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Diagnostic theories are fundamental to information system (IS) practice and are represented as trees. While there are approaches for validating diagnostic trees, these validate the overall performance of the tree rather than identifying ways incorrect diagnoses can occur. It is important to fully validate diagnostic trees because even if the tree gives the correct decision “most of the time,” it is possible for incorrect decisions traveling down little-used branches of the tree to result in catastrophic decisions. In this article, we describe the process of using a variant of q-sorting to validate diagnostic trees. In this methodology, diagnostic trees that independent experts develop are transformed into a quantitative form, and that quantitative form is tested to determine the inter-rater reliability of the individual branches in the tree. The trees are then successively transformed to incrementally test if they branch in the same way. The results help researchers not only identify quality items for use in a diagnostic tree but also facilitate diagnoses of problems with those items and facilitate the reconciliation of discrepant trees by experts. The methodology validates not only the whole tree but also its subparts.

Tree fruit architecture results from combination of the training system and pruning and thinning processes across multiple growth and development years. Further, the tree fruit architecture contributes to the light interception and improves tree growth, fruit quality, and fruit yield, in addition to easing the process of orchard management and harvest. Currently tree architectural traits are measured manually by researchers or growers, which is labor-intensive and time-consuming. In this study, the remote sensing techniques were evaluated to phenotype critical architectural traits with the final goal to assist tree fruit breeders, physiologists and growers in collecting architectural traits efficiently and in a standardized manner. For this, a consumer-grade red–green–blue (RGB) camera was used to collect apple tree side-images, while an unmanned aerial vehicle (UAV) integrated RGB camera was programmed to image tree canopy at 15 m above ground level to evaluate multiple tree fruit architectures. The sensing data were compared to ground reference data associated with tree orchard blocks within three training systems (Spindle, V-trellis, Bi-axis), two rootstocks (‘WA 38 trees grafted on G41 and M9-Nic29) and two pruning methods (referred as bending and click pruning). The data were processed to extract architectural features from ground-based 2D images and UAV-based 3D digital surface model. The traits extracted from sensing data included box-counting fractal dimension (DBs), middle branch angle, number of branches, trunk basal diameter, and tree row volume (TRV). The results from ground-based sensing data indicated that there was a significant (P

The multipoint dynamic aggregation (MPDA) problem of the multirobot system is of great significance for its real-world applications such as bush fire elimination. The problem is to design the optimal plan for a set of heterogeneous robots to complete some geographically distributed tasks collaboratively. In this article, we consider the dynamic version of the problem, where new tasks keep appearing after the robots are dispatched from the depot. The dynamic MPDA problem is a complicated optimization problem due to several characteristics, such as the collaboration of robots, the accumulative task demand, the relationships among robots and tasks, and the unpredictable task arrivals. In this article, a new model of the problem considering these characteristics is proposed. To solve the problem, we develop a new genetic programming hyperheuristic (GPHH) method to evolve reactive coordination strategies (RCSs), which can guide the robots to make decisions in real time. The proposed GPHH method contains a newly designed effective RCS heuristic template to generate the execution plan for the robots according to a GP tree. A new terminal set of features related to both robots and tasks and a cluster filter that assigns the robots to urgent tasks are designed. The experimental results show that the proposed GPHH significantly outperformed the state-of-the-art methods. Through further analysis, useful insights such as how to distribute and coordinate robots to execute different types of tasks are discovered.

With the popularization of location-based services (LBS), encryption techniques have been utilized to protect data security when outsourcing LBS to cloud. However, existing schemes only consider spatial range search or keyword search, while expressive and practical search over encrypted LBS data is still a challenging problem. In this paper, we introduce PrivSTL, a privacy-preserving spatio-temporal keyword search framework over the encrypted LBS data based on attribute-based encryption and linear encryption. It allows mobile users to submit LBS query with spatial range, time interval and Boolean keyword expression, and provides accurate and authorized search by matching these query conditions and also the access policy. Then we introduce an extended scheme PrivSTG, which utilizes Geohash to divide the locations into grids, and outsources an encrypted index tree to cloud servers. PrivSTG improves the service efficiency by searching only over the ciphertexts in the surrounding grids of mobile user. Finally, we analyze the security of PrivSTL against chosen-plaintext, chosen-keyword and outside keyword-guessing attacks in generic bilinear group model, and show that PrivSTL guarantees the spatio-temporal keyword profile privacy, and also protects the query privacy of mobile user. The experimental results indicate that our scheme is practical and efficient for outsourced LBS.

The proposed work aims to solve data sparsity problem in the recommendation system. It handles two-level pre-processing techniques to reduce the data size at the item level. Additional resources like items genre, tag, and time are added to learn and analyse the behaviour of the user preferences in-depth. The advantage of the proposed method is to recommend the item, based on user interest pattern and avoid recommending the outdated items. User information are grouped based on similar item genre and tag feature. This effectively handle overlapping conditions that exist on item’s genre, as it has more than one genre at initial level. Further, based on time, it analyses the user non-static interest. Overall it reduces the dimensions which is an initial way to prepare data, to analyse hidden pattern. To enhance the performance, the proposed method utilized Apache’s spark Mllib FP-Growth and association rule mining approach in a distributed environment. To reduce the computation cost of constructing tree in FP-Growth, the candidate data set is stored in matrix form. The experiments were conducted using MovieLens data set. The observed results shows that the proposed method achieves 4% increase in accuracy when compared to earlier methods.

Currently, there are many simultaneous localization and mapping (SLAM) algorithms for 3-D light detection and ranging (lidar) relying on features such as planes and edges. However, if a robot needs to work in a rubber forest, these simple features can be unstable due to the complexity of the environment. To address this problem, in this article, we propose the first SLAM system to make a deep adaptive adjustment for the particularity of the rubber-tapping robot and the rubber forest environment. In our article, tree trunks are used to construct a set of sparse maps called trunk atlas, which contain environmental information for localization and trunk position for rubber tapping. To ensure the quality of feature extraction, density field correction and multicriteria trunk detection are proposed. Due to the sparsity and stability of the trunk atlas, our SLAM system can ensure real-time performance. Experiments show that our lidar-only SLAM system can achieve nearly the same performance as state-of-the-art inertial-measurement-unit-aided algorithms in terms of runtime and localization accuracy. In addition, our trunk atlas also takes up less memory for storage than other algorithms. More specifically, it takes less than 50 kB to store a map of an area of approximately 5000 m $^2$ .

