Static Set Research Articles

Static Hilbert convex set clustering for web services aggregation

<span>Web services' high levels of duplicate textual structures have caused network bottlenecks and congestion. Clustering and then aggregating similar web services as one compressed message can potentially achieve network traffic reduction. In this paper, a static Hilbert clustering as new model for clustering web services based on convex set similarity is proposed. Mathematically, the proposed model calculates similarity among simple object access protocol (SOAP) messages and then cluster them based on higher similarity values. Next, each cluster is aggregated as a compact message. The experiments have explained the proposed model performance as it has outperformed the convention clustering strategies in both compression ratio and clustering time. The best results have been achievable by the proposed model has reached up to (15) with fixed-length and up to (21) with Huffman.</span>

Contrasting Population Projections to Induce Divergent Estimates of Landslides Exposure Under Climate Change

AbstractAt first glance, assessing future landslide‐exposed population appears to be a straightforward task if landslide hazard estimates, climate change, and population projections are available. However, the intersection of landslide hazard with socioeconomic elements may result in significant variation of estimated landslide exposure due to considerable variations in population projections. This study aims to investigate the effects of different sources of population data on the evaluation of landslide‐exposed population in China under four Shared Socioeconomic Pathways (SSPs) scenarios. We utilize multiple global climate models (GCMs) from Coupled Model Intercomparison Project Phase 6 and six high‐resolution spatially explicit static and dynamic population data sets to drive available landslide models. The results indicate an overall rise in landslide hazard projections, with an increase in the potential impact area of 0.4%–2.7% and an increase in the landslide frequency of 4.7%–20.1%, depending on the SSPs scenarios and future periods. However, the likely changes in future landslide exposed population, as modeled by incorporating population data from different sources with landslide hazard, yield divergent outcomes depending on the population data source. Thus, some of the projections depict an increase in future landslide exposure, while others show a clear decrease. The nationwide divergence ranged from −64% to +48%. These divergent findings were mainly attributed to differences in population data and a lesser extent to variations in GCMs. The present findings highlight the need to pay closer attention to the dynamic evolution of the elements at risk and the associated data uncertainties.

Reaching consensus for membership dynamic in secret sharing and its application to cross-chain

The communication efficiency optimization, censorship resilience, and generation of shared randomness are inseparable from the threshold cryptography in the existing Byzantine Fault Tolerant (BFT) consensus. The membership in consensus in a blockchain scenario supports dynamic changes, which effectively prevents the corruption of consensus participants. Especially in cross-chain protocols, the dynamic access to different blockchains will inevitably bring about the demand for member dynamic. Most existing threshold cryptography schemes rely on redefined key shares, leading to a static set of secret sharing participants. In this paper, we propose a general approach to coupling blockchain consensus and dynamic secret sharing. The committee performs consensus confirmation of both dynamic secret sharing and transaction proposals. Our scheme facilitates threshold cryptography membership dynamic, thus underlying support for membership dynamic of threshold cryptography-based BFT consensus schemes. We instantiate a dynamic HotStuff consensus to demonstrate the effectiveness of the scheme. After the correctness and security proof, our scheme achieves the secrecy and integrity of the threshold key shares while ensuring consensus liveness and safety. Experimental results prove that our scheme obtains dynamic membership with negligible overhead.

Result-oriented lumped error correction for photonic-assisted broadband phased array processors.

Imperfect optoelectronic devices deteriorate the performance of microwave photonic (MWP) systems and then hinder further practical application. This paper proposes a result-oriented lumped error correction to address the problem in the photonic-assisted broadband phased array. Herein, we focus on the evolution of the ultimate output resulting from various errors due to the nonideality of components. By establishing the static calibration base set (CBS) with tangent line approximation, the correction procedure is simplified, and the output degradation is greatly improved. Experimental results show the effective number of bits (ENOB) at the final output has been enhanced from 2.5 to 6.1. Further, double objectives optimization and imaging correction are demonstrated experimentally. The range resolution has been boosted from 3.9 cm to 2.4 cm, and the quality of the inverse synthetic aperture radar (ISAR) images is improved using the proposed method.

