Aggregation Framework Research Articles

Aggregation of Votes with Multiple Positions on Each Issue

We consider the problem of aggregating votes cast by a society on a fixed set of issues, where each member of the society may vote for one of several positions on each issue, but the combination of votes on the various issues is restricted to a set of feasible voting patterns. We follow the aggregation framework used by Dokow and Holzman [Aggregation of non-binary evaluations, Advances in Applied Mathematics , 45:4, 487--504, 2010], in which both preference aggregation and judgment aggregation can be cast. We require the aggregation to be independent on each issue, and also supportive, i.e., for every issue, the corresponding component of every aggregator, when applied to a tuple of votes, must take as value one of the votes in that tuple. We prove that, in such a setup, non-dictatorial aggregation of votes in a society of an arbitrary size is possible if and only if either there is a non-dictatorial aggregator for two voters or there is an aggregator for three voters such that, for each issue, the corresponding component of the aggregator, when restricted to two-element sets of votes, is a majority operation or a minority operation. We then introduce a notion of a uniform non-dictatorial aggregator, which is an aggregator such that on every issue, and when restricted to arbitrary two-element subsets of the votes for that issue, it differs from all projection functions. We first give a characterization of sets of feasible voting patterns that admit a uniform non-dictatorial aggregator. After this and by making use of Bulatov’s dichotomy theorem for conservative constraint satisfaction problems, we connect social choice theory with the computational complexity of constraint satisfaction by proving that if a set of feasible voting patterns has a uniform non-dictatorial aggregator of some arity, then the multi-sorted conservative constraint satisfaction problem on that set (with each issue representing a different sort) is solvable in polynomial time; otherwise, it is NP-complete.

Distributed meter data aggregation framework based on Blockchain and homomorphic encryption

A significant progress in modern power grids is witnessed by the tendency of becoming complex cyber-physical systems. As a fundamental physical infrastructure, smart meter in the demand side provides real-time energy consumption information to the utility. However, ensuring information security and privacy in the meter data aggregation process is a non-trivial task. This study proposes a distributed, privacy-preserving, and secure meter data aggregation framework, backed up by Blockchain and homomorphic encryption (HE) technologies. Meter data are aggregated and verified by a hierarchical Blockchain system, in which the consensus mechanism is supported by the practical Byzantine fault tolerance algorithm. On the top of the Blockchain system, HE technology is used to protect the privacy of individual meter data items during the aggregation process. Performance analysis is conducted to validate the proposed method.

Dynamic modelling of the iron deficiency modulated transcriptome response in Arabidopsis thaliana roots

The iron deficiency response in plants is a complex biological process with a host of influencing factors. The ability to precisely modulate this process at the transcriptome level would enable genetic manipulations allowing plants to survive in nutritionally poor soils and accumulate increased iron content in edible tissues. Despite the collected experimental data describing different aspects of the iron deficiency response in plants, no attempts have been made towards aggregating this information into a descriptive and predictive model of gene expression changes over time. We formulated and trained a dynamic model of the iron deficiency induced transcriptional response in Arabidopsis thaliana. Gene activity dynamics were modelled with a set of ordinary differential equations that contain biologically tractable parameters. The trained model was able to capture and account for a significant difference in mRNA decay rates under iron sufficient and iron deficient conditions, approximate the expression behaviour of currently unknown gene regulators, unveil potential synergistic effects between the modulating transcription factors and predict the effect of double regulator mutants. The presented modelling approach illustrates a framework for experimental design, data analysis and information aggregation in an effort to gain a deeper understanding of various aspects of a biological process of interest.

A Deep Spatial and Temporal Aggregation Framework for Video-Based Facial Expression Recognition

Video-based facial expression recognition is a long-standing problem owing to a gap between visual features and emotions, difficulties in tracking the subtle movement of muscles and limited datasets. The key to solving this problem is to exploit effective features characterizing facial expression to perform facial expression recognition. We propose an effective framework to solve these problems. In our work, both spatial information and temporal information are utilized through the aggregation layer of a framework that fuses two state-of-the-art stream networks. We investigate different strategies for pooling across spatial information and temporal information. We find that it is effective to pool jointly across spatial information and temporal information for video-based facial expression recognition. Our framework is end-to-end trainable for whole-video recognition. In addressing the problem of facial recognition, the main contribution of this project is the design of a novel, trainable deep neural network framework that fuses spatial information and temporal information of video according to CNNs and LSTMs for pattern recognition. The experimental results on two public datasets, i.e., the RML and eNTERFACE05 databases, show that our framework outperforms previous state-of-the-art frameworks.