We present a generalization of a combinatorial result by Aggarwal et al. (1989) on a linear-time algorithm that selects a constant fraction of leaves, with pairwise disjoint neighborhoods, from a binary tree embedded in the plane. This result of Aggarwal et al. (1989) is essential to the linear-time framework, which they also introduced, that computes certain Voronoi diagrams of points with a tree structure in linear time. An example is the diagram computed while updating the Voronoi diagram of points after deletion of one site. Our generalization allows that only a fraction of the tree leaves is considered, and it is motivated by research on linear time construction algorithms for Voronoi diagrams of non-point sites. We are given a plane tree T of n leaves, m of which have been marked, and each marked leaf is associated with a neighborhood (a subtree of T ) such that any two topologically consecutive marked leaves have disjoint neighborhoods. We show how to select in linear time a constant fraction of the marked leaves having pairwise disjoint neighborhoods.

In the field of information visualization, the concept of "tasks" is an essential component of theories and methodologies for how a visualization researcher or a practitioner understands what tasks a user needs to perform and how to approach the creation of a new design. In this article, we focus on the collection of tasks for tree visualizations, a common visual encoding in many domains ranging from biology to computer science to geography. In spite of their commonality, no prior efforts exist to collect and abstractly define tree visualization tasks. We present a literature review of tree visualization articles and generate a curated dataset of over 200 tasks. To enable effective task abstraction for trees, we also contribute a novel extension of the Multi-Level Task Typology to include more specificity to support tree-specific tasks as well as a systematic procedure to conduct task abstractions for tree visualizations. All tasks in the dataset were abstracted with the novel typology extension and analyzed to gain a better understanding of the state of tree visualizations. These abstracted tasks can benefit visualization researchers and practitioners as they design evaluation studies or compare their analytical tasks with ones previously studied in the literature to make informed decisions about their design. We also reflect on our novel methodology and advocate more broadly for the creation of task-based knowledge repositories for different types of visualizations. The Supplemental Material, which can be found on the Computer Society Digital Library at http://doi.ieeecomputersociety.org/10.1109/TVCG.2021.3064037, will be maintained on OSF: https://osf.io/u5ehs/.

Traditional searchable encryption schemes based on the Term Frequency-Inverse Document Frequency (TF-IDF) model adopt the presence of keywords to measure the relevance of documents to queries, which ignores the latent semantic meanings that are concealed in the context. Latent Dirichlet Allocation (LDA) topic model can be utilized for modeling the semantics among texts to achieve semantic-aware multi-keyword search. However, the LDA topic model treats queries and documents from the perspective of topics, and the keywords information is ignored. In this paper, we propose a privacy-preserving searchable encryption scheme based on the LDA topic model and the query likelihood model. We extract the feature keywords from the document using the LDA-based Information Gain (IG) and Topic Frequency-Inverse Topic Frequency (TF-ITF) model. With feature keyword extraction and the query likelihood model, our scheme can achieve a more accurate semantic-aware keyword search. A special index tree is used to enhance search efficiency. The secure inner product operation is utilized to implement the privacy-preserving ranked search. The experiments on real-world datasets demonstrate the effectiveness of our scheme.

Nurturing venture-capital-backed, high-growth entrepreneurship has been strongly promoted as an effective means to achieve local development in impoverished places. Yet growing evidence suggests th...

Music Information Retrieval (MIR) is the task of extracting high-level information, such as genre, artist or instrumentation from music. Genre classification is an important and rapidly evolving research area of MIR. To date, only a small amount of research work has been done on the automatic genre classification of Nigerian songs. Hence, this study presents a new music dataset, namely the ORIN dataset, consisting of only Nigerian songs. The study dataset contains 478 Nigerian traditional songs from five genres: fuji, juju, highlife, waka and apala. The timbral texture and tempo features were mined from 30-second segments of each song using the Librosa Python library. For genre classification, the ORIN datasets was trained on 4 different classifiers- k-Nearest Neighbhour, Support Vector Machine, eXtreme Gradient Boosting (XGBoost) and Random Forest- with 85–15 train-test splits. The results obtained for the five different genres indicates that XGBoost classifier is a better model, having the highest accuracy of 81.94% and recall of 84.57%. This study uses the global mean (Tree SHAP) method to determine feature importance and impact on the classification model. Further analysis on the individual genres found some nearness in the timbral properties between some of the genres. This analysis was confirmed by human observation.

The advance of smart eHealthcare has greatly enabled patients to enjoy high-quality healthcare services, e.g., disease prediction. Meanwhile, to support the dramatic increase of healthcare data, healthcare centers often outsource the on-premises data to the cloud. However, since the healthcare data contain some sensitive information and the cloud server is not fully trusted, healthcare centers need to encrypt the data before outsourcing them. Unfortunately, data encryption inevitably hinders some advanced data applications like the similarity range query. Although many studies on similarity range queries over encrypted data have been reported, most of them still have some limitations in security, efficiency, accuracy, and practicality. Aiming at this challenge, in this paper, we propose an efficient, privacy-preserving, and practical similarity range query (EPSim) scheme. Specifically, we first present a modified asymmetric scalar-product-preserving encryption (ASPE) scheme and prove its security. Then, we introduce a Quadsector tree to represent the data and employ a filtration condition to design an algorithm for efficient similarity range queries over the Quadsector tree. Finally, we propose our EPSim scheme by integrating the modified ASPE scheme and Quadsector tree. Detailed security analysis and performance evaluations indicate our scheme is really secure and efficient in similarity range queries.

