Minimal Memory Research Articles

Efficient generation of grids and traversal graphs in compositional spaces towards exploration and path planning

AbstractDiverse disciplines across science and engineering deal with problems related to compositions, which exist in non-Euclidean simplex spaces, rendering many standard tools inaccurate or inefficient. This work explores such spaces conceptually in the context of materials discovery, quantifies their computational feasibility, and implements several essential methods specific to simplex spaces through a new high-performance open-source library . Most significantly, we derive and implement an algorithm for constructing a novel n-dimensional simplex graph data structure, containing all discretized compositions and possible neighbor-to-neighbor transitions. Critically, no distance or neighborhood calculations are performed, instead leveraging pure combinatorics and order in procedurally generated simplex grids, keeping the algorithm $${\mathcal{O}}(N)$$ O ( N ) , with minimal memory, enabling rapid construction of graphs with billions of transitions in seconds. Additionally, we demonstrate how such graph representations can be combined to homogeneously express complex path-planning problems, while facilitating efficient deployment of existing high-performance gradient descent, graph traversal, and other optimization algorithms.

A Supervised Ml Algorithm for Detecting and Predicting Fraud Credit Card Transactions

A Credit card fraud presents an escalating threat in today's digital economy, leading to significant financial losses for consumers, businesses, and financial institutions. Traditional detection methods are increasingly inadequate due to the rise in online transactions and the evolving complexity of fraudulent activities. This research aims to create a reliable supervised machine learning model designed to detect and predict fraudulent credit card transactions in real-time. Our approach leverages advanced algorithms and extensive datasets to enhance transaction security, thereby mitigating financial risks. Current systems, like FICO's Fraud Detection System (FDS), utilize a range of supervised learning techniques, including Decision Trees, Random Forests, and Neural Networks. However, they face limitations, including data quality issues, high false positive rates, and the need for continuous retraining to adapt to evolving fraud patterns. To address these challenges, we propose an innovative anomaly detection method based on the Isolation Forest algorithm. Unlike traditional methods that rely on distance and density measures, Isolation Forests isolate anomalies by randomly selecting features and split values, making it more efficient in identifying fraudulent transactions, especially in imbalanced datasets. The proposed system boasts low linear time complexity and minimal memory requirements, enabling the construction of high-performing models even with large datasets. The research demonstrates that the Isolation Forest approach significantly outperforms traditional models in detecting credit card fraud, offering a promising solution for enhancing security in the financial ecosystem.

Exponential Learning Advantages with Conjugate States and Minimal Quantum Memory

The ability of quantum computers to directly manipulate and analyze quantum states stored in quantum memory allows them to learn about aspects of our physical world that would otherwise be invisible given a modest number of measurements. Here we investigate a new learning resource, which could be available to quantum computers in the future—measurements on the unknown state accompanied by its complex conjugate ρ⊗ρ∗. For a certain shadow tomography task, we surprisingly find that measurements on only copies of ρ⊗ρ∗ can be exponentially more powerful than measurements on ρ⊗K, even for large K. This expands the class of provable exponential advantages using only a constant overhead quantum memory, or minimal quantum memory, and we provide a number of examples where the state ρ∗ is naturally available in both computational and physical applications. In addition, we precisely quantify the power of classical shadows on single copies under a generalized Clifford ensemble and give a class of quantities that can be efficiently learned. The learning task we study in both the single copy and quantum memory settings is physically natural and corresponds to real-space observables with a limit of bosonic modes, where it achieves an exponential improvement in detecting certain signals under a noisy background. We quantify a new and powerful resource in quantum learning, and we believe the advantage may find applications in improving quantum simulation, learning from quantum sensors, and uncovering new physical phenomena. Published by the American Physical Society 2024

