Reviewerly/exHarmony huggingface dataset:
LATERAL THINKING IN TEACHING UKRAINIAN EDITING TO HIGH SCHOOL STUDENTS

Background. Teaching editing, being an effective tool of building up students’ text competence, faces a number of challenges. To foster high school students’ mastering the skills of effective text creation and written production, Ukrainian language teachers are searching for teaching strategies to improve their students’ literacy, increase motivation and induce thinking. Lateral thinking techniques are viewed as a powerful instrument to develop students’ text competence through teaching editing. Purpose: The aim of the article is to present the strategy of teaching the Ukrainian language text editing to 10-11graders using lateral thinking techniques; to analyze how lateral thinking techniques stimulate the interest of students in editing activities and build up their text competence. Methodology. The study was conducted in November-December 2022; the participants were 10th-grade (n=25) and 11th-grade (n=24) students of specialist philology-oriented school No 98 of Kyiv. The authors of the article designed the research framework; its implementation involved interviewing five Ukrainian language and literature teachers of the above-mentioned school with 9-17 years of teaching experience to specify the problem areas; selecting texts and designing tasks for the students to practice correcting and editing the suggested texts. Results and discussion. The study confirmed the effectiveness of introducing lateral thinking techniques into the Ukrainian language teaching editing; resulted in designing sample exercises with instructions to be included in the high school curriculum for developing the skills of text production and editing. Translations as a variety of texts to edit have proven to stimulate students’ interest and creativity. Further steps are to be taken to compile a manual for teachers to implement lateral thinking techniques into the Ukrainian language teaching editing to foster high schoolers building up their text production and editing competences.
CPiX: Real-Time Analytics Over Out-of-Order Data Streams by Incremental Sliding-Window Aggregation

Stream processing is used in various fields. In the field of big data, stream aggregation is a popular processing technique, but it suffers serious setbacks when the order of events (e.g., stream elements) occurring is different from the order of events arriving to the systems. Such data streams are called "non-FIFO steams". This phenomenon usually occurs in a distributed environment due to many factors, such as network disruptions, delays, etc. Many analyzing scenarios require efficient processing of such non-FIFO streams to meet various data processing requirements. This paper proposes an efficient scalable checkpoint-based bidirectional indexing approach, called CPiX , for faster real-time analysis over non-FIFO streams. CPiX maintains the partial aggregation results in an on-demand manner. CPiX needs less time and space than the state-of-the-art approach. Extensive experiments confirm that CPiX can deal with out-of-order streams very efficiently and is, on average, about 3.8 times faster than the state-of-the-art approach while consuming less memory. CPiX and the existing approaches support the distributive and algebraic aggregation functions, such as min, average, standard deviation, etc. Holistic aggregation is beyond the scope.
Demystifying Bitcoin Address Behavior via Graph Neural Networks

Bitcoin is one of the decentralized cryptocurrencies powered by a peer-to-peer blockchain network. Parties who trade in the bitcoin network are not required to disclose any personal information. Such property of anonymity, however, precipitates potential malicious transactions to a certain extent. Indeed, various illegal activities such as money laundering, dark network trading, and gambling in the bitcoin network are nothing new now. While a proliferation of work has been developed to identify malicious bitcoin transactions, the behavior analysis and classification of bitcoin addresses are largely overlooked by existing tools. In this paper, we propose BAClassifier, a tool that can automatically classify bitcoin addresses based on their behaviors. Technically, we come up with the following three key designs. First, we consider casting the transactions of the bitcoin address into an address graph structure, of which we introduce a graph node compression technique and a graph structure augmentation method to characterize a unified graph representation. Furthermore, we leverage a graph feature network to learn the graph representations of each address and generate the graph embeddings. Finally, we aggregate all graph embeddings of an address into the address-level representation, and engage in a classification model to give the address behavior classification. As a side contribution, we construct and release a large-scale annotated dataset that consists of over 2 million real-world bitcoin addresses and concerns 4 types of address behaviors. Experimental results demonstrate that our proposed framework outperforms state-of-the-art bitcoin address classifiers and existing classification models, where the precision and F1-score are 96% and 95%, respectively. Our implementation and dataset are released, hoping to inspire others.
Orchestrating Digital Wallets for On- and Off-Chain Decentralized Identity Management