Evolutionary multiobjective clustering (MOC) algorithms have shown promising potential to outperform conventional single-objective clustering algorithms, especially when the number of clusters k is not set before clustering. However, the computational burden becomes a tricky problem due to the extensive search space and fitness computational time of the evolving population, especially when the data size is large. This article proposes a new, hierarchical, topology-based cluster representation for scalable MOC, which can simplify the search procedure and decrease computational overhead. A coarse-to-fine-trained topological structure that fits the spatial distribution of the data is utilized to identify a set of seed points/nodes, then a tree-based graph is built to represent clusters. During optimization, a bipartite graph partitioning strategy incorporated with the graph nodes helps in performing a cluster ensemble operation to generate offspring solutions more effectively. For the determination of the final result, which is underexplored in the existing methods, the usage of a cluster ensemble strategy is also presented, whether k is provided or not. Comparison experiments are conducted on a series of different data distributions, revealing the superiority of the proposed algorithm in terms of both clustering performance and computing efficiency.

Underwater wireless acoustic networks (UWANs) take advantage of acoustic communication to enable many distinct applications. The peculiar features of underwater acoustic channels, such as long propagation delay, high bit error rates, and severely limited bandwidth, make the use of medium access control (MAC) solutions designed for terrestrial radio networks inefficient in UWANs. This paper proposes UW-SEEDEX, a MAC protocol for UWANs that employs random time slot schedules, created from seeds, to avoid collisions. After exchanging seeds, nodes can know other's entire schedules, allowing them to then better plan their transmissions. Simulations evaluate how each of UW-SEEDEX's parameter affects its performance in metrics such as end-to-end delay, energy consumption, transmissions per data reception, and reception rate using different test scenarios. Simulations also show that UW-SEEDEX can perform better than other MAC solutions, delivering more messages than protocols such as UW-Aloha and Slotted FAMA and reduced, on average, up to three times the number of transmissions required for each message reception in networks with grid topologies. UW-SEEDEX presented reception rates close to 100%, low energy cost, and fewer transmissions per data reception. Our code is available at https://gitlab.com/epmcj/ns-3-dev/-/tree/new-uan-mac-protocols.

The design of large devices suggests fundamental and efficient innovation in Field Programmable Gate Array (FPGA) interconnect structure to improve power consumption, density and performance. Mesh-based FPGA architectures are generally designed to maximize logic utilization and to ensure layout scalability whereas Tree-based FPGA architectures are designed to increase interconnect utilization. In this paper, we propose a new Mesh of Trees (MoTs) FPGA architecture where we try to benefit from Mesh of Clusters (MoCs) FPGA layout scalability and Tree-based FPGA efficient intra-cluster interconnect utilization thanks to Butterfly Fat Tree (BFT) topology. In order to attain the efficient use of the proposed routing interconnect resources, we develop Computer-Aided Design (CAD) tools that respond to the target MoTs FPGA features. In addition, we define the power, area and delay metric models according to the proposed architecture criteria. Experimentation results show that MoTs architecture with a cluster arity 32 offers the best trade-off between power, area and delay. Furthermore, we conduct a comparison between MoTs and MoCs FPGAs in general and the best MoTs and MoCs solutions which ensure the best trade-off between power, area and delay in particular. Results show that the best MoTs architecture reduces power, area, delay and energy by an average of 29%, 6%, 40% and 47%, respectively in comparison with the best MoCs architecture.

In this paper, we propose a fighting game AI that selects its actions from the perspective of highlight generation using Monte-Carlo tree search with three highlight cues in the evaluation function. The proposed AI is targeted for being used to generate gameplay in live streaming platforms such as Twitch and YouTube where a large number of spectators watch gameplay to entertain themselves. A gameplay analysis and user study are conducted using FightingICE, a fighting game platform in an international fighting game AI competition. Results show that gameplay generated by two AI players of the proposed method has more promising characteristics than not only gameplay generated according to an existing method but also gameplay by the top two AI players in the 2019 competition and that it is more entertaining than the latter gameplay.

Context based API recommendation is an important way to help developers find the needed APIs effectively and efficiently. For effective API recommendation, we need not only a joint view of both structural and textual code information, but also a holistic view of correlated API usage in control and data flow graph as a whole. Unfortunately, existing API recommendation methods exploit structural or textual code information separately. In this work, we propose a novel API recommendation approach called APIRec-CST (API Recommendation by Combining Structural and Textual code information). APIRec-CST is a deep learning model that combines the API usage with the text information in the source code based on an API Context Graph Network and a Code Token Network that simultaneously learn structural and textual features for API recommendation. We apply APIRec-CST to train a model for JDK library based on 1,914 open-source Java projects and evaluate the accuracy and MRR (Mean Reciprocal Rank) of API recommendation with another 6 open-source projects. The results show that our approach achieves respectively a top-1, top-5, top-10 accuracy and MRR of 60.3%, 81.5%, 87.7% and 69.4%, and significantly outperforms an existing graph-based statistical approach and a tree-based deep learning approach for API recommendation. A further analysis shows that textual code information makes sense and improves the accuracy and MRR. The sensitivity analysis shows that the top-k accuracy and MRR of APIRec-CST are insensitive to the number of APIs to be recommended in a hole. We also conduct a user study in which two groups of students are asked to finish 6 programming tasks with or without our APIRec-CST plugin. The results show that APIRec-CST can help the students to finish the tasks faster and more accurately and the feedback on the usability is overwhelmingly positive.

Multi-order proximity is useful for effective network embedding. In contrast to many previous works that only consider order-level weights, this paper proposes to explore a more expressive node-level weighting mechanism to encode the diverse local structure, with a scalable and theoretically justified sampling strategy for its learning. Specifically, we start with a formal definition of multi-order proximity matrix which leads to our new multi-order objective based on Laplacian Eigenmaps and Skip-Gram. Then we instantiate the node-specific multi-order weights in the objective with the help of neighborhood size estimation, which indicates node-specific multi-order information. For objective learning, it is implicitly fulfilled with our proposed branching tree-like random walk strategy termed by BTWalk, which differs from the dominant chain-like walk in existing sampling techniques. BTWalk is designed by a synergetic combination of BFS (breadth-first search) and DFS (depth-first search), which is modulated according to the weights of the considered proximity orders. We theoretically analyze its cost-efficiency, and further propose the so-called Vec4Cross framework that incorporates joint node embedding and network alignment for two partially overlapped networks based on the seed matchings, whereby BTWalk is also adopted for embedding. Promising experimental results are obtained on real-world datasets across popular tasks.

