Sequential Aggregation Research Articles

Trustworthy and Efficient Digital Twins in Post-Quantum Era with Hybrid Hardware-Assisted Signatures

Digital Twins (DT) virtually model cyber-physical objects via sensory inputs by simulating or monitoring their behavior. Therefore, DTs usually harbor vast quantities of Internet of Things (IoT) components (e.g., sensors) that gather, process, and offload sensitive information (e.g., healthcare) to the cloud. It is imperative to ensure the trustworthiness of such sensitive information with long-term and compromise-resilient security guarantees. Digital signatures provide scalable authentication and integrity with non-repudiation and are vital tools for DTs. Post-quantum cryptography (PQC) and forward-secure signatures are two fundamental tools to offer long-term security and breach resiliency. However, NIST-PQC signature standards are exorbitantly costly for embedded DT components and are infeasible when forward-security is also considered. Moreover, NIST-PQC signatures do not admit aggregation, which is a highly desirable feature to mitigate the heavy storage and transmission burden in DTs. Finally, NIST recommends hybrid PQ solutions to enable cryptographic agility and transitional security. Yet, there is a significant gap in the state of the art in the achievement of all these advanced features simultaneously. Therefore, there is a significant need for lightweight digital signatures that offer compromise resiliency and compactness while permitting transitional security into the PQ era for DTs. We create a series of highly lightweight digital signatures called Hardware-ASisted Efficient Signature ( HASES ) that meets the above requirements. The core of HASES is a hardware-assisted cryptographic commitment construct oracle ( CCO ) that permits verifiers to obtain expensive commitments without signer interaction. We created three HASES schemes: PQ-HASES is a forward-secure PQ signature, LA-HASES is an efficient aggregate Elliptic-Curve signature, and HY-HASES is a novel hybrid scheme that combines PQ-HASES and LA-HASES with novel strong nesting and sequential aggregation. HASES does not require a secure-hardware on the signer. We prove that HASES schemes are secure and implemented them on commodity hardware and and 8-bit AVR ATmega2560. Our experiments confirm that PQ-HASES and LA-HASES are two magnitudes of times more signer efficient than their PQ and conventional-secure counterparts, respectively. HY-HASES outperforms NIST PQC and conventional signature combinations, offering a standard-compliant transitional solution for emerging DTs. We open-source HASES schemes for public-testing and adaptation.

Energy and Distribution-Aware Cooperative Clustering Algorithm in Internet of Things (IoT)-Based Federated Learning

In Internet of Things (IoT)-based federated learning (FL), if IoT devices are located far from the base station (BS), they consume lots of energy to transmit the updated parameters to BS, which can cause energy depletion of IoT devices. To mitigate this problem, IoT devices can be clustered and the clustered IoT devices transmit the updated parameters to their cluster headers (CHs) instead of BS. However, if the aggregated data distribution in each cluster is non-independent and identically distributed (non-IID), the desired accuracy of the model cannot be achieved. In this paper, we propose an energy and distribution-aware cooperative clustering algorithm (EDA-CCA) where several IoT devices with a sufficient energy level are selected as CHs. By considering the distance to these CHs and BS and the data distribution of IoT devices, other IoT devices are clustered, and then a hierarchical parameter aggregation (i.e., sequential aggregation within each cluster and synchronous aggregation between CHs and FL server) is conducted. Evaluation results demonstrate that EDA-CCA can make the model having the desired accuracy with the lowest energy consumption among the comparison schemes.

Targeted mechanical stimulation via magnetic nanoparticles guides in vitro tissue development

Tissues take shape through a series of morphogenetic movements guided by local cell-scale mechanical forces. Current in vitro approaches to recapitulate tissue mechanics rely on uncontrolled self-organization or on the imposition of extrinsic and homogenous forces using matrix or instrument-driven stimulation, thereby failing to recapitulate highly localized and spatially varying forces. Here we develop a method for targeted mechanical stimulation of organoids using embedded magnetic nanoparticles. We show that magnetic clusters within organoids can be produced by sequential aggregation of magnetically labeled and non-labeled human pluripotent stem cells. These clusters impose local mechanical forces on the surrounding cells in response to applied magnetic fields. We show that precise, spatially defined actuation provides short-term mechanical tissue perturbations as well as long-term cytoskeleton remodeling in these organoids, which we term “magnetoids”. We demonstrate that targeted magnetic nanoparticle-driven actuation guides asymmetric tissue growth and proliferation, leading to enhanced patterning in human neural magnetoids. This approach, enabled by nanoparticle technology, allows for precise and locally controllable mechanical actuation in human neural tube organoids, and could be widely applicable to interrogate the role of local mechanotransduction in developmental and disease model systems.