3D Lightweight Cryptosystem Design For IoT Applications Based On Composite S-Box

The security of the Internet of Things (IoT) depends on strong cryptographic functions that require extensive computation and resources. The security of Internet of Things (IoT) relies on the use of strong cryptographic functions that demand extensive computation and resources. Therefore, the selection of a cryptographic system is influenced by the computational and communicational capabilities of IoT devices, including their energy requirements, memory constraints, and execution time. This paper aims to develop a lightweight and secure encryption algorithm for IoT applications, focusing on advancing cryptanalysis techniques. We propose a new lightweight algorithm named enhanced 3D RECTANGLE, designed to deliver robust security for IoT applications and optimized for cell phones with minimal memory usage, low power consumption, and efficient performance. The RECTANGLE block cipher was chosen for this research due to its high efficiency and speed relative to other lightweight algorithms, despite some security issues. The proposed enhanced 3D RECTANGLE algorithm improves confusion and diffusion properties through a new 3D array block rotation method for 4x4 plaintext, based on a 128-bit key and 16 rounds. Additionally, we designed a 4x4 composite S-Box with a Galois field pipelining structure, offering a more advanced solution compared to the Look-Up Table method. The cryptanalysis, avalanche effect, and bit error rate tests were conducted to verify the security strength of the proposed algorithm. The proposed algorithm was evaluated against two existing algorithms, RECTANGLE and Extended 3D RECTANGLE. The enhanced 3D RECTANGLE algorithm provides better cryptanalysis, demonstrating a 40% avalanche effect and achieving a bit error rate (BER) of 31%, indicating its greater effectiveness compared to the other two lightweight algorithms.

AGR – 2 The Multi-Dimensional Role of Neuropsychology in Diagnostically Complex Epilepsy Patients: Refining the Surgical Plan and Post-Operative Expectations

Abstract Objective We present a case highlighting the importance of neuropsychological involvement at all phases of a pre-surgical epilepsy work-up, including clinical interview, neuropsychological evaluation, Wada testing, extra-operative electrical stimulation mapping (ESM), and psychoeducation. Method A 24-year-old, right-handed, Caucasian female presented with non-lesional, intractable left-hemisphere localization-related epilepsy characterized by frequent experiences of déjà vu and autonomic sensations. Results Neuropsychological evaluation revealed weaknesses in processing speed and verbal memory implicating left hippocampal involvement. Wada testing suggested minimal contralateral memory support, indicating poor candidacy for resective surgery. The patient underwent prolonged sEEG monitoring to refine a plan for device implantation (i.e., responsive neurostimulation; RNS). No typical events were captured during extended study. Episodic somatosensory changes were captured, which had no electrographic correlate and were presumed to be non-epileptic. ESM evoked the patient’s classic semiology, elucidating nuanced information about her seizure onset zone and network. Barriers to extended monitoring and consistent, reliable engagement during ESM included depression, anxiety, and trauma; considerable neuropsychologist-led emotional support was required. Typical seizure semiology was elicited during stimulation of entorhinal cortices, posterior and anterior hippocampi, amygdala, cingulate and insula. RNS placement included a depth electrode targeting the medial structures (via a posterior approach) with a sub-temporal strip. Conclusions Involvement of neocortical and mesial temporal structures, coupled with a clear neuropsychiatric component, were believed to be poly-etiological factors to the patient’s presentation. Neuropsychological input at all phases of pre-surgical assessment was instrumental in systematically identifying and addressing her complex needs. Psychotherapeutic intervention was suggested to be equally as important to clinical, long-term patient outcome.

A novel weight index-based uniform partition technique of multi-dimensional probability space for structural uncertainty quantification

Accurately and efficiently achieving the uncertainty quantification of engineering structures is a challenging issue. The direct probability integral method (DPIM) provides an effective pathway to address this issue. However, the key partition technique via Voronoi cell of DPIM requires a prohibitive computational burden for multi-dimensional probability space. Moreover, due to the distributed nonuniformity of representative points, the accuracy of DPIM with the partition technique via Voronoi cell (DPIM-Voronoi) for obtaining the response probability density function (PDF) of structures with multi-dimensional probability space still needs to be improved. To this end, a novel weight index-based uniform partition technique is proposed in this study. This technique can generate uniformly distributed representative points and calculate their assigned probabilities using the weight indexes of representative regions. This feature ensures that the representative points can fill the probability space in a highly uniform manner, and avoid the resource-consuming calculation process of assigned probability by Monte Carlo simulation in the original partition technique via Voronoi cell. Based on the proposed technique, DPIM with a Weight index-based Uniform partition technique (DPIM-WU) is developed. Compared to DPIM-Voronoi, the advantages of DPIM-WU include: (1) improving the computational accuracy of response PDF for structures with multi-dimensional probability space, especially in the tail region, leading to improved accuracy of the dynamic reliability; (2) remarkably reducing the computational cost, with minimal computer memory required for the partition process of multi-dimensional probability space; (3) enhancing the robustness to the number of representative points. These advantages are verified through the stochastic response and dynamic reliability analyses of four typical examples, including the 5-story buildings, dry friction system, cylinder structure, and adjacent buildings with pounding motion. Notably, in the stochastic pounding response analysis of adjacent buildings, a stochastic P-bifurcation occurs as the coefficient of variation of the structural parameters decreases.