The glaucoma is an eye disease that causes blindness when it progresses in an advanced stage. Early glaucoma diagnosis is essential to prevent the vision loss. However, early detection is not covered due to the lack of ophthalmologists and the limited accessibility to retinal image capture devices. In this paper, we present an automated method for glaucoma screening dedicated for Smartphone Captured Fundus Images (SCFIs). The implementation of the method into a smartphone associated to an optical lens for retina capturing leads to a mobile aided screening system for glaucoma. The challenge consists in insuring higher performance detection despite the moderate quality of SCFIs, with a reduced execution time to be adequate for the clinical use. The main idea consists in deducing glaucoma based on the vessel displacement inside the Optic Disk (OD), where the vessel tree remains sufficiently modeled on SCFIs. Within this objective, our major contribution consists in proposing: (1) a robust processing for locating vessel centroids in order to adequately model the vessel distribution, and (2) a feature vector that relevantly reflect two main glaucoma biomarkers in terms of vessel displacement. Furthermore, all processing steps are carefully chosen based on lower complexity, to be suitable for fast clinical screening. A first evaluation of our method is performed using the two public DRISHTI-DB and DRIONS-DB databases, where 99% and 95% accuracy, 96.77% and 97,5% specificity and 100% and 95% sensitivity are respectively achieved. Thereafter, the method is evaluated using two fundus image databases respectively captured through a smartphone and retinograph for the same persons. We achieve 100% accuracy using both databases which assesses the robustness of our method. In addition, the detection is performed on 0.027 and 0.029 second when executed respectively on the Samsung-M51 on the Samsung-A70 smartphone devices. Our proposed smartphone app provides a cost-effective and widely accessible mobile platform for early screening of glaucoma in remote clinics or areas with limited access to fundus cameras and ophthalmologists.

Relative humidity (RH), an important climatic element influencing tree growth, is also crucial in assessing the General Circulation Models (GCMs) on global warming. However, in the absence of a relatively long record of RH in the Himalayan region, precise modelling of the climate change related processes and their impacts on this region are not well established. Here we present a new RH reconstruction for the pre-monsoon months (February–May) starting from 1800 C.E. using the tree-ring width data of subtropical Pinus roxburghii (chir-pine) from Pithoragarh, Western Himalaya, India. We found significant positive correlation between tree growth and pre-monsoon RH (n = 67, r = +0.569, p

Cloud storage attracts a lot of clients to join the paradise. For a high data availability, some clients require their files to be replicated and stored on multiple servers. Because clients are generally charged based on the redundancy level required by them, it is critical for clients to obtain convincing evidence that all replicas are stored correctly and are updated to the up-to-date version. In this paper, we propose a dynamic proof of data possession and replication (DPDPR) scheme, which is proved to be secure in the defined security model. Our scheme shares a single authenticated tree across multiple replicas, which reduces the tree's storage cost significantly. Our scheme allows for batch verification for multiple challenged leaves and can verify multiple replicas in a single batch way, which considerably save bandwidth and computation resources during audit process. We also evaluate the DPDPR's performance and compare it with the most related scheme. The evaluation results show that our scheme saves almost 66% tree's storage cost for three replicas, and obtains almost 60% and 80% efficiency improvements in terms of the overall bandwidth and computation costs, respectively, when three replicas are checked and each challenged with 460 blocks.

Species tree inference under the multi-species coalescent (MSC) model is a basic step in biological discovery. Despite the developments in recent years of methods that are proven statistically consistent and that have high accuracy, large datasets create computational challenges. Although there is generally some information available about the species trees that could be used to speed up the estimation, only one method–ASTRAL-J, a recent development in the ASTRAL family of methods–is able to use this information. Here we describe two new methods, NJst-J and FASTRAL-J, that can estimate the species tree given partial knowledge of the species tree in the form of a non-binary unrooted constraint tree.. We show that both NJst-J and FASTRAL-J are much faster than ASTRAL-J and we prove that all three methods are statistically consistent under the multi-species coalescent model subject to this constraint. Our extensive simulation study shows that both FASTRAL-J and NJst-J provide advantages over ASTRAL-J: both are faster (and NJst-J is particularly fast), and FASTRAL-J is generally at least as accurate as ASTRAL-J. An analysis of the Avian Phylogenomics project dataset with 48 species and 14,446 genes presents additional evidence of the value of FASTRAL-J over ASTRAL-J (and both over ASTRAL), with dramatic reductions in running time (20 hours for default ASTRAL, and minutes or seconds for ASTRAL-J and FASTRAL-J, respectively).AvailabilityFASTRAL-J and NJst-J are available in open source form at https://github.com/RuneBlaze/FASTRAL-constrained and https://github.com/RuneBlaze/NJst-constrained. Locations of the datasets used in this study and detailed commands needed to reproduce the study are provided in the supplementary materials at http://tandy.cs.illinois.edu/baqiao-suppl.pdf.

Most of the classification methods of urban functional areas nowadays are only based on single source data analysis and modeling, which can not make full use of the multi-scale and multi-source data that is easy to obtain. Therefore, this paper proposed a classification model of urban functional areas based on multi-modal machine learning, by analyzing regional remote sensing images and behavior data of visitors in the area, using the combination of supervised methods extracted the deep-seated features and relationships of kinds of data, filtered and merged the overall and local features of the data. The model used dual branch neural network combining SE-ResNeXt and Dual Path Network (DPN) to automatically mined and fused the overall characteristics of multi-source data, and used the designed feature engineering to deeply mine the behavior data of users to obtain more association information, then combined the algorithm based on Gradient Boosting Decision Tree to learn the characteristics of different levels and obtained the classification probability for different levels of features. Finally, we continued to use the algorithm based on the Gradient Boosting Decision Tree to learn the probability distribution of different levels of features to obtain the final prediction results of urban functional area classification. Through the analysis and experimental verification of real data sets, the results showed that MM-UrbanFAC model can effectively integrate the features of multi-modal data. Compared with a single classifier, the integration framework based on gradient lifting tree improved the prediction performance, this method can effectively integrate the results of multiple models and accurately classify urban functional areas, and the model can provide reference for tourism recommendation, urban land planning and urban construction.

