Rebalance Research Articles

Adaptive grouped balanced binary tree based multi user secure data transmission on cloud

Adaptive grouped balanced binary tree based multi user secure data transmission on cloud

Charging Scheduling Method for Wireless Rechargeable Sensor Networks Based on Energy Consumption Rate Prediction for Nodes.

With the development of the IoT, Wireless Rechargeable Sensor Networks (WRSNs) derive more and more application scenarios with diverse performance requirements. In scenarios where the energy consumption rate of sensor nodes changes dynamically, most existing charging scheduling methods are not applicable. The incorrect estimation of node energy requirement may lead to the death of critical nodes, resulting in missing events. To address this issue, we consider both the spatial imbalance and temporal dynamics of the energy consumption of the nodes, and minimize the Event Missing Rate (EMR) as the goal. Firstly, an Energy Consumption Balanced Tree (ECBT) construction method is proposed to prolong the lifetime of each node. Then, we transform the goal into Maximizing the value of the Evaluation function of each node's Energy Consumption Rate prediction (MEECR). Afterwards, the setting of the evaluation function is explored and the MEECR is further transformed into a variant of the knapsack problem, namely "the alternating backpack problem", and solved by dynamic programming. After predicting the energy consumption rate of the nodes, a charging scheduling scheme that meets the Dual Constraints of Nodes' energy requirements and MC's capability (DCNM) is developed. Simulations demonstrate the advantages of the proposed method. Compared to the baselines, the EMR was reduced by an average of 35.2% and 26.9%.

Correction to “Sharp threshold for embedding balanced spanning trees in random geometric graphs”

Correction to “Sharp threshold for embedding balanced spanning trees in random geometric graphs”

On the maximum value of the stairs2 index

Measures of tree balance play an important role in different research areas such as mathematical phylogenetics or theoretical computer science. The balance of a tree is usually quantified in a single number, called a balance or imbalance index, and several such indices exist in the literature. Here, we focus on the stairs2 balance index for rooted binary trees, which was first introduced in the context of viral phylogenetics but has not been fully analyzed from a mathematical viewpoint yet. While it is known that the caterpillar tree uniquely minimizes the stairs2 index for all leaf numbers and the fully balanced tree uniquely maximizes the stairs2 index for leaf numbers that are powers of two, understanding the maximum value and maximal trees for arbitrary leaf numbers has been an open problem in the literature. In this note, we fill this gap by showing that for all leaf numbers, there is a unique rooted binary tree maximizing the stairs2 index. Additionally, we obtain recursive and closed expressions for the maximum value of the stairs2 index of a rooted binary tree with n leaves.

Optimal randomized algorithms of weakly-balanced multi-branching AND-OR trees

This article treats AND-OR tree computation in terms of query complexity. We are interested in the cases where assignments (inputs) or algorithms are randomized. For the former case, it is known that there is a unique randomized assignment achieving the distributional complexity of balanced trees. On the other hand, the dual problem has the opposite result; the optimal randomized algorithms for balanced trees are not unique. We extend the latter study on randomized algorithms to weakly-balanced trees, and see that the uniqueness still fails.

Sharp threshold for embedding balanced spanning trees in random geometric graphs

AbstractA rooted tree is balanced if the degree of a vertex depends only on its distance to the root. In this paper we determine the sharp threshold for the appearance of a large family of balanced spanning trees in the random geometric graph . In particular, we find the sharp threshold for balanced binary trees. More generally, we show that all sequences of balanced trees with uniformly bounded degrees and height tending to infinity appear above a sharp threshold, and none of these appears below the same value. Our results hold more generally for geometric graphs satisfying a mild condition on the distribution of their vertex set, and we provide a polynomial time algorithm to find such trees.