APPS: Authentication-enabled privacy protection scheme for secure data transfer in Internet of Things

The Internet of Things (IoT) is an emerging field that encompasses several heterogeneous devices and smart objects that are integrated with the network. In open platforms, these objects are deployed to present advanced services in numerous applications. Innumerable security-sensitive data is generated by the IoT device and therefore, the security of these devices is an important task. This work formulates a secure data transfer technique in IoT, named Authentication enabled Privacy Protection (APPS) scheme for resource-constrained IoT devices. The proposed scheme demonstrates resilience against various attacks; such as resisting reply attacks, device anonymity, untracebility, session key establishment, quantum attacks and resisting MITM attacks. For the privacy protection scheme, the secured data transfer is initiated between the entities, like IoT devices, servers, and registration centers, by using various phases, namely registration phase, key generation phase, data encryption, authentication, verification, and data retrieval phase. Here, a mathematical model is designed for protecting data privacy using hashing, encryption, secret keys, etc. Finally, performance of proposed APPS model is analyzed; wherein the outcomes reveal that the proposed APPS model attained the maximum detection rate of 0.85, minimal memory usage of 0.497MB, and minimal computational time of 112. 79 sec and minimal turnaround time 131.91 sec.

MementoHash: A Stateful, Minimal Memory, Best Performing Consistent Hash Algorithm

MementoHash: A Stateful, Minimal Memory, Best Performing Consistent Hash Algorithm

REACT: Autonomous intrusion response system for intelligent vehicles

Autonomous and connected vehicles are rapidly evolving, integrating numerous technologies and software. This progress, however, has made them appealing targets for cybersecurity attacks. As the risk of cyber threats escalates with this advancement, the focus is shifting from solely preventing these attacks to also mitigating their impact. Current solutions rely on vehicle security operation centers, where attack information is analyzed before deciding on a response strategy. However, this process can be time-consuming and faces scalability challenges, along with other issues stemming from vehicle connectivity. This paper proposes a dynamic intrusion response system integrated within the vehicle. This system enables the vehicle to respond to a variety of incidents almost instantly, thereby reducing the need for interaction with the vehicle security operation center. The system offers a comprehensive list of potential responses, a methodology for response evaluation, and various response selection methods. The proposed solution was implemented on an embedded platform. Two distinct cyberattack use cases served as the basis for evaluating the system. The evaluation highlights the system’s adaptability, its ability to respond swiftly, its minimal memory footprint, and its capacity for dynamic system parameter adjustments. The proposed solution underscores the necessity and feasibility of incorporating dynamic response mechanisms in smart vehicles. This is a crucial factor in ensuring the safety and resilience of future smart mobility.

Unishyper: A Rust-based unikernel enhancing reliability and efficiency of embedded systems

Unikernels are simple, customizable, efficient, and small in code size, which makes them highly applicable to embedded scenarios. However, most existing unikernels are developed and optimized for cloud computing, and they do not fully meet the requirements of high reliability and platform customization in embedded environments. We propose Unishyper, a reliable and high-performance embedded unikernel in Rust. To support memory isolation between user applications, user code, and kernel code, Unishyper designs the Zone mechanism on top of Intel MPK. Unishyper further proposes a thread-level unwind strategy for safe fault handling while avoiding memory leakage. Finally, Unishyper supports fine-grained customization, seamlessly integrates with the Rust ecosystem, and uses Unilib for function offloading to further reduce image size. Our evaluation results show that Unishyper achieves better performance than peer unikernels on major micro-benchmarks, can effectively stop illegal memory accesses across application boundaries, and has a minimal memory footprint of less than 100 KB.