As‐global‐as‐possible stereo matching with adaptive smoothness prior

More global matching (MGM) overcomes the limitation of one-dimensional scanline optimisation in semi-global matching (SGM). Nevertheless, the possible weaknesses of the MGM algorithm are as follows: (i) only two directions are considered for each image traversal direction, which may lead to massive mismatches; (ii) disparity estimation around the object boundaries usually performs terrible since the smoothness term is designed independent of the image prior. In this research, the authors consider all of the four directions for each image traversal direction through a novel model. Besides utilising the prior of neighboured pixels' correlation, adaptive smoothness terms are modelled and augmented into the energy function. These contributions encourage ‘as-global-as-possible (AGAP)’. More importantly, different from the recent works in which the aggregated algorithms have been conducted as the data term of an energy function, conversely, the authors make the energy function as a part of cost aggregation framework. Performance evaluations on Middlebury v.2 and v.3 stereo data sets demonstrate that the proposed AGAP outperforms other four most challenging stereo matching algorithms, and also performs better on Microsoft i2i stereo videos. In addition, under various strategies of parallelisation, the presented AGAP shows a near real-time execution time.

Imputation of race/ethnicity to enable measurement of HEDIS performance by race/ethnicity.

To improve an existing method, Medicare Bayesian Improved Surname Geocoding (MBISG) 1.0 that augments the Centers for Medicare & Medicaid Services' (CMS) administrative measure of race/ethnicity with surname and geographic data to estimate race/ethnicity. Data from 284627 respondents to the 2014 Medicare CAHPS survey. We compared performance (cross-validated Pearson correlation of estimates and self-reported race/ethnicity) for several alternative models predicting self-reported race/ethnicity in cross-sectional observational data to assess accuracy of estimates, resulting in MBISG 2.0. MBISG 2.0 adds to MBISG 1.0 first name, demographic, and coverage predictors of race/ethnicity and uses a more flexible data aggregation framework. We linked survey-reported race/ethnicity to CMS administrative and US census data. MBISG 2.0 removed 25-39 percent of the remaining MBISG 1.0 error for Hispanics, Whites, and Asian/Pacific Islanders (API), and 9 percent for Blacks, resulting in correlations of 0.88 to 0.95 with self-reported race/ethnicity for these groups. MBISG 2.0 represents a substantial improvement over MBISG 1.0 and the use of CMS administrative data on race/ethnicity alone. MBISG 2.0 is used in CMS' public reporting of Medicare Advantage contract HEDIS measures stratified by race/ethnicity for Hispanics, Whites, API, and Blacks.

FSDA: Framework for Secure Data Aggregation in Wireless Sensor Network for Enhancing Key Management

An effective key management plays a crucial role in imposing a resilient security technique in Wireless Sensor Network (WSN). After reviewing the existing approaches of key management, it is confirmed that existing approachs does not offer good coverage on all potential security breaches in WSN. With WSN being essential part of Internet-of-Things (IoT), the existing approaches of key management can definitely not address such security breaches. Therefore, this paper introduces a Framework for Secure Data Aggregation (FSDA) that hybridizes the public key encryption mechanism in order to obtain a novel key management system. The proposed system does not target any specific attacks but is widely applicable for both internal and external attacks in WSN owing to its design principle. The study outcome exhibits that proposed FSDA offers highly reduced computational burden, minimal delay, less energy consumption, and higher data transmission perforance in contrast to frequency used encryption schemes in WSN.

Influence of Construction Schemes for a Non-compensatory Composite Indicator on Flood Vulnerability Assessments in the Korean Peninsula

This study aims to investigate the influence of construction schemes for non-compensatory composite indicators by multiplicative utility functions on the flood vulnerability assessment. The flood vulnerability outcomes are evaluated and compared for the 231 administrative districts in the Republic of Korea, based on the two composite indicators by different aggregation schemes from the three assessment components such as exposure, sensitivity, and coping, presented in The IPCC Third Assessment Report. The one scheme uses the coping component having a negative functional relationship with vulnerability as a divisor, and the other scheme employs the lack of coping component in the opposite concept to coping as a multiplier. As a result of comparison analysis, some districts show markedly large differences in the flood vulnerability ranking orders by the two different aggregation schemes using the same proxy variables. For robustness of flood vulnerability assessment outcomes, it is necessary to compile a non-compensatory composite indicator under the condition that all constituent assessment components have the same directional elasticity to vulnerability. This study can help to select a proper aggregation framework in constructing flood vulnerability indicators to provide useful information for supporting policy and decision-making on complex issues.

