as text generation and understanding. Recently, these models have extended their capabilities to coding tasks, bridging the gap between natural languages (NL) and programming languages (PL). Foundational models such as the Generative Pre-trained Transformer (GPT) and LLaMA series have set strong baseline performances in various NL and PL tasks. Additionally, several models have been fine-tuned specifically for code generation, showing significant improvements in code-related applications. Both foundational and fine-tuned models are increasingly used in education, helping students write, debug, and understand code. We present a comprehensive systematic literature review to examine the impact of LLMs in computer science and computer engineering education. We analyze their effectiveness in enhancing the learning experience, supporting personalized education, and aiding educators in curriculum development. We address five research questions to uncover insights into how LLMs contribute to educational outcomes, identify challenges, and suggest directions for future research.
Sometimes only some digits of a numerical product or some terms of a polynomial or series product are required. Frequently these constitute the most significant or least significant part of the value, for example when computing initial values or refinement steps in iterative approximation schemes. Other situations require the middle portion. In this paper we provide algorithms for the general problem of computing a given span of coefficients within a product, that is the terms within a range of degrees for univariate polynomials or range digits of an integer. This generalizes the "middle product" concept of Hanrot, Quercia and Zimmerman. We are primarily interested in problems of modest size where constant speed up factors can improve overall system performance, and therefore focus the discussion on classical and Karatsuba multiplication and how methods may be combined.
Marco Trofa, Andrea Pietro Reverberi, Marco Vocciante
Nanoparticles (NPs) are relevant in several industrial applications due to their peculiar properties with respect to their bulk precursor material. Hence, there is a growing need to develop novel technical solutions to synthetize such NPs by simple, eco-friendly, and cost-effective processes. In this regard, the authors have recently proposed a strategy for the safe and sustainable production of NPs involving a mechanical refining using magnetic agitation in wet-operating stirred media, which minimizes the NPs air dispersion and improves the control over the final product specifics. However, the magnetic agitation poses heavy limits of applicability in the case of synthesis of ferromagnetic NPs. In the present contribution, an alternative device configuration developed to overcome this limitation is investigated though a numerical approach. Discrete element method (DEM) simulations are performed to model the grinding and primary particles collisions and to clarify the effect of the parameters involved in both process setup and operation. The results are reported in terms of frequency and velocity of collision and compared to those of the standard device configuration to derive useful information about the functioning and capabilities of the novel system.
Chemical engineering, Computer engineering. Computer hardware
Rajesh Natarajan, Santosh Reddy P, Subash Chandra Bose
et al.
In the commercial and industrial sectors, automatic robotic control mechanisms, which include robots, end effectors, and anchors containing components, are often utilized to enhance service quality. Robotic systems must be installed in manufacturing lines for a variety of industrial purposes, which also increases the risk of a robot, end controller, and/or device malfunction. According to its automated regulation, this may hurt people and other items in the workplace in addition to resulting in a reduction in quality operation. With today's advanced systems and technology, security and stability are crucial. Hence, the system is equipped with fault management abilities for the identification of developing defects and assessment of their influence on the system's activity in the upcoming utilizing fault diagnostic methodologies. To provide adaptive control, fault detection, and state estimation for robotic automated systems intended to function dependably in complicated contexts, efficient techniques are described in this study. This paper proposed a fault detection and state estimation using Accelerated Gradient Descent based support vector machine (AGDSVM) and gaussian filter (GF) in automatic control systems. The Proposed system is called (AGDSVM + GF). The proposed system is evaluated with the following metrics accuracy, fault detection rate, state estimation rate, computation time, error rate, and energy consumption. The result shows that the proposed system is effective in fault detection and state estimation and provides intelligent control automatic control.
Suchita Pati, Shaizeen Aga, Mahzabeen Islam
et al.