Replicated tree data structures are a fundamental building block of distributed filesystems, such as Google Drive and Dropbox, and collaborative applications with a JSON or XML data model. These systems need to support a move operation that allows a subtree to be moved to a new location within the tree. However, such a move operation is difficult to implement correctly if different replicas can concurrently perform arbitrary move operations, and we demonstrate bugs in Google Drive and Dropbox that arise with concurrent moves. In this article we present a CRDT algorithm that handles arbitrary concurrent modifications on trees, while ensuring that the tree structure remains valid (in particular, no cycles are introduced), and guaranteeing that all replicas converge towards the same consistent state. Our algorithm requires no synchronous coordination between replicas, making it highly available in the face of network partitions. We formally prove the correctness of our algorithm using the Isabelle/HOL proof assistant, and evaluate the performance of our formally verified implementation in a geo-replicated setting.

Internet of Things (IoT) devices and systems are becoming increasingly commonplace, and as such systems scale up, so do the computational and storage requirements. Hence, one recent trend is to outsource data from IoT devices to remote systems. To facilitate both ciphertext retrieval and data confidentiality in the outsourced data, a number of searchable encryption approaches have been proposed in the literature. However, due to limited keyword space, a number of searchable encryption schemes are vulnerable to keyword guessing attacks (KGA). In addition, existing searchable encryption approaches generally do not consider the hierarchical structure in which users at different levels require varying access privileges. Furthermore, existing searchable encryption schemes seldom provide time-controlled access control. Therefore, in this paper we propose a time-controlled hierarchical multi-keyword search by using a double-server architecture to mitigate KGA. In our approach, we also build a public key tree to support different access permissions for hierarchical users. Formal security analysis shows that our scheme is secure, and extensive experiments demonstrate that our scheme is practical.

Across the tropics, recent agricultural shifts have led to a rapid expansion of tree plantations, often into intact forest and grassland habitats. However, this expansion is poorly characterized. Here we report tropical tree plantation expansion between 2000 and 2012, based on classifying nearly 7 million unique patches of observed tree cover gain using optical and radar satellite imagery. Most observed gain patches (69.2%) consisted of small patches of natural regrowth (5.9 ± 0.2 Mha). However, expansion of tree plantations dominated observed increases in tree cover across the tropics (11.8 ± 0.2 Mha) with 92% of plantation expansion occurring in biodiversity hotspots and 14% in arid biomes. We estimate that tree plantations expanded into 9.2% of accessible protected areas across the humid tropics, most frequently in southeast Asia, west Africa, and Brazil. Given international tree planting commitments, it is critical to understand how future tree plantation expansion will affect remaining natural ecosystems. One Sentence Summary: Tree plantations dominated recent expansions of tropical tree cover, including into 9% of accessible parks in the humid tropics.

This article proposes a binary tree of classifiers for multi-label classification that preserves label dependencies and handles class imbalance. At each node, the input data is strategically split into two subsets for its subsequent child nodes, keeping the label correlations intact. A novel approach of partitioning the data based on label-set proximity has also been proposed. Various data appropriate classifiers are trained to learn the binary partition at every internal node. The tree of classifiers grows iteratively depending on two parameters – multi-label entropy and sample cardinality, computed on the data at the current node. During training, the decision at any node is based on these parameters and the branching out is restricted, if deemed unnecessary. Specific classifiers at the leaf nodes perform the final classification task and assign appropriate label-sets to the unlabelled data. The proposed system aims to appropriately split the data and build the hierarchical structure such that the training and classification tasks become simpler. Also, the problem of class imbalance leads to the irregular splitting of data and excessive branching out of the tree which is handled through the novel use of suitable classifiers and parameters at the intermediate and leaf nodes. The proposed method has shown significant performance improvement on fourteen datasets against fourteen existing multi-label classifiers. Two-tailed Wilcoxon signed rank test statistics show that for $T_{Wilcoxon}(14,0.2)=31$ the proposed method outperforms all the other comparison models.

Electric Vehicles (EVs) are beginning to play a key role in the fast developing area of Internet of Things (IoT). Numerous results have shown the feasibility of vehicle-to-building (V2B) mode of charge/discharge, where EVs are considered as dynamically configurable dispersed energy storage units. When properly incorporated into the building energy system, EVs are able to provide ancillary services to the power grid during high demand periods or outage situations. The arising challenge is how to act intelligent behaviors in complex and changing microgrid environments. With the aim of minimizing the cost and maximizing satisfaction degree, this paper, unlike previous works, jointly considers the building energy need and the safety/willingness of EVs to find and dispatch the optimal vehicle to conduct auxiliary or supportive actions. To realize that, we propose an intelligent Privacy-preserving Context-based Online EV Dispatching System (PCOEDS), using a tree-based structure which supports the ever-increasing big metering datasets with context-awareness. Moreover, privacy preservation and security protection on both sides of the the energy transmission process are well guaranteed in our work. Theoretical results validate that our intelligent dispatching system achieves sublinear regret and differential privacy, which outperforms other online learning method when applied to a huge city-level dataset.

Decision tree is a widely used non-parametric technique in machine learning, data mining and pattern recognition. It is simple to understand and interpret, however it faces challenges such as handling higher dimensional and class imbalanced datasets, over-fitting and instability. To overcome some of these issues, vertical partitioning approaches like serial partitioning, theme based partitioning are used in the literature. A vertical partitioning approach divides the feature set into subsets of features (blocks) and makes use of these subsets for subsequent tasks. In this work, we use the ideas of music rhythm tree to propose a novel vertical partitioning technique. It orders the features based on the average correlation strength of the features before partitioning the feature set. The proposed method is proved to be superior by showing an average of 13.8 % , 6 % , 9.8 % , 19.7 % , 9.4 % , and 29.4 % higher classification accuracy over C4.5, Random Forest, Bagging, Adaboost, an ensemble technique and a vertical partitioning technique respectively. Our empirical results on 15 datasets demonstrate that the proposed vertical partitioning method is more stable and better in handling class-imbalanced data. Finally, some popular statistical tests are conducted to validate the statistical significance of the results of the proposed method.