Digital identity is becoming one of the core elements during the digitalization age, when more and more processes and interactions are taking place in the digital sphere. Therefore, current identity management approaches will define how these interactions will look in the future, but different fields and communities often approach management with their own solutions and tools, despite their similarities. This includes decentralized digital identities, where the identity is managed with asymmetric cryptographic keys, and no centralized entity oversees the whole identity system. This paper focuses on managing on- and off-chain decentralized digital identities, with the former being used for blockchain networks and the latter for self-sovereignty and privacy. While both types of decentralized identity build on the same cryptographic and identity primitives, there is no single wallet that handles both. Therefore, this paper proposes an orchestration solution for both wallet types, which enables their convergence to a single universal wallet and validates it with a real-life decentralized identity use case.
A Composite Service Selection Approach Based on Functionality Folding and Reinforcement Learning

The current service composition approaches usually assume that the candidate services among different abstract tasks are independent, and they select the optimal candidate service sequentially for each abstract task based on the constraints. However, such solutions are not suitable for the situation that the coarse-grained services implement multiple abstract tasks. In this paper, we propose an approach to model task correlation for service composition as set cover problem and using the DQN algorithm to find the optimal solution.
Most Probable Densest Subgraphs

Computing the densest subgraph is a primitive graph operation with critical applications in detecting communities, events, and anomalies in biological, social, Web, and financial networks. In this paper, we study the novel problem of Most Probable Densest Subgraph (MPDS) discovery in uncertain graphs: Find the node set that is the most likely to induce a densest subgraph in an uncertain graph. We further extend our problem by considering various notions of density, e.g., clique and pattern densities, studying the top-k MPDSs, and finding the node set with the largest containment probability within densest subgraphs. We show that it is #P-hard to compute the probability of a node set inducing a densest subgraph. We then devise sampling-based efficient algorithms, with end-to-end accuracy guarantees, to compute the MPDS. Our thorough experimental results and real-world case studies on brain and social networks validate the effectiveness, efficiency, and usefulness of our solution.
Research on Quantum Blockchain Domestic Cryptographic Algorithm Support Library

The blockchain is jointly maintained by multiple parties, and cryptography is used to ensure the security of transmission and access. At present, the United States no longer provides key technology of blockchain (national security technology) to competitors. Facing the increasingly tense international situation, the development of China’s blockchain must adhere to an independent and controllable development path. In this context, this paper is based on the research of quantum blockchain key distribution mechanism and domestic cryptographic blockchain algorithm system, discusses the application method of quantum cryptography and domestic cryptographic algorithm in blockchain technology, and builds an autonomous and controllable quantum area The block chain domestic cryptographic algorithm support library provides specific reference suggestions for the development of block chain security technology.
Extracting Graphs Properties with Semantic Joins

This paper proposes an approach to querying a relational database $\mathcal{D}$ and a graph G taken together in SQL. We introduce a semantic extension of joins across $\mathcal{D}$ and G such that if a tuple t in $\mathcal{D}$ and a vertex v in G refer to the same real-world entity, then we join t and v to correlate their information and complement tuple t with additional properties of vertex v from the graph. Moreover, we extract hidden relationships between t and other entities by exploring paths from v. To support the semantic joins, we develop an extraction scheme based on LSTM, path clustering and ranking, to fetch important properties from graphs, and incrementally maintain the extracted data in response to updates. We also provide methods for implementing static joins when t is a tuple in $\mathcal{D}$, dynamic joins when t comes from the intermediate result of a sub-query, and heuristic joins to strike a balance between the complexity and accuracy. Using real-life data and queries, we experimentally verify the effectiveness, scalability and efficiency of the methods.
Deep Bayesian Active Learning for Learning to Rank: A Case Study in Answer Selection (Extended Abstract)

Active learning can select informative data for model training to reduce the amount of labelling efforts required. Because traditional active learning methods cannot be directly used for deep learning, researchers have proposed multiple deep active learning methods. However, none of the previous research efforts on deep active learning algorithms presents a specific framework for learning-to-rank tasks. In this work, we introduce a novel deep active learning framework based on Deep Expected Loss Optimization (DELO) for the answer selection task.
PENGARUH METODE PERCOBAAN TERHADAP LITERASI SAINS DAN HASIL BELAJAR KOGNITIF SISWA KELAS V SEKOLAH DASAR KECAMATAN CAKRANEGARA