Multi-Tenancy- and Redundancy-Aware In-Network Aggregation using Programmable Switches

Recent advances in programmable switches make it possible to aggregate data in partition-aggregation applications (e.g., MapReduce applications) at programmable switches. However, a well-designed aggregation scheme is indispensable for aggregating as much data as possible under the limited resources of programmable switches in an environment where multiple applications are running concurrently. In this article, we propose a multitenancy- and redundancy-aware in-network aggregation (MARINA) scheme that preferentially aggregates highly redundant data at a programmable switch and improves aggregation performance by constructing a multi-tenancyaware aggregation tree. Evaluation results demonstrate that MARINA can improve data aggregation performance by up to 81% compared with the conventional approach using statically partitioned resources and sequential aggregation.

An Artificial Intelligence model for smart post-stroke assessment using wearable sensors

Wearable sensors provide a recording capability of the body segments’ motion. By collecting and analyzing the segments’ movements, automating the post-stroke assessment process would be feasible and reliable. Post-stroke classification of impaired or non-impaired body parts is the first step of automating the assessment process. It is recently achievable by utilizing enhancements in supervised machine learning (ML) using wearable sensors. Many classification-based approaches have numerous complexity/quality trade-offs with no optimal solution for all datasets of various structures, properties, distributions, and sizes. Ensemble machine learning provides reliable classification systems for diagnostic purposes. However, there are no criteria for identifying the baseline models in the aggregate (parallel or sequential aggregation), which significantly impacts the complexity and accuracy of the designed ensemble. In this paper, we propose an efficient post-stroke diagnosis system using the notion of multi-level ensemble learning, the Multi-Level Meta Learner (MLML) ensemble algorithm with heterogeneous or homogeneous baseline classifiers using Xsens wearable sensor collected dataset. The MLML outperforms the baseline models with comparable performance while generating a list of optimal candidate heterogeneous baseline classifiers. This list can provide only potential positive combinations, significantly improving the performance and reducing the complexity of building an ensemble. The linear acceleration and angular velocity derived from the wearable sensors are utilized. Experimental results show that the MLML has enhanced the accuracy of classifying the impaired hands from non-impaired hands with an improvement of up to 95.64% and 91.2% for boosting and Bagging learning, respectively.

Multi-channel CNN-Based Rāga Recognition in Carnatic Music Using Sequential Aggregation Strategy

Multi-channel CNN-Based Rāga Recognition in Carnatic Music Using Sequential Aggregation Strategy

Multi-Label Bird Species Classification Using Sequential Aggregation Strategy from Audio Recordings

Multi-Label Bird Species Classification Using Sequential Aggregation Strategy from Audio Recordings

Increasing H-Aggregates via Sequential Aggregation to Enhance the Hole Mobility of Printed Conjugated Polymer Films

Increasing H-Aggregates via Sequential Aggregation to Enhance the Hole Mobility of Printed Conjugated Polymer Films

City services provision assessment algorithm

The paper is dedicated to the problem of computational urban science to estimate availability of urban services with limited capacity. The applied side of the problem is related to the need to assess the provision of citizens living in residential buildings in the city with a sufficient number of places of city services, such as kindergartens, schools, clinics, and others. This information is necessary for the qualitative management of the socio-economic and spatial development of the city. The computational complexity of the problem is associated with a great variety of factors that determine the variability of the citizens' preference function when accessing a particular service. It is further complicated by the presence of self-organization processes within this system, which lead to the actual final satisfaction of the citizens' service demand but with significant deviations from the required parameters of service availability established in regulatory documents or the optimal state of the system. Since validation of such models for large cities is difficult or impossible, this requires the development of new computational approaches for assessing the provision of the population with urban services based on the optimal state of the system to improve the efficiency of city management. A proposed algorithm is based on sequential aggregation of the demand from buildings for each service object and redistribution of unserved demand between other services objects with free capacity. This approach avoids high computational complexity which arises when using nonlinear programming to calculate buildings’ provision of services in large cities. The article presents the algorithm for calculating the provision of the population and demonstrates its application on the data of the city of St. Petersburg.