Well known oil recovery factor estimation techniques such as analogy, volumetric calculations, material balance, decline curve analysis, hydrodynamic simulations have certain limitations. Those techniques are time-consuming, require specific data and expert knowledge. Besides, though uncertainty estimation is highly desirable for this problem, the methods above do not include this by default. In this work, we present a data-driven technique for oil recovery factor (limited to water flooding) estimation using reservoir parameters and representative statistics. We apply advanced machine learning methods to historical worldwide oilfields datasets (more than 2000 oil reservoirs). The data-driven model might be used as a general tool for rapid and completely objective estimation of the oil recovery factor. In addition, it includes the ability to work with partial input data and to estimate the prediction interval of the oil recovery factor. We perform the evaluation in terms of accuracy and prediction intervals coverage for several tree-based machine learning techniques in application to the following two cases: (1) using parameters only related to geometry, geology, transport, storage and fluid properties, (2) using an extended set of parameters including development and production data. For both cases model proved itself to be robust and reliable. We conclude that the proposed data-driven approach overcomes several limitations of the traditional methods and is suitable for rapid, reliable and objective estimation of oil recovery factor for hydrocarbon reservoir.

In vehicle operation, in order to maximize the fuel economy, the propulsion system control can easily adapt to pressure and temperature variations as these variations can be measured by sensors. However, it is challenging to detect driving cycles. With growing progress made in the artificial intelligence field, pattern recognition gains momentum in various applications. This study presents a study on driving cycle pattern recognition based on supervised learning. Training data 2-D visualization is achieved by t-SNE algorithm. Ten out of twelve supervised learning algorithms predict driving conditions with the accuracy at 88% or higher and Extra Tree algorithm leads the recognition accuracy at 90.26%. To improve recognition accuracy, two hierarchical frameworks are proposed by integrating multiple supervised learning methods using weighted average and vote methods. The two hierarchical frameworks boost the driving condition recognition accuracy from 90.26% to 90.43% (weighted average) and 91.76% (vote). The results are further validated in holdout test. Additionally, a plug-in hybrid electric vehicle simulation shows 3.88%-5.82% fuel economy improvement when compared to baseline method. Two hierarchical methods outperform the Extra Tree method by 2% fuel economy. In summary, supervised learning shows great potential to detect driving cycles for vehicle energy saving.

With the increasing demand of massive/big data applications, non-volatile memory (NVM), such as phase change memory (PCM), has become a promising candidate to replace DRAM because of its low leakage power, non-volatility, and high density. However, most of the existing memory read/write intensive algorithms and data structures are not aware of the PCM write heterogeneity in terms of both energy consumption and latency. In particular, self-balancing binary search trees, which are widely used to manage massive data in the big-data era, were designed without the consideration of PCM characteristics. Thus, the multiple rotations of the tree balancing process would degrade the memory performance. This work explores the relations among nodes and analyzes tree operations, and the node indexing and address mapping are redesigned to reduce the tree management overhead on single-level cell (SLC) PCM by decreasing the number of bit flips of tree rotations. When multi-level cell (MLC) PCM is included, our address mapping algorithm is developed to reduce the total energy consumption and latency with considerations of the heterogeneous write operations of different cell states. Experimental results show that our solution significantly outperforms the original implementation of a self-balancing binary search tree when the amount of data is large.

This article presents a configurable fast block partitioning decision for Versatile Video Coding (VVC) intra-frame prediction using Light Gradient Boosting Machine (LGBM). VVC further improves the coding efficiency by introducing a Quadtree with nested Multi-Type Tree (QTMT), enabling five split types allowing square and rectangular Coding Unit (CU) sizes. However, this improvement in the coding efficiency comes at the cost of a high computational burden since several combinations of block sizes and prediction modes are evaluated through the costly Rate-Distortion Optimization (RDO) process. In this article, we propose a partitioning decision using LGBM classifiers to avoid the exhaustive RDO process and skip the evaluation of split types that are unlikely to be chosen as the best one. For this purpose, five classifiers (one for each split type) were offline trained with an efficient training process and using effective features of texture, coding, and context information. The proposed solution is highly configurable and can provide several operation points with different tradeoffs between timesaving and coding efficiency, according to the application requirements. Considering five operation points, the configurable solution can reduce the encoding time from 35.22% to 61.34%, with coding efficiency losses from 0.46% to 2.43%. Compared to the state-of-the-art, our solution is able to outperform the related works in terms of combined rate-distortion and timesaving.

This paper presents a new fast, highly scalable distributed matrix multiplication algorithm on Apache Spark, called Stark, based on Strassen's matrix multiplication algorithm. Stark preserves Strassen's seven multiplications scheme in a distributed environment and thus achieves asymptotically faster execution time. It creates a distributed recursion tree of computation where each level of the tree corresponds to division and combination of distributed matrix blocks stored in the form of Resilient Distributed Datasets (RDDs). It processes each divide and combine step in parallel and memorises the sub-matrices by intelligently tagging matrix blocks in it. To the best of our knowledge, Stark is the first implementation of a distribute Strassen's algorithm on Spark platform. We also report a detailed complexity analysis for the proposed algorithm, taking into account computation and communication costs. Experimental results suggest that Stark outperforms existing distributed matrix multiplication implementations on Spark -- Marlin and MLLib, for high matrix sizes ( $\geq 16384\times 16384$ ). Our experiments reveal optimal block sizes for each matrix size, which is also shown from theoretical analysis. We also show that the experimental and theoretical running times for Stark match closely. It has also been shown experimentally that Stark exhibits strong scalability with increasing number of executors.