Czech medical coding assistant based on transformer networks

The International Classification of Diseases (ICD) hierarchical taxonomy is used for so-called clinical coding of medical reports, typically presented in unstructured text. In the Czech Republic, it is currently carried out manually by a so-called clinical coder. However, due to the human factor, this process is error-prone and expensive. The coder needs to be properly trained and spends significant effort on each report, leading to occasional mistakes.The main goal of this paper is to propose and implement a system that serves as an assistant to the coder and automatically predicts diagnosis codes. These predictions are then presented to the coder for approval or correction, aiming to enhance efficiency and accuracy. We consider two classification tasks: main (principal) diagnosis; and all diagnoses. Crucial requirements for the implementation include minimal memory consumption, generality, ease of portability, and sustainability.The main contribution lies in the proposal and evaluation of ICD classification models for the Czech language with relatively few training parameters, allowing swift utilisation on the prevalent computer systems within Czech hospitals and enabling easy retraining or fine-tuning with newly available data. First, we introduce a small transformer-based model for each task followed by the design of a transformer-based “Four-headed” model incorporating four distinct classification heads.This model achieves comparable, sometimes even better results, against four individual models. Moreover this novel model significantly economises memory usage and learning time. We also show that our models achieve comparable results against state-of-the-art English models on the Mimic IV dataset even though our models are significantly smaller.

Self-Knowledge Distillation via Progressive Associative Learning

As a specific form of knowledge distillation (KD), self-knowledge distillation enables a student network to progressively distill its own knowledge without relying on a pretrained, complex teacher network; however, recent studies of self-KD have discovered that additional dark knowledge captured by auxiliary architecture or data augmentation could create better soft targets for enhancing the network but at the cost of significantly more computations and/or parameters. Moreover, most existing self-KD methods extract the soft label as a supervisory signal from individual input samples, which overlooks the knowledge of relationships among categories. Inspired by human associative learning, we propose a simple yet effective self-KD method named associative learning for self-distillation (ALSD), which progressively distills richer knowledge regarding the relationships between categories across independent samples. Specifically, in the process of distillation, the propagation of knowledge is weighted based on the intersample relationship between associated samples generated in different minibatches, which are progressively estimated with the current network. In this way, our ALSD framework achieves knowledge ensembling progressively across multiple samples using a single network, resulting in minimal computational and memory overhead compared to existing ensembling methods. Extensive experiments demonstrate that our ALSD method consistently boosts the classification performance of various architectures on multiple datasets. Notably, ALSD pushes forward the self-KD performance to 80.10% on CIFAR-100, which exceeds the standard backpropagation by 4.81%. Furthermore, we observe that the proposed method shows comparable performance with the state-of-the-art knowledge distillation methods without the pretrained teacher network.

NGMD: next generation malware detection in federated server with deep neural network model for autonomous networks

Distributed denial-of-service (DDoS) attacks persistently proliferate, impacting individuals and Internet Service Providers (ISPs). Deep learning (DL) models are paving the way to address these challenges and the dynamic nature of potential threats. Traditional detection systems, relying on signature-based techniques, are susceptible to next-generation malware. Integrating DL approaches in cloud-edge/federated servers enhances the resilience of these systems. In the Internet of Things (IoT) and autonomous networks, DL, particularly federated learning, has gained prominence for attack detection. Unlike conventional models (centralized and localized DL), federated learning does not require access to users’ private data for attack detection. This approach is gaining much interest in academia and industry due to its deployment on local and global cloud-edge models. Recent advancements in DL enable training a quality cloud-edge model across various users (collaborators) without exchanging personal information. Federated learning, emphasizing privacy preservation at the cloud-edge terminal, holds significant potential for facilitating privacy-aware learning among collaborators. This paper addresses: (1) The deployment of an optimized deep neural network for network traffic classification. (2) The coordination of federated server model parameters with training across devices in IoT domains. A federated flowchart is proposed for training and aggregating local model updates. (3) The generation of a global model at the cloud-edge terminal after multiple rounds between domains and servers. (4) Experimental validation on the BoT-IoT dataset demonstrates that the federated learning model can reliably detect attacks with efficient classification, privacy, and confidentiality. Additionally, it requires minimal memory space for storing training data, resulting in minimal network delay. Consequently, the proposed framework outperforms both centralized and localized DL models, achieving superior performance.