Constant-Time Sliding Window Framework with Reduced Memory Footprint and Efficient Bulk Evictions

The fast evolution of data analytics platforms has resulted in an increasing demand for real-time data stream processing. From Internet of Things applications to the monitoring of telemetry generated in large data centers, a common demand for currently emerging scenarios is the need to process vast amounts of data with low latencies, generally performing the analysis process as close to the data source as possible. Stream processing platforms are required to be malleable and absorb spikes generated by fluctuations of data generation rates. Data is usually produced as time series that have to be aggregated using multiple operators, being sliding windows one of the most common abstractions used to process data in real-time. To satisfy the above-mentioned demands, efficient stream processing techniques that aggregate data with minimal computational cost need to be developed. In this paper we present the Monoid Tree Aggregator general sliding window aggregation framework, which seamlessly combines the following features: amortized $O(1)$ time complexity and a worst-case of $O(\log {n})$ between insertions; it provides both a window aggregation mechanism and a window slide policy that are user programmable; the enforcement of the window sliding policy exhibits amortized $O(1)$ computational cost for single evictions and supports bulk evictions with cost $O(\log {n})$ ; and it requires a local memory space of $O(\log {n})$ . The framework can compute aggregations over multiple data dimensions, and has been designed to support decoupling computation and data storage through the use of distributed Key-Value Stores to keep window elements and partial aggregations.

Constructing a Public Narrative of Regulations for Big Data and Analytics: Results From a Community-Driven Discussion

This article reports on community perspectives about the regulation of municipality-led Big Data initiatives developed through an exploratory, deliberative democracy-informed approach. While analytics hold great promise for policy design and service delivery improvements, their mythologized nature may elicit a blind faith in empirical outcomes, leading to misrepresentation or omission of marginalized populations. Scholars have begun pointing to public consultation as a means of avoiding these challenges, suggesting that a truly “smart city” should vet potential Big Data polices through the community in order to identify locally relevant concerns. The Big Data in Cities: Barriers and Benefits symposium, held in May of 2017, took a deliberative democracy approach designed to contribute toward a midsized southern Ontario city’s regulatory framework for data aggregation and mobilization. Approximately 100 self-selected participants (primarily public advocates) attended a 2-day symposium that featured a series of presentations designed to introduce critiques to and strategies for the implementation of Big Data initiatives. Participants also engaged in several facilitated roundtable discussions during the symposium, and their transcribed conversations served as the data for this study. Thematic analysis identified three recurrent concerns: publicly vetted data ethics, consultation and literacy practices, and regulatory frameworks. The public consultation process employed by this study produced results that reflect critiques raised in other academic papers.

Development of Framework for Aggregation and Visualization of Three-Dimensional (3D) Spatial Data

Geospatial information plays an important role in environmental modelling, resource management, business operations, and government policy. However, very little or no commonality between formats of various geospatial data has led to difficulties in utilizing the available geospatial information. These disparate data sources must be aggregated before further extraction and analysis may be performed. The objective of this paper is to develop a framework called PlaniSphere, which aggregates various geospatial datasets, synthesizes raw data, and allows for third party customizations of the software. PlaniSphere uses NASA World Wind to access remote data and map servers using Web Map Service (WMS) as the underlying protocol that supports service-oriented architecture (SOA). The results show that PlaniSphere can aggregate and parses files that reside in local storage and conforms to the following formats: GeoTIFF, ESRI shape files, and KML. Spatial data retrieved using WMS from the Internet can create geospatial data sets (map data) from multiple sources, regardless of who the data providers are. The plug-in function of this framework can be expanded for wider uses, such as aggregating and fusing geospatial data from different data sources, by providing customizations to serve future uses, which the capacity of the commercial ESRI ArcGIS software is limited to add libraries and tools due to its closed-source architectures and proprietary data structures. Analysis and increasing availability of geo-referenced data may provide an effective way to manage spatial information by using large-scale storage, multidimensional data management, and Online Analytical Processing (OLAP) capabilities in one system.