A framework for designing vulnerability metrics

Vulnerability analysis has long been used to evaluate the security posture of a system. Different approaches, including vulnerability graphs and various vulnerability metrics, have been used to study the vulnerability landscape and provide security analysts with cyber situational awareness. However, most current solutions still lack a principled approach to quantifying various dimensions of known vulnerabilities in a way that can easily adapt to different applicative domains and operating conditions. To address this limitation, we introduce a vulnerability metrics framework that extends and generalizes our previous metrics for evaluating the exploitation likelihood of a vulnerability and the exposure factor of system components to vulnerability exploits. We argue that the factors influencing these metrics and their relative weights depend on the specific applicative domain, defender’s priorities, and attacker’s knowledge. Thus, instead of providing a static set of equations, we establish a framework for instantiating the equations that best model the scenario being considered. We combine likelihood and exposure factor metrics into a severity score that allows us to rank vulnerabilities. In our evaluation, we demonstrate that ranking vulnerabilities solely based on their CVSS scores is not sufficient for effective prioritization, due to the limited number of possible distinct severity values compared to the sheer number of existing vulnerabilities. We define a ranking quality score and show that considering additional information about vulnerabilities helps refine their ranking, providing more actionable intelligence to security analysts.

Locality-preserving minimal perfect hashing of k-mers.

Minimal perfect hashing is the problem of mapping a static set of n distinct keys into the address space {1,…,n} bijectively. It is well-known that n log 2(e) bits are necessary to specify a minimal perfect hash function (MPHF) f, when no additional knowledge of the input keys is to be used. However, it is often the case in practice that the input keys have intrinsic relationships that we can exploit to lower the bit complexity of f. For example, consider a string and the set of all its distinct k-mers as input keys: since two consecutive k-mers share an overlap of k-1 symbols, it seems possible to beat the classic log 2(e) bits/key barrier in this case. Moreover, we would like f to map consecutive k-mers to consecutive addresses, as to also preserve as much as possible their relationship in the codomain. This is a useful feature in practice as it guarantees a certain degree of locality of reference for f, resulting in a better evaluation time when querying consecutive k-mers. Motivated by these premises, we initiate the study of a new type of locality-preserving MPHF designed for k-mers extracted consecutively from a collection of strings. We design a construction whose space usage decreases for growing k and discuss experiments with a practical implementation of the method: in practice, the functions built with our method can be several times smaller and even faster to query than the most efficient MPHFs in the literature.

Spiking neural network with working memory can integrate and rectify spatiotemporal features.

In the real world, information is often correlated with each other in the time domain. Whether it can effectively make a decision according to the global information is the key indicator of information processing ability. Due to the discrete characteristics of spike trains and unique temporal dynamics, spiking neural networks (SNNs) show great potential in applications in ultra-low-power platforms and various temporal-related real-life tasks. However, the current SNNs can only focus on the information a short time before the current moment, its sensitivity in the time domain is limited. This problem affects the processing ability of SNN in different kinds of data, including static data and time-variant data, and reduces the application scenarios and scalability of SNN. In this work, we analyze the impact of such information loss and then integrate SNN with working memory inspired by recent neuroscience research. Specifically, we propose Spiking Neural Networks with Working Memory (SNNWM) to handle input spike trains segment by segment. On the one hand, this model can effectively increase SNN's ability to obtain global information. On the other hand, it can effectively reduce the information redundancy between adjacent time steps. Then, we provide simple methods to implement the proposed network architecture from the perspectives of biological plausibility and neuromorphic hardware friendly. Finally, we test the proposed method on static and sequential data sets, and the experimental results show that the proposed model can better process the whole spike train, and achieve state-of-the-art results in short time steps. This work investigates the contribution of introducing biologically inspired mechanisms, e.g., working memory, and multiple delayed synapses to SNNs, and provides a new perspective to design future SNNs.