Scaling neural network models has delivered dramatic quality gains across ML problems. However, this scaling has increased the reliance on efficient distributed training techniques. Accordingly, as with other distributed computing scenarios, it is important to understand how will compute and communication scale relative to one another as models scale and hardware evolves? A careful study which answers this question can better guide the design of future systems which can efficiently train future large models. Accordingly, this work provides a comprehensive multi-axial (algorithmic, empirical, hardware evolution) analysis of compute vs. communication (Comp-vs.-Comm) scaling for future Transformer models on future hardware. First, our algorithmic analysis shows that compute generally enjoys an edge over communication as models scale. However, since memory capacity scales slower than compute, these trends are being stressed. Next, we quantify this edge by empirically studying how Comp-vs.-Comm scales for future models on future hardware. To avoid profiling numerous Transformer models across many setups, we extract execution regions and project costs using operator models. This allows a spectrum (hundreds) of future model/hardware scenarios to be accurately studied ($<$15% error), and reduces profiling costs by 2100$\times$. Our experiments show that communication will be a significant portion (40-75%) of runtime as models and hardware evolve. Moreover, communication which is hidden by overlapped computation in today's models often cannot be hidden in future, larger models. Overall, this work highlights the increasingly large role communication will play as models scale and discusses techniques and upcoming technologies that can help address it.
Vasileios Leon, Muhammad Abdullah Hanif, Giorgos Armeniakos
et al.
The rapid growth of demanding applications in domains applying multimedia processing and machine learning has marked a new era for edge and cloud computing. These applications involve massive data and compute-intensive tasks, and thus, typical computing paradigms in embedded systems and data centers are stressed to meet the worldwide demand for high performance. Concurrently, over the last 15 years, the semiconductor industry has established power efficiency as a first-class design concern. As a result, the community of computing systems is forced to find alternative design approaches to facilitate high-performance and power-efficient computing. Among the examined solutions, Approximate Computing has attracted an ever-increasing interest, which has resulted in novel approximation techniques for all the layers of the traditional computing stack. More specifically, during the last decade, a plethora of approximation techniques in software (programs, frameworks, compilers, runtimes, languages), hardware (circuits, accelerators), and architectures (processors, memories) have been proposed in the literature. The current article is Part I of a comprehensive survey on Approximate Computing. It reviews its motivation, terminology and principles, as well it classifies the state-of-the-art software & hardware approximation techniques, presents their technical details, and reports a comparative quantitative analysis.
The digitization of engineering drawings is crucial for efficient reuse, distribution, and archiving. Existing computer vision approaches for digitizing engineering drawings typically assume the input drawings have high quality. However, in reality, engineering drawings are often blurred and distorted due to improper scanning, storage, and transmission, which may jeopardize the effectiveness of existing approaches. This paper focuses on restoring and recognizing low-quality engineering drawings, where an end-to-end framework is proposed to improve the quality of the drawings and identify the graphical symbols on them. The framework uses K-means clustering to classify different engineering drawing patches into simple and complex texture patches based on their gray level co-occurrence matrix statistics. Computer vision operations and a modified Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) model are then used to improve the quality of the two types of patches, respectively. A modified Faster Region-based Convolutional Neural Network (Faster R-CNN) model is used to recognize the quality-enhanced graphical symbols. Additionally, a multi-stage task-driven collaborative learning strategy is proposed to train the modified ESRGAN and Faster R-CNN models to improve the resolution of engineering drawings in the direction that facilitates graphical symbol recognition, rather than human visual perception. A synthetic data generation method is also proposed to construct quality-degraded samples for training the framework. Experiments on real-world electrical diagrams show that the proposed framework achieves an accuracy of 98.98% and a recall of 99.33%, demonstrating its superiority over previous approaches. Moreover, the framework is integrated into a widely-used power system software application to showcase its practicality.
The focus of this paper is on the problems of system identification, process modeling and time series forecasting which can be met during the use of locally recurrent neural networks in heuristic modelling technique. However, the main interest of this paper is to survey the properties of the dynamic neural processor which is developed by the author. Moreover, a comparative study of selected recurrent neural architectures in modeling tasks is given. The results of experiments showed that some processes tend to be chaotic and in some cases it is reasonable to use soft computing models for fault diagnosis and control.