EPSMR: An efficient privacy-preserving semantic-aware multi-keyword ranked search scheme in cloud

Traditional privacy-preserving search schemes have commonly used the TF-IDF model, which relies on keyword frequency statistics but neglects the semantic relationships between and documents. In recent years, a few secure multi-keyword ranked search schemes over encrypted cloud data have considered the data semantics. However, the search efficiency of these schemes can still be improved. This paper presents an efficient privacy-preserving semantic-aware multi-keyword ranked search scheme (EPSMR) over encrypted cloud data. EPSMR utilizes the LDA topic model to discover hidden semantic information in documents and convert documents into vector representations. vector encryption by using the secure inner product is employed to implement the privacy-preserving semantic relevance score computation between search keywords and documents. To achieve efficient multi-keyword searches, the bisecting k-means algorithm and search tree structure are introduced to design a novel ω-way balanced search tree index which benefits finding search results quickly. to further improve the search efficiency, a filtering-threshold lookup table is presented, which can be used to prune unqualified sub-trees in advance. The experimental results show the superior performance of the proposed scheme compared to existing schemes in terms of both semantic search precision and search time cost.

BT-CNN: a balanced binary tree architecture for classification of brain tumour using MRI imaging.

Deep learning is a very important technique in clinical diagnosis and therapy in the present world. Convolutional Neural Network (CNN) is a recent development in deep learning that is used in computer vision. Our medical investigation focuses on the identification of brain tumour. To improve the brain tumour classification performance a Balanced binary Tree CNN (BT-CNN) which is framed in a binary tree-like structure is proposed. It has a two distinct modules-the convolution and the depthwise separable convolution group. The usage of convolution group achieves lower time and higher memory, while the opposite is true for the depthwise separable convolution group. This balanced binarty tree inspired CNN balances both the groups to achieve maximum performance in terms of time and space. The proposed model along with state-of-the-art models like CNN-KNN and models proposed by Musallam et al., Saikat et al., and Amin et al. are experimented on public datasets. Before we feed the data into model the images are pre-processed using CLAHE, denoising, cropping, and scaling. The pre-processed dataset is partitioned into training and testing datasets as per 5 fold cross validation. The proposed model is trained and compared its perforarmance with state-of-the-art models like CNN-KNN and models proposed by Musallam et al., Saikat et al., and Amin et al. The proposed model reported average training accuracy of 99.61% compared to other models. The proposed model achieved 96.06% test accuracy where as other models achieved 68.86%, 85.8%, 86.88%, and 90.41% respectively. Further, the proposed model obtained lowest standard deviation on training and test accuracies across all folds, making it invariable to dataset.

Modeling of thermal energy saving in commercial buildings of Australia by balanced tree growth optimizer

Low-energy cases can be designed more efficiently by using analytical optimization. The non-linear thermal performance of buildings has led to the development of optimization techniques based on simulation. In building optimization, it is important to achieve superior solutions while minimizing calculation expenses. This study aims to optimize an Australian office building using the Balanced Tree Growth Optimizer (BTGO). It has resulted that more than 11.7 % of energy can be saved by the optimization process and also some energy-saving measures. A comparison of the utilized algorithm with benchmark algorithms including the Nelder-Mead method, hybrid Particle Swarm Optimization, Hooke-Jeeves, and Ant Colony Optimization for continuous domain showed that the BTGO can achieve better solutions and needs less computational time.

The quandary of sources and sinks of CO2 efflux in tree stems-new insights and future directions.

Stem respiration (RS) substantially contributes to the return of photo assimilated carbon to the atmosphere and, thus, to the tree and ecosystem carbon balance. Stem CO2 efflux (ECO2) is often used as a proxy for RS. However, this metric has often been challenged because of the uncertain origin of CO2 emitted from the stem due to post-respiratory processes. In this Insight, we (i) describe processes affecting the quantification of RS, (ii) review common methodological approaches to quantify and model RS and (iii) develop a research agenda to fill the most relevant knowledge gaps that we identified. Dissolution, transport and accumulation of respired CO2 away from its production site, reassimilation of respired CO2 via stem photosynthesis and the enzyme phosphoenolpyruvate carboxylase, axial CO2 diffusion in the gas phase, shifts in the respiratory substrate and non-respiratory oxygen (O2) consumption are the most relevant processes causing divergence between RS and measured stem gas exchange (ECO2 or O2 influx, IO2). Two common methodological approaches to estimate RS, namely the CO2 mass balance approach and the O2 consumption technique, circumvent some of these processes but have yielded inconsistent results regarding the fate of respired CO2. Stem respiration modelling has recently progressed at the organ and tree levels. However, its implementation in large-scale models, commonly operated from a source-driven perspective, is unlikely to reflect adequate mechanisms. Finally, we propose hypotheses and approaches to advance the knowledge of the stem carbon balance, the role of sap pH on RS, the reassimilation of respired CO2, RS upscaling procedures, large-scale RS modelling and shifts in respiratory metabolism during environmental stress.