Searchable encryption enables users to perform a keyword-based search over encrypted data. Most of the existing searchable encryption schemes are based on the single cloud server model, which has some limitations, such as the single point of failure and malicious insiders. In contrast, the multicloud server model has higher data availability and security. However, directly moving the existing keyword search schemes to the multicloud would cause larger storage and search costs, and the data security cannot be distinctly improved. In addition, different from single cloud, a challenge is how to effectively verify query integrity when some cloud servers return incorrect or incomplete results in a multicloud situation. To ensure the data security, we propose an iterative encryption method to ensure the file privacy even if multiple cloud servers collude with each other. We use Bloom filter to map keywords and construct a tree index for each cloud server to achieve secure and efficient distributed keyword search. To verify the query integrity, we propose a fast verification algorithm to embed the file content and index information into the file ID. The analysis and experiments demonstrate the security and effectiveness of our scheme.

Publisher Copyright: IEEE Copyright: Copyright 2021 Elsevier B.V., All rights reserved. We consider learning to play multiplayer imperfect-information games with simultaneous moves and large state-action spaces. Previous attempts to tackle such challenging games have largely focused on model-free learning methods, often requiring hundreds of years of experience to produce competitive agents. Our approach is based on model-based planning. We tackle the problem of partial observability by first building an (oracle) planner that has access to the full state of the environment and then distilling the knowledge of the oracle to a (follower) agent which is trained to play the imperfect-information game by imitating the oracle's choices. We experimentally show that planning with naive Monte Carlo tree search performs poorly in large combinatorial action spaces. We therefore propose planning with a fixed-depth tree search and decoupled Thompson sampling for action selection. We show that the planner is able to discover efficient playing strategies in the games of Clash Royale and Pommerman and the follower policy successfully learns to implement them by training on few hundred battles.

Due to its strong performance in handling uncertain and ambiguous data, the fuzzy k -nearest-neighbor method (FKNN) has realized substantial success in a wide variety of applications. However, its classification performance would be heavily deteriorated if the number k of nearest neighbors was unsuitably fixed for each testing sample. This study examines the feasibility of using only one fixed k value for FKNN on each testing sample. A novel FKNN-based classification method, namely, fuzzy KNN method with adaptive nearest neighbors (A-FKNN), is devised for learning a distinct optimal k value for each testing sample. In the training stage, after applying a sparse representation method on all training samples for reconstruction, A-FKNN learns the optimal k value for each training sample and builds a decision tree (namely, A-FKNN tree) from all training samples with new labels (the learned optimal k values instead of the original labels), in which each leaf node stores the corresponding optimal k value. In the testing stage, A-FKNN identifies the optimal k value for each testing sample by searching the A-FKNN tree and runs FKNN with the optimal k value for each testing sample. Moreover, a fast version of A-FKNN, namely, FA-FKNN, is designed by building the FA-FKNN decision tree, which stores the optimal k value with only a subset of training samples in each leaf node. Experimental results on 32 UCI datasets demonstrate that both A-FKNN and FA-FKNN outperform the compared methods in terms of classification accuracy, and FA-FKNN has a shorter running time.

We analyze a tree search problem with an underlying Markov decision process, in which the goal is to identify the best action at the root that achieves the highest cumulative reward. We present a new tree policy that optimally allocates a limited computing budget to maximize a lower bound on the probability of correctly selecting the best action at each node. Compared to widely used Upper Confidence Bound (UCB) tree policies, the new tree policy presents a more balanced approach to manage the exploration and exploitation trade-off when the sampling budget is limited. Furthermore, UCB assumes that the support of reward distribution is known, whereas our algorithm relaxes this assumption. Numerical experiments demonstrate the efficiency of our algorithm in selecting the best action at the root., Comment: Submitted to IEEE Transactions on Automatic Control

Time-calibrated phylogenetic trees are a tremendously powerful tool for studying evolutionary, ecological, and epidemiological phenomena. Such trees are predominantly inferred in a Bayesian framework, with the phylogeny itself treated as a parameter with a prior distribution (a “tree prior”). However, we show that the tree “parameter” consists, in part, of data, in the form of taxon samples. Treating the tree as a parameter fails to account for these data and compromises our ability to compare among models using standard techniques (e.g., marginal likelihoods estimated using path-sampling and stepping-stone sampling algorithms). Since accuracy of the inferred phylogeny strongly depends on how well the tree prior approximates the true diversification process that gave rise to the tree, the inability to accurately compare competing tree priors has broad implications for applications based on time-calibrated trees. We outline potential remedies to this problem, and provide guidance for researchers interested in assessing the fit of tree models. [Bayes factors; Bayesian model comparison; birth-death models; divergence-time estimation; lineage diversification]

Recent efforts applying machine learning techniques to query optimization have shown few practical gains due to substantive training overhead, inability to adapt to changes, and poor tail performance. Motivated by these difficulties, we introduce Bao (the \underlineBa ndit \underlineo ptimizer). Bao takes advantage of the wisdom built into existing query optimizers by providing per-query optimization hints. Bao combines modern tree convolutional neural networks with Thompson sampling, a well-studied reinforcement learning algorithm. As a result, Bao automatically learns from its mistakes and adapts to changes in query workloads, data, and schema. Experimentally, we demonstrate that Bao can quickly learn strategies that improve end-to-end query execution performance, including tail latency, for several workloads containing long-running queries. In cloud environments, we show that Bao can offer both reduced costs and better performance compared with a commercial system.