Performance tracking: A historical background to promote learning

This geologic note provides a lookback on lookbacks, hopefully to spur further interest by companies in generating learning from exploration experience, rather than being victimized by repeated mistakes to which exploration assessment may unknowingly be exposed. The lookback contribution to portfolio management is properly called performance tracking. Likely inspired by the after-action review process installed by the US Army approximately 50 yr ago, oil industry published results of tracking predictive performance began via a simple cross plot (predrill prospect resource size versus postdrill accumulation resource size) by US Geological Survey (USGS) workers documenting discovery sizes on the United States continental shelf. Whereas predrill estimates made by the USGS were relatively accurate, analogous publications by the industry have usually documented recurring optimistic bias. Other contributions have demonstrated the utility of determining (1) the efficiency of prospect ranking by various predrill estimation parameters (via an estimation efficiency plot), (2) when a drilling program falls outside of statistical control (via a sequential aggregation plot), and (3) where along a series of predrill forecast distributions persistent estimation bias has occurred (via a percentile histogram). These basics can serve as a starting point for any company to build their own baseline study to learn what aspects of their many estimates need improvement.

단일 입력변수기반 퍼지 모델을 이용한 순차결합 모델의 설계

본 논문에서는 실제출력과 모델출력사이의 오차를 최소화하기 위해 단일 입력변수기반 퍼지모델들로 구현된 순차결합 모델의 설계 방법론을 제안한다. 단일 입력변수 기반 퍼지 모델의 전반부 멤버십 함수는 퍼지 클러스터링을 통해 얻은 클러스터의 중심점을 사용하여 생성한다. 퍼지 규칙의 후반부는 선형 함수로 구성되며 선형 함수의 계수는 LSE 기반 학습에 의해 훈련된다. 순차결합 모델은 기본 모델과 보상 모델로 구분할 수 있다. 기본 모델은 순차결합 모델의 첫 번째 모델을 의미하며 보상모델은 기본모델과 실제출력사이의 오차를 줄여주는 역할을 한다. 고차원 합성 데이터 및 머신 러닝 데이터를 이용한 실험을 통해 제안된 순차결합 모델과 기존 퍼지 모델을 비교하여 제안된 설계 방법론의 우수성을 보여준다.

Sequential Aggregation of Probabilistic Forecasts—Application to Wind Speed Ensemble Forecasts

Abstract In numerical weather prediction (NWP), the uncertainty about the future state of the atmosphere is described by a set of forecasts (called an ensemble). All ensembles have deficiencies that can be corrected via statistical post-processing methods. Several ensembles, based on different NWP models, exist and may be corrected using different statistical methods. These raw or post-processed ensembles can thus be combined. The theory of prediction with expert advice allows us to build combination algorithms with theoretical guarantees on the forecast performance. We adapt this theory to the case of probabilistic forecasts issued as stepwise cumulative distribution functions, computed from raw and post-processed ensembles. The theory is applied to combine wind speed ensemble forecasts. The second goal of this study is to explore the use of two forecast performance criteria: the continuous ranked probability score (CRPS) and the Jolliffe–Primo test. The usual way to build skilful probabilistic forecasts is to minimize the CRPS. Minimizing the CRPS may not produce reliable forecasts according to the Jolliffe–Primo test. The Jolliffe–Primo test generally selects reliable forecasts, but could lead to issuing suboptimal forecasts in terms of CRPS. We propose to use both criteria to achieve reliable and skilful probabilistic forecasts.