Computer engineering. Computer hardware, Mechanics of engineering. Applied mechanics
Muhammad Azeem Akbar, Arif Ali Khan, Sajjad Mahmood
et al.
Quantum computing (QC) is no longer only a scientific interest but is rapidly becoming an industrially available technology that can potentially tackle the limitations of classical computing. Over the last few years, major technology giants have invested in developing hardware and programming frameworks to develop quantum-specific applications. QC hardware technologies are gaining momentum, however, operationalizing the QC technologies trigger the need for software-intensive methodologies, techniques, processes, tools, roles, and responsibilities for developing industrial-centric quantum software applications. This paper presents the vision of the quantum software engineering (QSE) life cycle consisting of quantum requirements engineering, quantum software design, quantum software implementation, quantum software testing, and quantum software maintenance. This paper particularly calls for joint contributions of software engineering research and industrial community to present real-world solutions to support the entire quantum software development activities. The proposed vision facilitates the researchers and practitioners to propose new processes, reference architectures, novel tools, and practices to leverage quantum computers and develop emerging and next generations of quantum software.
We design deep neural networks (DNNs) and corresponding networks' splittings to distribute DNNs' workload to camera sensors and a centralized aggregator on head mounted devices to meet system performance targets in inference accuracy and latency under the given hardware resource constraints. To achieve an optimal balance among computation, communication, and performance, a split-aware neural architecture search framework, SplitNets, is introduced to conduct model designing, splitting, and communication reduction simultaneously. We further extend the framework to multi-view systems for learning to fuse inputs from multiple camera sensors with optimal performance and systemic efficiency. We validate SplitNets for single-view system on ImageNet as well as multi-view system on 3D classification, and show that the SplitNets framework achieves state-of-the-art (SOTA) performance and system latency compared with existing approaches.
Piernicola Masella, Giulia Angeloni, Lorenzo Guerrini
et al.
The overall quality of extra virgin olive oil (EVOO) strictly relates to the evolution of its oxidative state, which in turn depends on the amount of the oxygen availability during the product life, starting with the olive fruit milling. The issue of oil oxygenation during processing has been poorly studied. The few available literature assesses the relative contribution of three main processing steps (paste malaxation, decanter centrifugation, and vertical centrifugation) to the final dissolved oxygen concentration and the consequences on the oil quality decay during storage. Nevertheless, until now information about the contribution of the devices used to moves materials during processing, i.e. screw conveyor and pumps to move olive, olive paste and oil, to the amount of dissolved oxygen and in broader terms on the oil quality, are lacking. It can be reasonably assumed that the intact drupes handling during leaf-removal and washing before crushing, had a negligible effect on the final oil dissolved oxygen content, whereas from crusher to the decanter centrifuge by the malaxer, where olive paste handling occurs, a noticeable effect on the future oil characteristics could occur. The standard and widespread device used to move olive paste during processing, essentially from the crusher to the malaxer and from the malaxer to the decanter centrifuge, is the progressive cavity pump, commonly named mono-pump. Notices of other pumping devices used or proved in EVOO mills are missing. In the present work a peristaltic pump (roller pump) has been tested in comparison to the conventional mono-pump in a continuous centrifugal extraction plant. Specifically, the two pumps were alternatively used to feed the decanter centrifuge by empting the malaxation chambers. The oxygenation effect was assessed in terms of dissolved oxygen amount (DOA) in the produced EVOO, which were also compared for the main qualitative traits such as commercial parameters, phenolic and volatile profiles and phthalates occurrence being the peristaltic pump equipped with a phthalates-free tube. Basically, the qualitative effect of the two pumps significantly differs for the DOA with about 10% saving for the peristaltic pump. The latter also gives significantly (p
Chemical engineering, Computer engineering. Computer hardware
Julian Mack, Rossella Arcucci, Miguel Molina-Solana
et al.