Join and spilt TCP for SDN networks: Architecture, implementation, and evaluation

With the advent of SDN technologies, fine-granular steering and dynamic controlling of network flows can be possible. Such features can be appropriately utilized to enhance the network performance. In this paper, we present a composite system-level implementation framework for aggregation and splitting of TCP flows. Our framework is featured to work in an end-user agnostic manner that doesn’t involve intervention or modification from the end-user.While most existing works are focused either on aggregation-only or split-only frameworks, we propose a more practical ‘join-and-split’ working prototype using SDN. To the best of our knowledge, for the first time, we implement a composite join-and-split TCP architecture that uses efficient mechanisms to synchronize different join and split network points. We have used a ‘linked-ACK’ mechanism to preserve the end-to-end semantics of flows that are separated by the join and split network points. With extensive performance evaluation study, we show the improved performance achieved by our framework in both wired and wireless environments. We describe the implementation of aggregation/splitting of TCP flows intuitively through state diagram. We are hopeful that this paper serves as system-level guide for the research community.

NR Wide Bandwidth Operations

The 3rd Generation Partnership Project (3GPP) is in the process of developing the next generation radio access technology, named New Radio (NR), which will be proposed as a candidate technology for IMT-2020. This article outlines the wide bandwidth operation of NR, among other new features being considered, based on the up-to-date discussions and decisions made in 3GPP standardization meetings. The much wider channel bandwidth of NR, compared to LTE, enables more efficient use of resources than the existing carrier aggregation framework at lower control overhead. The support of multiple sub-carrier spacing options allows NR to operate in a wide range of carrier frequency from sub-6 GHz band to mmWave band with appropriate handling of multi-path delay spread and phase noise depending on the carrier frequency. In addition, the introduction of the new bandwidth part concept allows to flexibly and dynamically configure User Equipment's (UE's) operating bandwidth, which will make NR an energy efficient solution despite the support of wide bandwidth. Other NR wideband operation related issues, such as the support of UEs with limited radio frequency (RF) capability and frequency domain resource indexing, are also explained in this article.

A Framework of Residential Demand Aggregation With Financial Incentives

The development of intelligent demand-side management with automatic control enables a large amount of residential demands to provide efficient demand-side ancillary services for load serving entities. In this paper, we introduce the concept of a comfort indicator, present an advanced reward system, and finally propose a framework for aggregating residential demands enrolled in incentive-based demand response (DR) programs. The proposed framework not only allocates load serving entities' demand reduction requests among residential appliances quickly and efficiently without affecting residents' comfort levels but also rewards residential consumers based on their actual participation. Also, since the framework is designed with the practical considerations of simplicity and efficiency, it can be utilized as a quick implementation for existing pilot development works. The effectiveness and merit of this framework are demonstrated and discussed in the comparison studies with conventional incentive-based DR.

A Sample Aggregation Approach to Experiences Replay of Dyna-Q Learning

In a complex environment, the learning efficiency of reinforcement learning methods always decreases due to large-scale or continuous spaces problems, which can cause the well-known curse of dimensionality. To deal with this problem and enhance learning efficiency, this paper introduces an aggregation method by using framework of sample aggregation based on Chinese restaurant process (CRP), named FSA-CRP, to cluster experiential samples, which is represented by quadruples of the current state, action, next state, and the obtained reward. In addition, the proposed algorithm applies a similarity estimation method, the MinHash method, to calculate the similarity between samples. Moreover, to improve the learning efficiency, the experience sharing Dyna learning algorithm based on samples/clusters prediction method is proposed. While an agent learns the value function of the current state, it acquires clustering results, the value functions of the sample merge with the original as the updated value function of the cluster. In indirect learning (planning) for the Dyna-Q, a learning agent looks for the most likely branches of the constructed FSA-CRP model to raise up learning efficiency. The most likely branches will be selected by an improved action/sample selection algorithm. The algorithm applies the probability that the sample appears in the cluster to select simulated experiences for indirect learning. To verify the validity and applicability of the proposed method, experiments are conducted on a simulated maze and a cart-pole system. The results demonstrate that the proposed method can effectively accelerate the learning process.