Face identification proficiency test designed using item response theory.

Measures of face-identification proficiency are essential to ensure accurate and consistent performance by professional forensic face examiners and others who perform face-identification tasks in applied scenarios. Current proficiency tests rely on static sets of stimulus items and so cannot be administered validly to the same individual multiple times. To create a proficiency test, a large number of items of "known" difficulty must be assembled. Multiple tests of equal difficulty can be constructed then using subsets of items. We introduce the Triad Identity Matching (TIM) test and evaluate it using item response theory (IRT). Participants view face-image "triads" (N = 225) (two images of one identity, one image of a different identity) and select the different identity. In Experiment3, university students (N = 197) showed wide-ranging accuracy on the TIM test, and IRT modeling demonstrated that the TIM items span various difficulty levels. In Experiment3, we used IRT-based item metrics to partition the test into subsets of specific difficulties. Simulations showed that subsets of the TIM items yielded reliable estimates of subject ability. In Experiments3a and b, we found that the student-derived IRT model reliably evaluated the ability of non-student participants and that ability generalized across different test sessions. In Experiment3c, we show that TIM test performance correlates with other common face-recognition tests. In summary, the TIM test provides a starting point for developing a framework that is flexible and calibrated to measure proficiency across various ability levels (e.g., professionals or populations with face-processing deficits).

Probe microscopy is all you need * * Notice: This manuscript has been authored by UT-Battelle, LLC, under Contract No. DE-AC0500OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published

We pose that microscopy offers an ideal real-world experimental environment for the development and deployment of active Bayesian and reinforcement learning methods. Indeed, the tremendous progress achieved by machine learning (ML) and artificial intelligence over the last decade has been largely achieved via the utilization of static data sets, from the paradigmatic MNIST to the bespoke corpora of text and image data used to train large models such as GPT3, DALL·E and others. However, it is now recognized that continuous, minute improvements to state-of-the-art do not necessarily translate to advances in real-world applications. We argue that a promising pathway for the development of ML methods is via the route of domain-specific deployable algorithms in areas such as electron and scanning probe microscopy and chemical imaging. This will benefit both fundamental physical studies and serve as a test bed for more complex autonomous systems such as robotics and manufacturing. Favorable environment characteristics of scanning and electron microscopy include low risk, extensive availability of domain-specific priors and rewards, relatively small effects of exogenous variables, and often the presence of both upstream first principles as well as downstream learnable physical models for both statics and dynamics. Recent developments in programmable interfaces, edge computing, and access to application programming interfaces (APIs) facilitating microscope control, all render the deployment of ML codes on operational microscopes straightforward. We discuss these considerations and hope that these arguments will lead to create novel set of development targets for the ML community by accelerating both real world ML applications and scientific progress.

¿Cómo se construye el pasado? Reflexiones desde una etnografía histórica en el istmo veracruzano

The objective of this article is to reflect on the non-univocal construction of the past. To this end, it contrasts the reconstruction of the past based on archival sources against ethnographic information as part of an academic research, with the views on the past elaborated by the local people from their different socio-cultural and political positions. The existing gaps between both approaches to the past are shown, but without seeking to establish the “veracity” of one or the other. It is concluded that the past does not refer to a static set of events, accessible in the same way to anyone at any time, but that in the reconstruction of the past, events acquire or lose relevance depending on theoretical approaches as well as on present experiences and imagined futures. In other words, the present and the past are mutually constituted as part of a process that is always dynamic and changing.

