Reviewerly/exHarmony huggingface dataset:
Discovering Frequency Bursting Patterns in Temporal Graphs

A frequency bursting pattern (FBP) in temporal graphs represents some interaction behavior that accumulates its frequency at the fastest rate. Mining FBPs is essential to early warning of emergencies. However, existing studies on frequency-based pattern mining in graphs do not consider the temporal information and bursting features of a subgraph pattern. As a result, they may not provide effective and efficient mining algorithms for FBP discovery. In this paper, we study the problem of discovering top-k FBPs in temporal graphs. We present a novel model, referred to as maximal (m, θ)-bursting pattern, to describe FBPs in a temporal graph, which is a subgraph with a size larger than m that accumulates its frequency at the fastest rate during a time interval of length no less than θ. A naive solution for top-k FBPs discovery is to use the best-first search algorithm, where the burstiness threshold changes as more patterns are mined. However, this method will result in huge search space since we need to check every possible time interval for a candidate pattern in the temporal graph. To tackle this problem, we devise an online top-k framework in which k candidate results are maintained from the initial timestamp to the end in the temporal graph. Under the new framework, we further conceive two optimization strategies by exploiting incremental subgraph matching and Evolutionary Game Theory to boost the performance. Extensive experiment results on five real temporal graphs show that our algorithm has higher efficiency, effectiveness and scalability.
Planning Data Poisoning Attacks on Heterogeneous Recommender Systems in a Multiplayer Setting

Data poisoning attacks against recommender systems (RecSys) often assume a single seller as the adversary. However, in reality, there are usually multiple sellers attempting to promote their items through RecSys manipulation. To obtain the best data poisoning plan, it is important for an attacker to anticipate and withstand the actions of his opponents. This work studies the problem of Multiplayer Comprehensive Attack (MCA) from the perspective of the attacker, considering the subsequent attacks by his opponents. In MCA, we target the Heterogeneous RecSys, where user-item interaction records, user social network, and item correlation graph are used for recommendations. To tackle MCA, we present the Multilevel Stackelberg Optimization over Progressive Differentiable Surrogate (MSOPDS). The Multilevel Stackelberg Optimization (MSO) method is used to form the optimum strategies by solving the Stackelberg game equilibrium between the attacker and his opponents, while the Progressive Differentiable Surrogate (PDS) addresses technical challenges in deriving gradients for candidate poisoning actions. Experiments on Heterogeneous RecSys trained with public datasets show that MSOPDS outperforms all examined prior works by up to 10.6% in average predicted ratings and up to 11.4% in HitRate@3 for an item targeted by an attacker facing one opponent. Source code provided in https://github.com/jimmy-academia/MSOPDS.
HyperMinHash: MinHash in LogLog Space (Extended Abstract)

In this extended abstract, we describe and analyze a lossy compression of MinHash from buckets of size O(log n) to buckets of size O(log log n) by encoding using floating-point notation. This new compressed sketch, which we call HyperMinHash, as we build off a HyperLogLog scaffold, can be used as a drop-in replacement of MinHash. Unlike comparable Jaccard index fingerprinting algorithms in sub-logarithmic space (such as b-bit MinHash), HyperMinHash retains MinHash’s features of streaming updates, unions, and cardinality estimation. For an additive approximation error ϵ on a Jaccard index t, given a random oracle, HyperMinHash needs $O\left( {{ \in ^{ - 2}}\left( {\log \log n + \log \frac{1}{ \in }} \right)} \right)$ space. HyperMinHash allows estimating Jaccard indices of 0.01 for set cardinalities on the order of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">19</sup> with relative error of around 10% using 2MiB of memory; MinHash can only estimate Jaccard indices for cardinalities of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">10</sup> with the same memory consumption.
CrypText: Database and Interactive Toolkit of Human-Written Text Perturbations in the Wild

User-generated textual contents on the Internet are often noisy, erroneous, and not in correct grammar. In fact, some online users choose to express their opinions online through carefully perturbed texts, especially in controversial topics (e.g., politics, vaccine mandate) or abusive contexts (e.g., cyberbullying, hate-speech). However, to the best of our knowledge, there is no framework that explores these online "human-written" perturbations (as opposed to algorithm-generated perturbations). Therefore, we introduce an interactive system called CrypText. CrypText is a data-intensive application that provides the users with a database and several tools to extract and interact with human-written perturbations. Specifically, CrypText helps look up, perturb, and normalize (i.e., de-perturb) texts. CrypText also provides an interactive interface to monitor and analyze text perturbations online. The demo is available at: https://lethaiq.github.io/anthro.
Variabel Non Akademik Untuk Memprediksi Prestasi Siswa Dengan Data Mining Menggunakan Metoda Naïve Bayes