Towards Quality Aware Information Integration in Distributed Sensing Systems

In this paper, we present GDA, a generalized decision aggregation framework that integrates information from distributed sensor nodes for decision making in a resource efficient manner. Different from traditional approaches, our proposed GDA framework is able to not only estimate the reliability of each sensor, but also take advantage of its confidence information, and thus achieves higher decision accuracy. Targeting generalized problem domains, our framework can naturally handle the scenarios where different sensor nodes observe different sets of events whose numbers of possible classes may also be different. GDA also makes no assumption about the availability level of ground truth label information, while being able to take advantage of any if present. For these reasons, our approach can be applied to a much broader spectrum of sensing scenarios. In this paper, we also propose two extensions of the GDA framework, i.e., incremental GDA (I-GDA) and parallel GDA (P-GDA) to deal with streaming and large-scale data. The advantages of our proposed methods are demonstrated through both theoretic analysis and extensive experiments.

Re‐ADP: Real‐Time Data Aggregation with Adaptive ω‐Event Differential Privacy for Fog Computing

In the Internet of Things (IoT), aggregation and release of real‐time data can often be used for mining more useful information so as to make humans lives more convenient and efficient. However, privacy disclosure is one of the most concerning issues because sensitive information usually comes with users in aggregated data. Thus, various data encryption technologies have emerged to achieve privacy preserving. These technologies may not only introduce complicated computing and high communication overhead but also do not work on the protection of endless data streams. Considering these challenges, we propose a real‐time stream data aggregation framework with adaptive ω‐event differential privacy (Re‐ADP). Based on adaptive ω‐event differential privacy, the framework can protect any data collected by sensors over any dynamic ω time stamp successively over infinite stream. It is designed for the fog computing architecture that dramatically extends the cloud computing to the edge of networks. In our proposed framework, fog servers will only send aggregated secure data to cloud servers, which can relieve the computing overhead of cloud servers, improve communication efficiency, and protect data privacy. Finally, experimental results demonstrate that our framework outperforms the existing methods and improves data availability with stronger privacy preserving.

Load Forecasting via Diversified State Prediction in Multi-Area Power Networks

Load forecasting has a pivotal role in operating electric utilities. This paper proposes a learning-based tool for short-term load forecasting via first predicting the state parameters of the grid and then delineating load forecasts as functions of the predicted state parameters. Such indirect load forecasting via predicting state parameters leads to improved forecast quality due to the inherent diversity in predictions for state parameters. Specifically, due to the strong inter-connectivity among network components, some state parameters are shared by multiple substations. Hence, allowing each substation to provide local predictions for its associated state parameters based on its local dynamics enables providing multiple predictions for each shared state parameter, leading to a diversified set of predictions for it. Our analysis shows that the proposed aggregation framework provides a prediction for each state parameter such that the quality of this prediction equals to that of the best local prediction provided for that state parameter. This implies that this framework can identify and track the best local prediction for each state parameter without requiring any knowledge about network dynamics.

A New Platform for Automatic Bottom-Up Electric Load Aggregation

In this paper, a new virtual framework for load aggregation in the context of the liberalized energy market is proposed. Since aggregation is managed automatically through a dedicated platform, the purchase of energy can be carried out without intermediation as it happens in peer-to-peer energy transaction models. Differently from what was done before, in this new framework, individual customers can join a load aggregation program through the proposed aggregation platform. Through the platform, their features are evaluated and they are clustered according to their reliability and to the width of range of regulation allowed. The simulations show the deployment of an effective clustering and the possibility to meet the target power demand at a given hour according to each customer’s availability.

Engineering multiple topological phases in nanoscale Van der Waals heterostructures: realisation of α-antimonene

Van der Waals heterostructures have recently been identified as providing many opportunities to create new two-dimensional materials, and in particular to produce materials with topologically-interesting states. Here we show that it is possible to create such heterostructures with multiple topological phases in a single nanoscale island. We discuss their growth within the framework of diffusion-limited aggregation, the formation of moiré patterns due to the differing crystallographies of the materials comprising the heterostructure, and the potential to engineer both the electronic structure as well as local variations of topological order. In particular we show that it is possible to build islands which include both the hexagonal β- and rectangular α-forms of antimonene, on top of the topological insulator α-bismuthene. This is the first experimental realisation of α-antimonene, and we show that it is a topologically non-trivial material in the quantum spin Hall class.