Parallel Training: An ACP-Based Training Framework for Iterative Learning in Uncertain Driving Spaces

The traffic environment and driving behaviors are of great complexity and uncertainty in our physical world. Therefore, training in the digital world with low cost and diverse complexities become popular for autonomous driving in recent years. However, the current training methods tend to be limited to static data sets and deterministic models that do not sufficiently take into account the uncertainty and diversity prevalent in real traffic scenarios. These approaches also limit more possibilities for the comprehensive development and optimization of vision systems. In this paper, we develop a parallel training method based on artificial systems, computational experiments, and parallel execution (ACP) for the intelligent optimization and learning of the aforementioned agents in uncertain driving spaces. Parallel training creates a virtual driving space following the instruction of the ACP approach and conducts large-scale rehearsal experiments for possible scenarios. By enhancing the diversity of virtual scenarios, intelligent vehicles are trained to respond and adapt to the diverse uncertainties in the physical real-world driving space. Specifically, parallel training first proposes a standard operating procedure for intelligent driving systems, namely the projection-emergence-convergence-operation (PECO) loop. Digital quadruplets for parallel training, i.e., physical, descriptive, predictive, and prescriptive coaches, are also proposed. With the guidance of parallel training, virtual and real-world driving spaces are set up in parallel and interact frequently. They are closely linked and unified in opposition to each other, ultimately building a parallel driving system that fulfills safety, security, sustainability, sensitivity, service, and smartness (6S).

Q learning based adaptive protocol parameters for WSNs

Wireless sensor networks (WSN) are widely used for multi-disciplinary applications. According to the requirements and the goal of the application, the network is designed and the protocol is tuned to obtain the best performance of the WSN. In real world applications, all nodes in the network have a common protocol parameter set, irrespective of their position in the network. In several experiments with multihop sensor networks, we observed that individual nodes perform differently depending on the protocol parameter values. With the observation the question was raised whether the performance of the network can be improved by using tuned parameter sets for each individual node in the network. Tuning protocol parameters for each node manually is tedious and may not be practical for large number of nodes. As a solution, adaptive protocol parameters are introduced using reinforcement learning. The learning algorithm gradually approaches an optimal set of protocol parameter values for each and every node during the runtime resulting in average improved network performance with 13.44% and 29.41% compared to networks with static common parameter sets in a network of 20 and 30 nodes respectively in simulation environment. The performance of the adaptive protocol is validated using real testbed with 10 nodes and the performance improvement is 16.21%. With the simulation results it was observed that networks with higher number of nodes obtain more performance gain using the adaptive protocol algorithm compared to networks with lower of nodes.

Deployment of an IoT System for Adaptive In-Situ Soundscape Augmentation

Soundscape augmentation is an emerging approach for noise mitigation by introducing additionalsounds known as "maskers" to increase indices like acoustic comfort. Traditionally, the choice of maskers is oftenpredicated on expert guidance or post-hoc analysis which can be time-consuming and sometimesarbitrary. Moreover, this often results in a static set of maskers that are inflexible to the dynamicnature of real-world acoustic environments. Overcoming the inflexibility of traditional soundscapeaugmentation is two-fold. First, given a snapshot of a soundscape, the system must be able to select anoptimal masker without human supervision. Second, the system must also be able to react to changesin the acoustic environment with near real-time latency. In this work, we harness the combinedprowess of cloud computing and internet-of-things (IoT) to allow in-situ listening and playback usingmicrocontrollers while delegating the computationally expensive inference tasks to the cloud. Inparticular, a serverless cloud architecture was used for inference, ensuring near real-time latencyand scalability without the need to provision computing resources. A working prototype of the systemis currently being deployed in a public area experiencing high traffic noise, as well as undergoingpublic evaluation for future improvements.

Hedging Effectiveness as an International Financial Risk Management Strategy

In the dynamic landscape of global finance, businesses face intricate challenges navigating international markets, necessitating robust risk management strategies. This explores the evolution of hedging as a crucial financial maneuver originating from the 20th-century financial upheavals. Hedging, strategically responding to market fluctuations, has transformed into a sophisticated risk management tool, requiring a nuanced understanding of market dynamics. It serves as a financial shield, particularly in the face of currency fluctuations and interest rate volatility for multinational corporations. The symbiotic relationship between theoretical underpinnings and practical application becomes evident as the narrative unfolds. The story emphasizes the complexities and challenges of financial markets, highlighting the need for strategic and proactive responses. The effectiveness of hedging is portrayed not as a one-size-fits-all solution but as a philosophy ingrained in decision-making processes for businesses operating globally. Real-world case studies dissect the successes and failures of companies employing hedging, bridging theory and application. Hedging is portrayed not as a static set of practices but as a dynamic philosophy adapting to technological advancements. The integration of sophisticated algorithms and artificial intelligence transforms risk management, enabling more precise, agile, and adaptive hedging approaches.