A calculation method for optical properties of yolk shell based on deep learning.

The yolk shell is widely used in optoelectronic devices due to its excellent optical properties. Compared to single metal nanostructures, yolk shells have more controllable degrees of freedom, which may make experiments and simulations more complex. Using neural networks can efficiently simplify the computational process of yolk shell. In our work, the relationship between the size and the absorption efficiency of the yolk-shell structure is established using a backpropagation neural network (BPNN), significantly simplifying the calculation process while ensuring accuracy equivalent to discrete dipole scattering (DDSCAT). The absorption efficiency of the yolk shell was comprehensively described through the forward and reverse prediction processes. In forward prediction, the absorption spectrum of yolk shell is obtained through its size parameter. In reverse prediction, the size parameters of yolk shells are predicted through absorption spectra. A comparison with the traditional DDSCAT demonstrated the high precision prediction capability and fast computation of this method, with minimal memory consumption.

Improving Graph Compression for Efficient Resource-Constrained Graph Analytics

Recent studies have shown the promise of directly processing compressed graphs. However, its benefits have been limited by high peak-memory usage and unbearably long compression time. In this paper, we introduce Laconic, a novel rule-based graph processing solution that overcomes the challenges of restricted memory and impractical compression time faced by existing approaches. Laconic, for the first time, ensures minimal memory overhead during compression and significantly reduces graph sizes, thus reducing peak memory demand during computations. By employing an efficient parallel compression algorithm, Laconic achieves a remarkable reduction in compression time. In our experiments, we compare Laconic with state-of-the-art solutions. The results demonstrate that Laconic outperforms other methods, reducing peak memory consumption by an average of 70% during compression and 66% during computation. Additionally, Laconic reduces rule compression time by an average of 93% compared to traditional rule-based compression, achieving a 2.47× higher compression ratio, and providing a 2.12× performance speedup.

A Lightweight File System Design for Unikernel

Unikernels are specialized operating system (OS) kernels optimized for a single application or service, offering advantages such as rapid boot times, high performance, minimal memory usage, and enhanced security compared to general-purpose OS kernels. Unikernel applications must remain compatible with the runtime environment of general-purpose kernels, either through binary or source compatibility. As a result, many Unikernel projects have prioritized system call compatibility over performance enhancements. In this paper, we explore the design principles of Unikernel file systems and introduce a new file system tailored for Unikernels named ULFS (Ultra Lightweight File System). ULFS provides system call services akin to those of general-purpose OS kernels but achieves superior performance and security with significantly fewer system resources. Specifically, ULFS is developed as a lightweight file system embracing Unikernel design principles. It streamlines system calls, removes unnecessary locks, and omits permission checks for multiple users, utilizing a non-hypervisor architecture. This approach significantly reduces the memory footprint of the file system and enhances performance. Through measurement studies, we assess the performance and memory requirements of various file systems from major Unikernel projects. Our findings demonstrate that ULFS surpasses several existing Unikernel file systems, including Rumpvfs, Ramfs-u, Ramfs-q, 9pfs, and Hcfs.