Action Recognition Using Deep 3D CNNs with Sequential Feature Aggregation and Attention

Action recognition is an active research field that aims to recognize human actions and intentions from a series of observations of human behavior and the environment. Unlike image-based action recognition mainly using a two-dimensional (2D) convolutional neural network (CNN), one of the difficulties in video-based action recognition is that video action behavior should be able to characterize both short-term small movements and long-term temporal appearance information. Previous methods aim at analyzing video action behavior only using a basic framework of 3D CNN. However, these approaches have a limitation on analyzing fast action movements or abruptly appearing objects because of the limited coverage of convolutional filter. In this paper, we propose the aggregation of squeeze-and-excitation (SE) and self-attention (SA) modules with 3D CNN to analyze both short and long-term temporal action behavior efficiently. We successfully implemented SE and SA modules to present a novel approach to video action recognition that builds upon the current state-of-the-art methods and demonstrates better performance with UCF-101 and HMDB51 datasets. For example, we get accuracies of 92.5% (16f-clip) and 95.6% (64f-clip) with the UCF-101 dataset, and 68.1% (16f-clip) and 74.1% (64f-clip) with HMDB51 for the ResNext-101 architecture in a 3D CNN.

An IoT-oriented real-time storage mechanism for massive small files based on Swift

In the internet of things (IoT), large amounts of small files are generated from various structure sensors in cloud storage platforms. Real-time storage of massive small files will put great pressures to the traditional file system. By studying the real-time storage strategy of massive small files based on the object storage architecture Swift, we proved a unique aggregation storage strategy, called sequential data aggregation strategy (SDAS), for storage of small files. We designed a two-level index structure to improve writing rate by transferring randomly write to sequentially write. To improve overall data access efficiency and solve the performance bottleneck of proxy node, we utilise a file's potential relevance of timing to prefetch related files that are merged in other blocks. Simulation results show that relative to the original system, SDAS has shorter response time of writing operation, lower cost of index maintenance cost, more balanced node load and 30% reduction in disk overhead.

USING THE AGGREGATED CRITERIA TO EVALUATE THE SOFTWARE TESTS QUALITY

An approach to evaluating the software tests quality using aggregated quality criteria is proposed. The article considers the finding of such characteristics of software tests that can be used to judge their quality and their need for improvement. The subject of the study is the formation of a software tests quality evaluation system, which can be used in the software development process. It is proposed to consider a software test as a multiattribute object. It is emphasized that it is necessary to take into account both quantitative and qualitative characteristics of tests and test coverage, which greatly complicates the construction of a model for evaluating the software tests quality. Various approaches to solving the problem of evaluating complex, multiattribute objects are considered. The problem of comparing and ordering complex objects taking into account different criteria is considered. The choice of the method of sequential aggregation of classified states to solve the problem of multi-criteria selection and assessment is justified. The stages of the procedure for solving the estimation problem using the method of sequential aggregation of classified states are considered. An activity diagram is constructed that reflects an algorithm for constructing a hierarchical system of criteria. The criteria for evaluating software tests are given, which belong to three groups - efficiency, coverage, and software implementation. For a hierarchical system of criteria aggregation, a set of indicators, their qualitative gradations with corresponding numerical intervals, are allocated. At the highest level of the hierarchy, it is proposed to use three composite criteria that correspond to the groups of efficiency, coverage and implementation, which will allow to obtain an integral indicator of the software tests quality. The resulting integral indicator includes five quality classes, each of which corresponds to a multitude of low-level indicator estimates. Tests quality evaluation will improve the testing process, which purpose is to ensure the specified quality of the software being developed.

Long-term prediction of system degradation with similarity analysis of multivariate patterns

To forecast the long-term degradation behavior of mechanical systems, a method named Distance Based Sequential Aggregation with Gaussian Mixture Model (DBSA-GMM) that completes predictions with two steps was proposed: first, it calculates the statistical distances (SD-s) of the objective degradation signature pattern to historical precursors, then, it uses the SD-S to generate hypothetical Gaussian estimations of the objective features, and synthesizes these Gaussians to build up Gaussian Mixture Model (GMM) approximations of feature Probability Density Functions (PDF-s) with a newly proposed algorithm called Descending Order Aggregation (DOA). DBSA-GMM was applied in the condition prediction of a fleet of commercial aero-engines and showed advantageous prediction precision over Auto-Regressive Moving Average (ARMA), Back Propagation Artificial Neural Network (BP-ANN), and former similarity based prediction (SBP) methods. Meanwhile, DOA was also validated to be with higher generalization ability with additional tests on outlier samples against Kernel Density Estimation (KDE) method.