A survey on optimization-based approaches to Dynamic Centralized Group Key management

Abstract—In secure multicast communication, only authorized group members are able to receive the transmitted data. Key generation, distribution, and management are all carried out by a single entity (called the Key Server) in the centralized system. The main challenges of a dynamic centralized group key management scheme include scalability overhead, rekeying overhead, storage overhead, maintaining forward and backward secrecy, etc. Logical Key Hierarchy (LKH), one of the key management methods, uses a tree structure to manage keys to share with participants. Due to the fact that members may join or exit the group at any time during communication, a crucial problem in centralized group key management (CGKM) is minimizing rekeying costs while maintaining a balanced tree. The majority of CGKM methods effectively update the group key by modifying cryptographic techniques or based on logical/heuristic arguments. One of the efficient approaches is based on an optimization model with the two mentioned important objectives. In this paper, we conduct a survey on optimization-based approaches to dynamic CGKM, which can be divided into two categories based on logical/heuristic arguments and a rigorous mathematical optimization model.

Anatomical and physiological consequences of beech leaf disease in Fagus grandifolia L.

AbstractBeech leaf disease (BLD) is a foliar disease of American Beech (Fagus grandifolia L.) causally linked to the nematode Litylenchus crenatae mccannii and has rapidly spread throughout central and northeastern North America. This study aimed to characterize the anatomical and physiological differences between asymptomatic and symptomatic leaves to provide evidence for a mechanistic link between abnormal leaf development associated with BLD and the long‐term decline of affected trees. We found that leaf mass per area (LMA) and leaf thickness were 45% and 249% higher in symptomatic regions, respectively. The difference in leaf thickness was largely attributable to the spongy mesophyll being 410% thicker in symptomatic as compared with asymptomatic regions of the leaves, but palisade mesophyll and abaxial epidermal tissues were also thicker in symptomatic regions. While major vein density was not significantly different, minor vein density was significantly lower in symptomatic regions, suggesting that the effects on leaf development occur after the formation and initial development of first‐ and second‐order veins. Stomatal density was also lower in symptomatic leaves. Maximum photosynthetic rates were approximately 61% lower in symptomatic leaves and respiration rate increased as the percentage of affected leaf tissue increased. Collectively, our data show reduced photosynthetic capacity, increased respiration rates, and higher leaf construction costs, which will likely have a negative, long‐term impact on the carbon balance of trees affected by BLD.

Hierarchical Cut Labelling - Scaling Up Distance Queries on Road Networks

Answering the shortest-path distance between two arbitrary locations is a fundamental problem in road networks. Labelling-based solutions are the current state-of-the-arts to render fast response time, which can generally be categorised into hub-based labellings, highway-based labellings, and tree decomposition labellings. Hub-based and highway-based labellings exploit hierarchical structures of road networks with the aim to reduce labelling size for improving query efficiency. However, these solutions still result in large search spaces on distance labels at query time, particularly when road networks are large. Tree decomposition labellings leverage a hierarchy of vertices to reduce search spaces over distance labels at query time, but such a hierarchy is generated using tree decomposition techniques, which may yield very large labelling sizes and slow querying. In this paper, we propose a novel solution hierarchical cut 2-hop labelling (HC2L) to address the drawbacks of the existing works. Our solution combines the benefits of hierarchical structures from both perspectives - reduce the size of a distance labelling at preprocessing time and further reduce the search space on a distance labelling at query time. At its core, we propose a new hierarchy, balanced tree hierarchy, which enables a fast, efficient data structure to reduce the size of distance labelling and to select a very small subset of labels to compute the shortest-path distance at query time. To speed up the construction process of HC2L, we further propose a parallel variant of our method, namely HC2L^p. We have evaluated our solution on 10 large real-world road networks through extensive experiments. The results show that our method is 1.5-4 times faster in terms of query processing while being comparable in terms of labelling construction time and achieving up to 60% smaller labelling size compared to the state-of-the-art approaches.