Ensemble Methods for Spatial Data Stream Classification

In this paper, we study the problem of classification of data in a spatial data stream. The classification of spatial objects in a static data set has been well studied. However, classifying objects in a spatial data stream has received very little attention. We propose an iterative ensemble approach for deep learning of a spatial data stream. Using several deep neural networks, our strategy iteratively performs training and testing of the classifier, with the goal of reaching a desired accuracy, and the same accuracy that would be achieved as a classifier that is trained and tested with the entire dataset. An experimental evaluation and comparison of the ensemble approach shows improvement over a previously proposed iterative deep learning strategy.

Exploiting Ideology and Making Higher Education Serve Vietnam’s Authoritarian Regime

This article shows how the Communist Party of Vietnam has proactively deployed ideological indoctrination in the higher education system to raise submissive youth as well as to suppress dissenting academics. By demonstrating the use of organizational constraint and ideological exploitation strategies, I propose that post-totalitarian regimes’ political ideology should not just be viewed as a static set of theories or some official claims by the ruling party. Rather, an examination of how states strategically use ideological principles on a day-to-day basis gives us a more nuanced understanding. That is, ideology can be deployed as an effective tool to cultivate loyalty in students and signal a regime’s strength to suppress academics’ dissent. The article illustrates how educational organizations, once having their norms and disciplines embedded in political ideology, can act effectively to consolidate authoritarian regimes at a deeper level.

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.

Efficient Direct Isosurface Rasterization of Scalar Volumes

AbstractIn this paper we propose a novel and efficient rasterization‐based approach for direct rendering of isosurfaces. Our method exploits the capabilities of task and mesh shader pipelines to identify subvolumes containing potentially visible isosurface geometry, and to efficiently extract primitives which are consumed on the fly by the rasterizer. As a result, our approach requires little preprocessing and negligible additional memory. Direct isosurface rasterization is competitive in terms of rendering performance when compared with ray‐marching‐based approaches, and significantly outperforms them for increasing resolution in most situations. Since our approach is entirely rasterization based, it affords straightforward integration into existing rendering pipelines, while allowing the use of modern graphics hardware features, such as multi‐view stereo for efficient rendering of stereoscopic image pairs for geometry‐bound applications. Direct isosurface rasterization is suitable for applications where isosurface geometry is highly variable, such as interactive analysis scenarios for static and dynamic data sets that require frequent isovalue adjustment.

Robust and Fault-Tolerant Fog Design and Dimensioning for Reliable Operation

Internet of Things (IoT) applications depend on reliable external storage and processing such as cloud data centers. In response to high latency from cloud, fog computing has been introduced as a network of microdata centers closer to IoT devices that provides a geo-distributed low-latency response. Current contributions regarding design and dimensioning of fog infrastructures are developed to service a static set of IoT traffic and a reliable fog network. However, these designs are not fault tolerant. This article explores the implementation of reliable and fault-tolerant fog infrastructures via dynamically available fog nodes—standby nodes that activate when a nearby fog node fails. We formulate the design and dimensioning of dynamically available nodes as a set partitioning problem, which is solved via a mixed-integer linear program (MILP). This MILP formulation proves to be intractable; we therefore introduce a column generation approach to increase scalability with little loss to optimal design and dimensioning cost. Compared to other benchmark heuristic methods, our column generation approach yields reduced cost, with proportional solution time.

Moroni, Moses, and President Nelson: Vital Correlations with the COVID-19 Vaccine

Moroni, Moses, and President Nelson: Vital Correlations with the COVID-19 Vaccine