Efficient Optimization approach in WSN using Multicast Forwarding Scheme in 6LowPAN Environment

The rapidly growing network of devices that are connected known as the Internet of Things, also referred to as IoT, is designed to collect and exchange data. These devices are integrated with sensors, software, and other technologies. An IPv6-based routing system called 6LowPAN was created especially for Internet of Things devices with little resources. However, because multicast packets must be replicated to every multicast group member, the conventional multicast forwarding technique in 6LowPAN may be wasteful. This can result in excessive energy usage and network traffic, particularly in large-scale Internet of Things implementations. An optimization process is presented here using a multicast forwarding scheme. In the proposed forwarding scheme, nodes can send multicast packets up and down at Low Power and Lossy networks over the 6LowPAN environment. Should the sink node be on the root node, it should immediately request that other nodes join the multicast messaging network of things. If otherwise, an ICMPv6 packet will be sent towards the root node in the Destination-oriented Directed-Acyclic Graphs (DODAG) tree, which expands the multicast messages in the first source's interest. In multi-bounce forward straight-line topology, when the DODAG network root is the source of multicast congestion, it exhibits a similar ratio of packet delivery and delay from end to end with a minimal memory overhead. When traffic via multicast originates from the most important rank node in a backward straight-line topology, this paper focuses on the efficiency of the multicast network at 6LowPAN.

Differentially Private Consensus for Second-Order Multiagent Systems With Quantized Communication.

This article considers the differentially private consensus problem of discrete-time second-order multiagent systems with partially measurable states and limited communication channel capacity, where only the integer-value information of agents can be transmitted. To reduce the potential risk of state information disclosure in digital communication, a differentially private consensus algorithm via dynamic encoding-decoding is proposed for the second-order multiagent system to make agents achieve mean-square consensus by transmitting quantized integer values with privacy protection. To deal with the uncertainty of the quantizer saturation, the statistical analysis is given for the boundedness of the input of quantizers. It is shown that the expectation of the minimum memory capacity of quantizers is 2 bits. Finally, some simulation results are given to visualize our conclusions.

Population-Specific Glucose Prediction in Diabetes Care With Transformer-Based Deep Learning on the Edge.

Leveraging continuous glucose monitoring (CGM) systems, real-time blood glucose (BG) forecasting is essential for proactive interventions, playing a crucial role in enhancing the management of type 1 diabetes (T1D) and type 2 diabetes (T2D). However, developing a model generalized to a population and subsequently embedding it within a microchip of a wearable device presents significant technical challenges. Furthermore, the domain of BG prediction in T2D remains under-explored in the literature. In light of this, we propose a population-specific BG prediction model, leveraging the capabilities of the temporal fusion Transformer (TFT) to adjust predictions based on personal demographic data. Then the trained model is embedded within a system-on-chip, integral to our low-power and low-cost customized wearable device. This device seamlessly communicates with CGM systems through Bluetooth and provides timely BG predictions using edge computing. When evaluated on two publicly available clinical datasets with a total of 124 participants with T1D or T2D, the embedded TFT model consistently demonstrated superior performance, achieving the lowest prediction errors when compared with a range of machine learning baseline methods. Executing the TFT model on our wearable device requires minimal memory and power consumption, enabling continuous decision support for more than 51 days on a single Li-Poly battery charge. These findings demonstrate the significant potential of the proposed TFT model and wearable device in enhancing the quality of life for people with diabetes and effectively addressing real-world challenges.

Potential applications of internet of things: A comprehensive analysis

Internet of Things (IoT) is the amalgamation of hardware, like sensors and trackers, which monitor several parameters of the environment or physical objects, and software that processes all the data gathered by hardware. Globally, the IoT market is anticipated to reach 53.8 billion USD by 2025. This enhancing demand is due to its innate ability to automate, which drives several industries to adopt IoT. In addition, minimum memory cost, processing, and storage with an increase in Big Data (BD), cloud, and conjunction of industrial networks and the internet are the added factors for the increase in IoT development. Due to this significance, IoT has applications in numerous areas like medical management, farming, wearable technology, smart energy meters, smart cities, etc. The applications are not limited to the examples mentioned above. Considering this, existing studies have considered different applications and attempted to execute them. As different applications have been focused on by these studies, the present review intends to provide a compilation of potential applications of IoT as considered by conventional research between 2018 and 2022. The study also intends to explore the advantages and disadvantages of different IoT applications (deliberated by conventional studies) through tabular analysis. Further, this review emphasizes IoT’s major key challenges and countermeasures to resolve its security issues. Finally, the study affords recommendations that will assist all IoT experts in bringing IoT products with enhanced security into the market.