The aim of this research is to measure not only the accuracy rate but also the precision result and the recall of the data mining application to predict Junior High School students’ learning outcomes based on their gender, the origin of the school, parents’ education and occupation. Determination of the students’ learning outcomes are very important in the education world. it becomes important because of the difficulty in determining the factors and variables which can affect the students’ learning outcomes.The accurate process of the data mining can recognize and extract the pattern of knowledge in order to offer solutions to increase the education quality where it can help the students maximize their achievement.There are some classification models in data mining: ID3 algorithm, C4.5 and Naïve Bayes which can be used to predict the students’ achievement, specifically, in Junior High School. This research uses Naïve Bayes classification mode to predict the Saint Mary Junior High School students’ achievement in order to get a better accuracy.
Enhancing Factorization Machines with Generalized Metric Learning (Extended Abstract)

Factorization Machines (FMs) have been proven effective in modeling the second-order interactions between attributes. Nevertheless, existing methods often fall short of learning the complex and rich intra-attribute feature interactions, which are abundantly exhibited in real-world recommendation data. To address this problem, we propose an FM framework equipped with generalized metric learning to better handle the essential feature correlations. In particular, based on this framework, we present a Mahalanobis distance and a deep neural network method, which can effectively model the linear and non-linear correlations between features, respectively. Besides, we design an efficient approach for simplifying the model functions. Experiments on several benchmark datasets demonstrate that our proposed framework outperforms several state-of-the-art baselines by a significant margin.
Unsupervised Transformer Balanced Hashing for Multispectral Remote Sensing Image Retrieval

For remote sensing (RS) image retrieval task, hashing technology have been extensively researched in recent works. Unsupervised hashing approaches have attracted much attention in the RS data processing field because label collection takes a lot of time. Most of which fail to consider the interactions among the multichannel information of multispectral RS images and the disparity between the hash-like codes space and the Hamming space, which lead to the poor performance of multispectral RS image retrieval. In this article, we tackle these dilemmas with a novel unsupervised hashing approach, namely <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Unsupervised Transformer Balanced Hashing</i> (UTBH), to utilize a convolutional variational autoencoder architecture with a novel RS transformer to perform effective hash codes learning. We first integrate a convolutional variational autoencoder architecture with a novel RS transformer, which can guide the interactions among the multichannel information of multispectral RS images. Meanwhile, a new objective function is proposed to preserve discrimination of hash codes in the hashing learning process and reduce the disparity between the hash-like codes space and the Hamming space effectively. Finally, experimental results on two multispectral RS image datasets indicate that UTBH approach achieves superior performance over other unsupervised image retrieval approaches.
Retracted: English-Chinese Machine Translation Based on Transfer Learning and Chinese-English Corpus

[This retracts the article DOI: 10.1155/2022/1563731.].
LightMIRM: Light Meta-learned Invariant Risk Minimization for Trustworthy Loan Default Prediction

Machine learning models are increasingly applied to loan default prediction to reduce the labor cost of financial institutions and the waiting time of lenders. We find that existing loan default prediction models remain lack minimax fairness, i.e., encountering significant performance drops on underrepresented subpopulations. The main cause of this trustworthy issue is pursuing Empirical Risk Minimization over the whole population, which will overlook the underrepresented subpopulations. To tackle this issue, we split the training data into subpopulations (a.k.a. environments) and conduct Invariant Risk Minimization (IRM) to learn the optimal prediction model across environments. A technical challenge is the computation cost of directly using existing IRM methods suitable for loan default prediction, such as meta-IRM, which quadratically increases as the number of environments. To reduce the complexity in training, we propose a light meta-IRM method which reduces time complexity to be linear through environment sampling and loss replaying strategies. We apply the light meta-IRM to train a representative loan default prediction model and conduct both online and offline evaluations on a large auto loan platform. Extensive experiment results validate the advantage of the proposed light meta-IRM w.r.t. the overall accuracy, minimax fairness, and training cost.
Agile Outside of Software Development – Systematic Literature Review

The ascension of agile project management (APM) has been evident in the past few years while projects increasingly require flexible methods to handle dynamic business environments [1]. Even though APM started and developed itself in the IT and software development sectors [2], it is no longer tied to those boundaries as its methodologies hold value to many different industries. As APM keeps expanding its reach, research has increased over these applications of agile methodologies outside of IT and software development. However, these approaches are still few if compared to conventional applications of agile and lack standardized factors to study it in unconventional environments, such as manufacturing. In order to understand how researchers and practitioners have been applying agile in different contexts, and pursue similarities among these approaches, a systematic literature review was conducted in two renowned online repositories: Scopus and Web of Science. The results found three core agile applications outside of IT that are relevant to understand how agile methodologies can be applied in sectors such as manufacturing, telecommunication and construction.
Data Representation by Joint Hypergraph Embedding and Sparse Coding (Extended Abstract)