Verifiable fuzzy keyword search supporting sensitive information hiding for data sharing in cloud-assisted e-healthcare systems

Nowadays, cloud-assisted e-healthcare systems are playing a more and more important role in intelligent medical services. By uploading Electronic Medical Records (EMRs) to the cloud, users can facilitate data sharing and decrease local data management overhead. Before EMRs are outsourced to the cloud, the sensitive information in them is usually encrypted for protecting the privacy. Though the techniques of traditional searchable encryption could achieve the retrieval of ciphertext, it results in EMRs unable to be shared with external researchers. Moreover, the existing searchable encryption schemes for cloud-assisted e-healthcare systems rarely consider fault-tolerant search and result verification. To address these issues, this paper proposes a verifiable fuzzy keyword search scheme supporting sensitive information hiding for data sharing in cloud-assisted e-healthcare systems. We encrypt sensitive information and share the rest information in EMRs among all users. We improve the approaches of keyword transformation to support the type of keyword in EMRs and realize fuzzy keyword search. For purpose of accomplishing the public verification, we adopt the BLS signature algorithm to generate two kinds of verification tags. We also generate the auxiliary match information to realize condition search. To enhance search efficiency, we build a secure index based on the balanced binary tree. In addition, the proposed scheme can also achieve dynamic update in real-time. We analyze the security and implement a series of experiments for evaluating the effectiveness of this scheme. The experimental results illustrate our scheme could reach high accuracy.

ANALYSIS OF WORD SEARCH ALGORITHMS IN THE DICTIONARIES OF MACHINE TRANSLATION SYSTEMS FOR ARTIFICIAL LANGUAGES

The paper is devoted to the development of an organizational model of the machine translation system of artificial languages. The main goal is the analysis of word search algorithms in the dictionary, which are significant elements of the developed machine translation system at the stage of improvement of new dictionaries created on the basis of already existing dictionaries. In the course of the work was developed a model of the machine translation system, created dictionaries based on texts and based on already existing dictionaries using augmentation methods such as back translation and crossover; improved dictionary based on algorithms of n-grams, Knuth-Morris-Pratt and word search in the text (such as binary search, tree search, root decomposition search). In addition, the work implements the possibility of using the prepared dictionary for translation. The obtained results can improve existing systems of machine translation of the text of artificial languages. Namely, to reduce the operating time by approximately 20 times when switching from the balanced tree algorithm to other logarithmic algorithms. The practical significance of this work is the analysis and improvement of text augmentation algorithms using algorithms of binary search, hashes, search tree, and root decomposition.

A balanced communication-avoiding support vector machine decision tree method for smart intrusion detection systems

The Internet of Things field has created many challenges for network architectures. Ensuring cyberspace security is the primary goal of intrusion detection systems (IDSs). Due to the increases in the number and types of attacks, researchers have sought to improve intrusion detection systems by efficiently protecting the data and devices connected in cyberspace. IDS performance is essentially tied to the amount of data, data dimensionality, and security features. This paper proposes a novel IDS model to improve computational complexity by providing accurate detection in less processing time than other related works. The Gini index method is used to compute the impurity of the security features and refine the selection process. A balanced communication-avoiding support vector machine decision tree method is performed to enhance intrusion detection accuracy. The evaluation is conducted using the UNSW-NB 15 dataset, which is a real dataset and is available publicly. The proposed model achieves high attack detection performance, with an accuracy of approximately 98.5%.

Large-Scale Interactive Recommendation With Tree-Structured Reinforcement Learning

Although reinforcement learning (RL) techniques are regarded as promising solutions for interactive recommender systems (IRS), such solutions still face three main challenges, namely, i) time inefficiency when handling large discrete action space in IRS, ii) inability to deal with the cold-start scenarios in IRS, iii) data inefficiency during training the RL-based methods. To tackle these challenges, we propose a generic tree-structured RL framework taking both policy-based and value-based approaches into consideration. We propose to construct a balanced tree over representations of the items, such that picking an item is formulated as seeking a suitable path from the root to a leaf node in the balanced tree, which dramatically reduces the time complexity of item recommendation. Further, for cold-start scenarios where prior information of the items is unavailable, we initialize a random balanced tree as the starting point and then refine the tree structure based on the learned item representations. Besides, we also incorporate a user modeling component to explicitly model the environment, which can be utilized in the training phase to improve data efficiency. Extensive experiments on two real-world datasets are conducted and demonstrate that our framework can achieve superior recommendation performance and provide time and data efficiency improvement over state-of-the-art methods in both warm-start and cold-start IRS scenarios.

