Personalizing Type-Based Facet Ranking Using BERT Embeddings

In Faceted Search Systems (FSS), users navigate the information space through facets, which are attributes or meta-data that describe the underlying content of the collection. Type-based facets (aka t-facets) help explore the categories associated with the searched objects in structured information space. This work investigates how personalizing t-facet ranking can minimize user effort to reach the intended search target. We propose a lightweight personalisation method based on Vector Space Model (VSM) for ranking the t-facet hierarchy in two steps. The first step scores each individual leaf-node t-facet by computing the similarity between the t-facet BERT embedding and the user profile vector. In this model, the user’s profile is expressed in a category space through vectors that capture the users’ past preferences. In the second step, this score is used to re-order and select the sub-tree to present to the user. The final ranked tree reflects the t-facet relevance both to the query and the user profile. Through the use of embeddings, the proposed method effectively handles unseen facets without adding extra processing to the FSS. The effectiveness of the proposed approach is measured by the user effort required to retrieve the sought item when using the ranked facets. The approach outperformed existing personalization baselines.

Eigenvalue Spectrum and Synchronizability of Two Types of Double-Layer Star-Ring Networks with Hybrid Directional Coupling

In this study, we studied the eigenvalue spectrum and synchronizability of two types of double-layer hybrid directionally coupled star-ring networks, namely, the double-layer star-ring networks with the leaf node pointing to the hub node (Network I) and the double-layer star-ring networks with the hub node pointing to the leaf node (Network II). We strictly derived the eigenvalue spectrum of the supra-Laplacian matrix of these two types of networks and analyzed the relationship between the synchronizability and the structural parameters of networks based on the master stability function theory. Furthermore, the correctness of the theoretical results was verified through numerical simulations, and the optimum structural parameters were obtained to achieve the optimal synchronizability.

Decision Tree dalam Menganalisis Data Pengunjung Wisata Danau Poso untuk Pengambilan Keputusan

This study uses a classification system in managing its data. In classification there are several methods provided, one of which is the decision tree method with the C4.5 algorithm this method means a decision tree where the structure is the same as a flowchart where each node signifies an attribute test, each branch presents the test results and the leaf node represents the class or class distribution. The data used is the data of Lake Poso Tourism visitors from 2009 to 2020, then the method used in this study is divided into several stages, namely the data being studied, analyzing the data, transforming data and designing a decision tree with the C4.5 algorithm. The results achieved from this study are that the number of visitors more than 28,984 has a description of "Much" which is dominated by local tourists, while the value with the name "Less" is in foreign tourists. This is one of the important points in determining the right strategy for developing tourism in Lake Poso.

Concurrent Binary Trees (with application to longest edge bisection)

We introduce the concurrent binary tree (CBT), a novel concurrent representation to build and update arbitrary binary trees in parallel. Fundamentally, our representation consists of a binary heap, i.e., a 1D array, that explicitly stores the sum-reduction tree of a bitfield. In this bitfield, each one-valued bit represents a leaf node of the binary tree encoded by the CBT, which we locate algorithmically using a binary-search over the sum-reduction. We show that this construction allows to dispatch down to one thread per leaf node and that, in turn, these threads can safely split and/or remove nodes concurrently via simple bitwise operations over the bitfield. The practical benefit of CBTs lies in their ability to accelerate binary-tree-based algorithms with parallel processors. To support this claim, we leverage our representation to accelerate a longest-edge-bisection-based algorithm that computes and renders adaptive geometry for large-scale terrains entirely on the GPU. For this specific algorithm, the CBT accelerates processing speed linearly with the number of processors.

The Proof of Collatz Conjecture

This paper redefines Collatz conjecture, and proposes strong Collatz conjecture, the strong Collatz conjecture is a sufficient condition for the Collatz conjecture. Based on the computer data structure–tree, we construct the non-negative integer inheritance decimal tree. The nodes on the decimal tree correspond to non-negative integers. We further define the Collatz-leaf node (corresponding to the Collatz-leaf integer) on the decimal tree. The Collatz-leaf nodes satisfy strong Collatz conjecture. Derivation through mathematics, we prove that the Collatz-leaf node (Collatz-leaf integer) has the characteristics of inheritance. With computer large numbers and big data calculation, we conclude that all nodes at depth 800 are Collatz-leaf nodes. So we prove that strong Collatz conjecture is true, the Collatz conjecture must also be true. And for any positive integer N greater than 1, the minimum number of Collatz transform times from N to 1 is log2 N, the maximum number of Collatz transform times is 800 *（N-1）. The non-negative integer inheritance decimal tree proposed and constructed in this paper also can be used for the proof of other mathematical problems.

Effects of different plant growth regulators on in vitro growth of Aloe vera L.

An attempt was made to standardize the appropriate concentration of different growth regulators for successful in vitro growth of different explants (leaf, node and internode) of Aloe vera L. Results demonstrated best in vitro growth in leaf explants in MS medium supplemented with BAP (1.0 mg/l) and NAA (1.0 mg/l) at 26 ± 2ºC) with pH 5.70 using agar solidified medium and 16 hrs photoperiod.

New classification technique: fuzzy oblique decision tree

Based on axiomatic fuzzy set (AFS) theory and fuzzy information entropy, a novel fuzzy oblique decision tree (FODT) algorithm is proposed in this paper. Traditional axis-parallel decision trees only consider a single feature at each non-leaf node, while oblique decision trees partition the feature space with an oblique hyperplane. By contrast, the FODT takes dynamic mining fuzzy rules as a decision function. The main idea of the FODT is to use these fuzzy rules to construct leaf nodes for each class in each layer of the tree; the samples that cannot be covered by the fuzzy rules are then put into an additional node – the only non-leaf node in this layer. Construction of the FODT consists of four major steps: (a) generation of fuzzy membership functions automatically by AFS theory according to the raw data distribution; (b) extraction of dynamically fuzzy rules in each non-leaf node by the fuzzy rule extraction algorithm (FREA); (c) construction of the FODT by the fuzzy rules obtained from step (b); and (d) determination of the optimal threshold [Formula: see text] to generate a final tree. Compared with five traditional decision trees (C4.5, LADtree (LAD), Best-first tree (BFT), SimpleCart (SC) and NBTree (NBT)) and a recently obtained fuzzy rules decision tree (FRDT) on eight UCI machine learning data sets and one biomedical data set (ALLAML), the experimental results demonstrate that the proposed algorithm outperforms the other decision trees in both classification accuracy and tree size.