We propose a new 'Bi-Reduced Space' approach to solving 3D Variational Data Assimilation using Convolutional Autoencoders. We prove that our approach has the same solution as previous methods but has significantly lower computational complexity; in other words, we reduce the computational cost without affecting the data assimilation accuracy. We tested the new method with data from a real-world application: a pollution model of a site in Elephant and Castle, London and found that we could reduce the size of the background covariance matrix representation by O(10^3) and, at the same time, increase our data assimilation accuracy with respect to existing reduced space methods.
We initiate the study of computational complexity of graph coverings, aka locally bijective graph homomorphisms, for {\em graphs with semi-edges}. The notion of graph covering is a discretization of coverings between surfaces or topological spaces, a notion well known and deeply studied in classical topology. Graph covers have found applications in discrete mathematics for constructing highly symmetric graphs, and in computer science in the theory of local computations. In 1991, Abello, Fellows, and Stillwell asked for a classification of the computational complexity of deciding if an input graph covers a fixed target graph, in the ordinary setting (of graphs with only edges). Although many general results are known, the full classification is still open. In spite of that, we propose to study the more general case of covering graphs composed of normal edges (including multiedges and loops) and so-called semi-edges. Semi-edges are becoming increasingly popular in modern topological graph theory, as well as in mathematical physics. They also naturally occur in the local computation setting, since they are lifted to matchings in the covering graph. We show some solvable cases and, in particular, completely characterize the complexity of the already very nontrivial problem of covering one- and two-vertex (multi)graphs with semi-edges. Our NP-hardness results are proven for simple input graphs, and in the case of regular two-vertex target graphs, even for bipartite ones. We remark that our new characterization results also strengthen previously known results for covering graphs without semi-edges, and they in turn apply to an infinite class of simple target graphs with at most two vertices of degree more than two. Some of the results are moreover proven in a more general setting (e.g., finding $k$-tuples of pairwise disjoint perfect matchings in regular graphs).
Quantum computing is a nascent technology, which is advancing rapidly. There is a long history of research into using computers for music. Nowadays computers are absolutely essential for the music economy. Thus, it is very likely that quantum computers will impact the music industry in time to come. This chapter lays the foundations of the new field of 'Quantum Computer Music'. It begins with an introduction to algorithmic computer music and methods to program computers to generate music, such as Markov chains and random walks. Then, it presents quantum computing versions of those methods. The discussions are supported by detailed explanations of quantum computing concepts and walk-through examples. A bespoke generative music algorithm is presented, the Basak-Miranda algorithm, which leverages a property of quantum mechanics known as constructive and destructive interference to operate a musical Markov chain. An Appendix introducing the fundamentals of quantum computing deemed necessary to understand the chapter and a link to access Jupyter Notebooks with examples are also provided.
Context: The use of standards is considered a vital part of any engineering discipline. So one could expect that standards play an important role in Requirements Engineering (RE) as well. However, little is known about the actual knowledge and use of RE-related standards in industry. Objective: In this article, we investigate to which extent standards and related artifacts such as templates or guidelines are known and used by RE practitioners. Method: To this end, we have conducted a questionnaire-based online survey. We could analyze the replies from 90 RE practitioners using a combination of closed and open-text questions. Results: Our results indicate that the knowledge and use of standards and related artifacts in RE is less widespread than one might expect from an engineering perspective. For example, about 47% of the respondents working as requirements engineers or business analysts do not know the core standard in RE, ISO/IEC/IEEE 29148. Participants in our study mostly use standards by personal decision rather than being imposed by their respective company, customer, or regulator. Beyond insufficient knowledge, we also found cultural and organizational factors impeding the widespread adoption of standards in RE. Conclusions: Overall, our results provide empirically informed insights into the actual use of standards and related artifacts in RE practice and - indirectly - about the value that the current standards create for RE practitioners.