The explosive growth of modern web-scale applications has made cost-effectiveness a primary design goal for their underlying databases. As a backbone of modern databases, LSM-tree based key–value stores (LSM store) face limited storage options. They are either designed for local storage that is relatively small, expensive, and fast or for cloud storage that offers larger capacities at reduced costs but slower. Designing an LSM store by integrating local storage with cloud storage services is a promising way to balance the cost and performance. However, such design faces challenges such as data reorganization, metadata overhead, and reliability issues. In this article, we propose RocksMash , a fast and efficient LSM store that uses local storage to store frequently accessed data and metadata while using cloud to hold the rest of the data to achieve cost-effectiveness. To improve metadata space-efficiency and read performance, RocksMash uses an LSM-aware persistent cache that stores metadata in a space-efficient way and stores popular data blocks by using compaction-aware layouts. Moreover, RocksMash uses an extended write-ahead log for fast parallel data recovery. We implemented RocksMash by embedding these designs into RocksDB. The evaluation results show that RocksMash improves the performance by up to 1.7 \( \times \) compared to the state-of-the-art schemes and delivers high reliability, cost-effectiveness, and fast recovery.

We study the prevalence of forest fires in Spain in the long run by computing the probability that a forest tree, as opposed to shrub or bush, will be lost to fire over the course of the year. Climate change is first shown to increase the likelihood of this event. Next, we document how risk grew dramatically from 1961 up to the democratic era (c. 1980) and has since receded to less than 2 trees lost per thousand. We bring together the socio-economic drivers identified for this trend reversal. Our finding is commensurate with the evolution of the same risk in neighboring Mediterranean countries.

Cloud storage becomes the priority for storing and sharing data for enterprise users. Encrypting prior to uploading data to the cloud is the best way to protect business secrets, however, it hinders the convenient operations on plaintexts, such as searching over the cloud data. In addition, employees in an enterprise have multiple layer structures and a higher layer employee should have the privilege to monitor the lower layer employees' data to check if these users violate the regulation without letting the employees be aware of. Public key encryption with keyword search (PEKS) is a well-known cryptographic primitive suitable for secure cloud storage, which supports keyword search without decryption in public key encryption settings. Unfortunately, there is no existing PEKS scheme supporting the monitoring function without authorization from the sender. To address this issue, we propose a variant of PEKS named Hierarchical Public Key Encryption with Keyword Search (HPEKS) and provide a semi-generic construction utilizing a public key tree (PKTree) and a PEKS scheme. To better suit for the enterprise secret data sharing, we build an advanced HPEKS scheme, named designated-tester decryptable hierarchical public key encryption with keyword search, which enjoys stronger security and integrates the public key and symmetric key.

Density Peak Clustering (DPC), a popular density-based clustering approach, has received considerable attention from the research community primarily due to its simplicity and fewer-parameter requirement. However, the resultant clusters obtained using DPC are influenced by the sensitive parameter $d_c$, which depends on data distribution and requirements of different users. Besides, the original DPC algorithm requires visiting a large number of objects, making it slow. To this end, this paper investigates index-based solutions for DPC. Specifically, we propose two list-based index methods viz. (i) a simple List Index, and (ii) an advanced Cumulative Histogram Index. Efficient query algorithms are proposed for these indices which significantly avoids irrelevant comparisons at the cost of space. For memory-constrained systems, we further introduce an approximate solution to the above indices which allows substantial reduction in the space cost, provided that slight inaccuracies are admissible. Furthermore, owing to considerably lower memory requirements of existing tree-based index structures, we also present effective pruning techniques and efficient query algorithms to support DPC using the popular Quadtree Index and R-tree Index. Finally, we practically evaluate all the above indices and present the findings and results, obtained from a set of extensive experiments on six synthetic and real datasets. The experimental insights obtained can help to guide in selecting a befitting index.

A key element in solving real-life data science problems is selecting the types of models to use. Tree ensemble models (such as XGBoost) are usually recommended for classification and regression problems with tabular data. However, several deep learning models for tabular data have recently been proposed, claiming to outperform XGBoost for some use cases. This paper explores whether these deep models should be a recommended option for tabular data by rigorously comparing the new deep models to XGBoost on various datasets. In addition to systematically comparing their performance, we consider the tuning and computation they require. Our study shows that XGBoost outperforms these deep models across the datasets, including the datasets used in the papers that proposed the deep models. We also demonstrate that XGBoost requires much less tuning. On the positive side, we show that an ensemble of deep models and XGBoost performs better on these datasets than XGBoost alone.

With the development of cloud computing, more and more data owners are motivated to outsource their documents to the cloud and share them with the authorized data users securely and flexibly. To protect data privacy, the documents are generally encrypted before being outsourced to the cloud and hence their searchability decreases. Though many privacy-preserving document search schemes have been proposed, they cannot reach a proper balance among functionality, flexibility, security and efficiency. In this paper, a new encrypted document retrieval system is designed and a proxy server is integrated into the system to alleviate data owner's workload and improve the whole system's security level. In this process, we consider a more practical and stronger threat model in which the cloud server can collude with a small number of data users. To support multiple document search patterns, we construct two AVL trees for the filenames and authors, and a Hierarchical Retrieval Features tree (HRF tree) for the document vectors. A depth-first search algorithm is designed for the HRF tree and the Enhanced Asymmetric Scalar-Product-Preserving Encryption (Enhanced ASPE) algorithm is utilized to encrypt the HRF tree. All the three index trees are linked with each other to efficiently support the search requests with multiple parameters. Theoretical analysis and simulation results illustrate the security and efficiency of the proposed framework.

Localization in the complex urban environment is an open problem for current methods. The occlusion from dynamic objects, such as vehicles and pedestrians, degenerates the precision of the localization result. This article proposes a pole-like feature-based localization framework to solve this problem. Pole-like objects, such as posts of lamps or traffic sign and tree trunks, widely exist in the urban environment and are robust to occlusion, as they are usually higher than the objects on the road. First, this type of feature is extracted from the point cloud by a robust clustering algorithm. Then, the features from different frames of data are stitched to generate a feature map. For online localization, a Monte Carlo localization (MCL) framework is used to fuse the vehicle motion data and the map-matching result. An improved version of iterative closest point (ICP) that is specifically designed for the pole-like feature association is used for map matching based on the state of every particle. With the MCL scheme, localization is robust to the local minimum or robot kidnapping problem. Experimental results in the real urban environment demonstrate the precision and robustness of the proposed method, with mean absolute errors less than 0.20 m and 0.5°. The results also show that the proposed method outperforms some state-of-the-art localization methods in the complex urban environment.