Matrix factorization (MF), a popular unsupervised learning technique for data representation, has been widely applied in data mining and machine learning. According to different application scenarios, one can impose different constraints on the factorization to find the desired basis, which captures high-level semantics for the given data, and learns the compact representation corresponding to the basis. We note that almost all previous work on MF in data mining has ignored to find such a basis, which can carry high-order semantics in the data. In this work, we propose a novel MF framework called Joint Hypergraph Embedding and Sparse Coding, in which the obtained basis captures high-order semantic information in data. Experimental results on data clustering demonstrate that the proposed method consistently outperforms the other state-of-the-art matrix factorization methods.
Projection Mapping Segmentation Block: A Fusion Approach of Pointcloud and Image for Multi-Objects Classification

There is an increasing trend towards multi-modal sensor fusion to improve the perception capability of autonomous vehicles, such as the fusion of light detection and ranging (LiDAR) pointcloud and camera image data. In this work, we proposed the early-fusion method of projection mapping segmentation blocks, which is a method to map spatial pointcloud segmentation blocks and image pixel segmentation blocks to each other according to the projection relationship. The local spatial association feature and the local semi-variance texture feature were additionally designed for feature extraction, and each pair of segmentation blocks is used as a processing unit for feature extraction. Finally, the classification task was completed by Support Vector Machines (SVM) algorithm. We compared the classification results of the single pointcloud without fusion and pointcloud+image fusion under two segmentation methods: box-based and sector ring hexahedron-based. Our findings suggest that the data fusion classification method based on sector ring hexahedron segmentation not only improves classification accuracy but also reduces computational resources, yielding encouraging results.
Dynamic Activation of Clients and Parameters for Federated Learning over Heterogeneous Graphs

The data generated in many real-world applications can be modeled as heterogeneous graphs of multi-typed entities (nodes) and relations (links). Nowadays, such data are commonly generated and stored by distributed clients, making direct centralized model training unpractical. While the data in each client are prone to biased local distributions, generalizable global models are still in frequent need for large-scale applications. However, the large number of clients enforce significant computational overhead due to the communication and synchronization among the clients, whereas the biased local data distributions indicate that not all clients and parameters should be computed and updated at all times. Motivated by specifically designed preliminary studies on training a state-of-the-art heterogeneous graph neural network (HGN) with the vanilla FedAvg framework, in this work, we propose to leverage the characteristics of heterogeneous graphs by designing dynamic activation strategies for the clients and parameters during the federated training of HGN, named FedDA. Moreover, we design a novel disentangled model D-HGN to enable type-oriented activation of model parameters for FedDA. The effectiveness and efficiency of our proposed techniques are backed by both theoretical and empirical analysis– We theoretically analyze the validity and convergence of FedDA and mathematically illustrate its efficiency gain; meanwhile, we demonstrate the significant performance gains of FedDA and corroborate its efficiency gains with extensive experiments over multiple realistic FL settings synthesized based on real-world heterogeneous graphs.
Symbol Detection with Deep Learning

This study shows the feasibility of detecting some symbols from a distance. Therefore, it will be possible to use these symbols as identification of things, e.g. people, vehicles, objects, information labels etc. There is such a necessity especially for construction sites to know the rough location of workers. In order to achieve this goal, firstly some two-dimensional symbols that are distinguishable from each other by both human eye and computer are generated. Afterwards, they are put on a three dimensional surface and annotated. Lastly, the labeled data are trained by the deep learning method YOLO version 5. Results indicate that it is highly efficient to use the chosen symbols for recognizing from a distance. For now, up to 10 meter images are taken into account. Real time tests are taken and the accuracy is about 85%. As the dataset will be combined with real-life images, the accuracy results will be even higher.
Lightweight-Yet-Efficient: Revitalizing Ball-Tree for Point-to-Hyperplane Nearest Neighbor Search