Who are the Best Adopters? User Selection Model for Free Trial Item Promotion

With the increasingly fierce market competition, the free trial has been widely applied as an effective incentive strategy to attract users and promote products. By providing opportunities to experience goods without charge, a free trial offers adopters more direct contact with the products and thus raises their willingness to buy. However, as the key point in the promotion process, how to select proper adopters is rarely explored. Empirically winnowing users by their static demographic attributes is feasible but less effective due to the lack of consideration of personalized demands. In this work, we propose SMILE – a tailored free trial user selection model for finding the best adopters promptly. Based on the reinforcement learning (RL) technique, SMILE can consider long-run profits and rely on user-item interactions to suggest actions. Besides, since selecting adopters from the large user candidates set is time-consuming, we design a balanced tree structure that reformulates the user action space. The experimental analysis on three datasets demonstrates the proposed model's superiority and elucidates why reinforcement learning and tree structure can improve performance. Our study shows technical feasibility of constructing a more robust and intelligent user selection model and guides for investigating more marketing promotion strategies.

A Single-TSV and Single-DCDL Approach for Skew Compensation of Multi-Dies Clock Synchronization in 3-D-ICs

Existing methods used for the clock distribution of multiple dies employ a balanced tree structure to minimize the impact of the within-die process and loading variations. No topology for die-to-die clock skew compensation of more than two dies has been presented yet. This article presents a novel die-to-die clock skew compensation topology to address these limitations. Unlike existing designs, the proposed topology does not need a phase detector (hence, no dead zone); it only requires one through-silicon via (TSV) to connect a pair of dies and one digitally controlled delay line (DCDL) in each die; thus, there is no skew from extra TSVs and DCDLs. Accordingly, the system has a small chip area and low lock time. The postsynthesis of this work was accomplished in a 65-nm CMOS process. The performance of our design was evaluated theoretically and practically in terms of mismatch/finite resolution of delay lines, buffer mismatch, and TSV delay. Under identical conditions, the residual skew of the proposed design was as low as 13 ps at 1 GHz. This study is the first to obtain the solution for die-to-die clock synchronization of multiple dies (more than two dies) in a three-dimensional (3-D) integrated circuit, while other systems can only support two dies.

On the distance spectrum of generalized balanced trees

For positive integers m 1 , m 2 , … , m h , a generalized balanced tree T ( m 1 , m 2 , … , m h ) is a rooted tree of height h such that every vertex of depth i has m i + 1 children, 0 ≤ i ≤ h − 1 . The distance matrix D ( G ) of a simple connected graph G of order n is an n × n matrix whose ( i , j ) th entry is the distance between i th and j th vertices. A connected graph G is called a k-partitioned transmission regular graph if there exists a vertex partition { V 1 , V 2 , … , V k } of G so that for 1 ≤ i , j ≤ k , and x ∈ V i , ∑ y ∈ V j d ( x , y ) is a constant. Here we show that T ( m 1 , m 2 , … , m h ) is an ( h + 1 ) -partitioned transmission regular graph. We find an ( h + 1 ) × ( h + 1 ) matrix whose largest eigenvalue is the distance spectral radius of T ( m 1 , m 2 , … , m h ) . We obtain the characteristic polynomial of D ( T ( m 1 , m 2 , … , m h ) ) in terms of that of the smaller matrices and give an idea to find the full spectrum. Moreover, we get that D ( T ( m 1 , m 2 , … , m h ) ) has − 2 an eigenvalue with multiplicity at least m 1 ⋯ m h − 1 ( m h − 1 ) and − ( m h + 2 ) ± m h ( m h + 4 ) as eigenvalues with multiplicity at least m 1 ⋯ m h − 2 ( m h − 1 − 1 ) .