Sequential decision aggregation with social pressure

This paper proposes and characterizes a sequential decision aggregation system consisting of agents performing binary sequential hypothesis testing and a fusion center which collects the individual decisions and reaches the global decision according to some threshold rule. Individual decision makers’ behaviors in the system are influenced by other decision makers, through a model for social pressure; our notion of social pressure is proportional to the ratio of individual decision makers who have already made the decisions. For our proposed model, we obtain the following results: First, we derive a recursive expression for the probabilities of making the correct and wrong global decisions as a function of time, system size, and the global decision threshold. The expression is based on the individual decision makers’ decision probabilities and does not rely on the specific individual decision-making policy. Second, we discuss two specific threshold rules: the fastest rule and the majority rule. By means of a mean-field analysis, we relate the asymptotic performance of the fusion center, as the system size tends to infinity, to the individual decision makers’ decision probability sequence. In addition to theoretical analysis, simulation work is conducted to discuss the speed/accuracy tradeoffs for different threshold rules.

Sequential aggregation of heterogeneous experts for PM10 forecasting

We experiment some methods of sequential aggregation for forecasting PM10 concentrations for the next day, in the context of air quality monitoring in Normandy (France). Besides the field of application and the adaptation to the special context of the work of the forecaster, the main originality of this paper is that the set of experts contains at the same time statistical models built by means of various methods and groups of predictors, as well as experts coming from deterministic chemical models of prediction modeling pollution, weather and atmosphere.Numerical results on recent data from April 2013 until March 2015, on the whole network of fifteen monitoring stations, illustrate and compare some various methods of aggregation. The obtained results show that such a strategy clearly improves the performances of the best expert both in terms of prediction errors and in terms of alerts. What is more, it reaches, for the non-convex weighting strategy, the “unbiasedness” of observed-forecasted scatterplot, extremely difficult to obtain.

Aggregation Issues of Foreign Direct Investment Estimation in an Interdependent World

AbstractThis paper attempts to understand foreign direct investment (FDI) heterogeneity and offers useful insights about aggregation issues in FDI estimations by carrying out a spatial econometric analysis using affiliate‐level data on sales activities of Swedish multinational corporations around the globe. The results indicate that the multilayered nature of aggregation in FDI matters for empirical analysis. Affiliate‐level host‐country and third‐country sales activity provides evidence of a negative spatial lag or substitution of FDI in space, broadly supporting the export‐platform theory. For exports back to Sweden, we find a positive spatial lag suggesting agglomeration of production activities and vertical specialisation. The sequential aggregation from affiliate level to firm and country level provides evidence of a severe scale problem particularly in export‐platform FDI. This aggregation bias is likely present in many of the country‐level analyses in previous literature.

Ensemble forecast of solar radiation using TIGGE weather forecasts and HelioClim database

Medium-range forecasts (one day to two weeks) of solar radiation are commonly assessed with a single forecast at a given location. In this paper, we forecast maps of surface solar irradiance, using ensembles of forecasts from the THORPEX Interactive Grand Global Ensemble (TIGGE) with a 6-h timestep. We compare our forecasts with observations derived from MeteoSat Second Generation (MSG) and provided by the HelioClim-3 database as gridded observations over metropolitan France. First, we study the ensembles from six meteorological centers. Second, we use sequential aggregation to linearly combine all the forecasts with weights that vary in space and time. Sequential aggregation updates the weights before any forecast, using available observations. We use the global numerical weather prediction from the European Center for Medium-range Weather Forecasts (ECMWF) as a reference forecast. The issue of spatial resolution is discussed because the low resolution forecasts from TIGGE are compared to high resolution irradiance estimated from MSG data. We found that the TIGGE ensembles are under-dispersed but rather different from one to another. Aggregation decreases the forecast error by 20%, and produces a more realistic spatial pattern of predicted irradiance.