Finding the nearest neighbor to a hyperplane (or Point-to-Hyperplane Nearest Neighbor Search, simply P2HNNS) is a new and challenging problem with applications in many research domains. While existing state-of-the-art hashing schemes (e.g., NH and FH) are able to achieve sublinear time complexity without the assumption of the data being in a unit hypersphere, they require an asymmetric transformation, which increases the data dimension from d to Ω(d <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ). This leads to considerable overhead for indexing and incurs significant distortion errors.In this paper, we investigate a tree-based approach for solving P2HNNS using the classical Ball-Tree index. Compared to hashing-based methods, tree-based methods usually require roughly linear costs for construction, and they provide different kinds of approximations with excellent flexibility. A simple branch-and-bound algorithm with a novel lower bound is first developed on Ball-Tree for performing P2HNNS. Then, a new tree structure named BC-Tree, which maintains the Ball and Cone structures in the leaf nodes of Ball-Tree, is described together with two effective strategies, i.e., point-level pruning and collaborative inner product computing. BC-Tree inherits both the low construction cost and lightweight property of Ball-Tree while providing a similar or more efficient search. Experimental results over 16 real-world data sets show that Ball-Tree and BC-Tree are around 1.1~10× faster than NH and FH, and they can reduce the index size and indexing time by about 1~3 orders of magnitudes on average. The code is available at https://github.com/HuangQiang/BC-Tree.
Procedure for checking the uniformity of samples of text documents based on nonparametric criteria

One of the most important tasks in Text Mining is the formation of sufficiently large representative and consistent samples (datasets). Usually, datasets are obtained from various information sources. In some cases, due to the lack of specialized texts in Russian, the dataset is expanded by adding translated English-language documents. In such situations, it is advisable to evaluate the uniformity-heterogeneity of the combined arrays. However, such a verification is complicated by the fact that the documents are multidimensional vectors, the correct comparison of which is a very non-trivial task. Insufficient elaboration of procedures for checking the uniformity of samples for the multidimensional case leads to the fact the problem of possible differences in data is ignored that in practice as insignificant. As a result, classifiers are trained on samples that are a mixture of quite diverse texts, and the resulting quality of categorization does not improve (or even deteriorates). Thus, it seems relevant to develop a procedure for checking the uniformity of documentary samples. To do this, we provide a comprehensive study of the problem of shift in textual data, identified and analyzed the reasons that cause the heterogeneity of documentary arrays. In this study, the datasets consist of bibliographic descriptions of scientific articles (title, abstract, keywords). The authors develop a procedure for assessing the homogeneity of two samples having approximately the same volume and the same method for calculating the weights of terms. For comparison, centroids are used, which have the size of a common dictionary of two datasets (in the absence of some terms, zero values are put in the corresponding positions of the centroids). The representation of samples in the form of «terminological portraits» (centroids) allowed us to reduce the verification of the homogeneity of multidimensional document vectors to a well-studied problem of analyzing two one-dimensional connected samples, for which nonparametric criteria were used. The sign criterion and the Wilcoxon sign rank criterion were used in the study. The proposed procedure for checking the uniformity of samples was tested on three collections of documents obtained from Russian and English-language sources.
Active Sampling for Sparse Table by Bayesian Optimization with Adaptive Resolution

Open-source relational database systems have become increasingly popular in the cloud era. However, practitioners are often beset with query performance issues. Thus a general-purpose database performance tuning tool independent of the various DBMS kernels becomes desired to lower the bar of using these systems. The first mandatory step in developing such a tool is to design an effective sampling method that collects representative records from different tables. Although one could leverage standard SQL statements and indexes to achieve this, sampling performance and statistical efficiency are not guaranteed when the underlying tables are frequently updated, especially for Sparse Tables where the range of index values is significantly greater than the table size.To this end, we propose a novel Active Sampling algorithm that queries regions more likely to contain data records from Sparse Tables. It relies on Gaussian process regression to characterize the probability density of whether a data record is non-null at a given index value. With the help of this estimated density function, the proposed method achieves efficient sampling by actively querying records with adaptive resolutions of interval lengths and provides an unbiased estimator for histogram construction. Comprehensive experiments on synthetic and real-world datasets demonstrate that the proposed Active Sampling method can effectively improve the estimation accuracy and use less query cost than other commonly used sampling methods.
PERANCANGAN APLIKASI KOMPETENSI DAN PRESTASI MAHASISWA UNIVERSITAS ISLAM KALIMANTAN MUHAMMAD ARSYAD AL BANJARI

Student achievements are a form of outcomes achieved during their academic activities at a higher education institution. Achievements are one of the essential factors in assessing the quality of an educational institution. One example is the accreditation evaluation of a study program or the assessment of an institution based on student achievements. Currently, the management of student achievement data is still done manually, where each student's academic and non-academic achievements are reported to the Student Affairs department for data recording. Considering the importance of student achievement data, there is a need for a system to facilitate the management of this data by the Student Affairs department. In this system, students who achieve success can directly input their data into the system. When the data is needed, the operator can easily present the required student achievement data. The system is built using PHP programming language and MySQL database, and it has proven to provide convenience to the Student Affairs department in managing student achievement data. With this system in place, study programs are also assisted in accessing student achievement data for accreditation assessment purposes
Retracted: Analysis of Psychological Shaping Function of Music Education under the Background of Artificial Intelligence

[This retracts the article DOI: 10.1155/2022/7162069.].