According to the results of PISA 2018 and researcher’s observations, scientific literacy skills and students' cognitive learning outcomes are still lacking. This study aims to determine the effects of science experiments on scientific literacy and student cognitive learning outcomes. This research design used a non-equivalent control group with a quasiexperimental type. The research sample was consisted of 3 fifth grade elementary schools located at Cakranegara region. The data were collected through tests and questionnaires filled out by students. Data were analyzed using independent sample t-test and MANOVA. The analysis showed the following results: first, the scientific literacy ability of students who took part in learning using the experimental method has significant more influential than students who took part in learning using the lecture method (tcount = 8.926; p &lt;0.05). Furthermore, both cognitive learning outcomes of students who took part in learning using the experimental method were significantly more influential than students who took part in learning using the lecture method (tcount = 4.435; p &lt;0.05). Third, the scientific literacy ability and cognitive learning outcomes of students who participated in learning using the experimental method were significantly more influential than students who participated in the lecture learning method (F = 42,375; p &lt; 0.05). From these results, it could be concluded that the experimental method can be applied by teachers as one of the learning methods that has a positive influence on the progress of scientific literacy skills and students' cognitive learning outcomes in science learning
ATNAS: Automatic Termination for Neural Architecture Search

Neural architecture search (NAS) is a framework for automating the design process of a neural network structure. While the recent one-shot approaches have reduced the search cost, there still exists an inherent trade-off between cost and performance. It is important to appropriately stop the search and further reduce the high cost of NAS. Meanwhile, the differentiable architecture search (DARTS), a typical one-shot approach, is known to suffer from overfitting. Heuristic early-stopping strategies have been proposed to overcome such performance degradation. In this paper, we propose a more versatile and principled early-stopping criterion on the basis of the evaluation of a gap between expectation values of generalisation errors of the previous and current search steps with respect to the architecture parameters. The stopping threshold is automatically determined at each search epoch without cost. In numerical experiments, we demonstrate the effectiveness of the proposed method. We stop the one-shot NAS algorithms and evaluate the acquired architectures on the benchmark datasets: NAS-Bench-201 and NATS-Bench. Our algorithm is shown to reduce the cost of the search process while maintaining a high performance.
Probabilistic Majority Rule-Based Group Recommendation

Group recommendation has received increased attention over the past decade. The fundamental challenge in group recommendation is how to aggregate the preferences of group members to select a set of items maximizing the overall satisfaction of the group. Different aggregation methods with different semantics have been proposed. In this paper, we explore a novel semantics of group recommendation, that is, probabilistic majority rule, allowing group members to make a "democratic" decision on which items are appropriate. Specifically, we propose a probabilistic model that captures the probability that a given item satisfies the majority of the group. We show that the naive strategy for computing such a probability is exponential time complexity, and propose an efficient dynamic programming approach to avoid this shortcoming. Furthermore, we design and develop an efficient algorithm, which leverages effective pruning techniques, for recommending the k items with the highest majority satisfaction probabilities. Finally, we demonstrate both the retrieval effectiveness and the efficiency of our approach through extensive experimental evaluation on real datasets.
SChain: Scalable Concurrency over Flexible Permissioned Blockchain

Permissioned blockchains are being widely applied to solve the trust problem in enterprise collaboration. However, most of these systems suffer from low throughput and flexibility lacking issues. In this paper, we present a blockchain system SChain with scalable concurrent execution based on a flexible architecture. SChain separates the functionality of a complete "node" into three sub-functions and assigns them to different peers within every organization. Then each organization can scale each sub-function flexibly with no need for negotiation between organizations. Based on this architecture, SChain explores scalable concurrent execution from two levels. First, SChain takes the advantage of multiple peers to execute transactions collectively, while promising they make the same results as one peer does serially. Second, SChain enables concurrent transaction execution across blocks to utilize the resources of peers fully, breaking up the block-by-block process manner, based on a pipelined workflow. The extensive evaluation results demonstrate that SChain significantly outperforms the serial execution and other competing systems-level approaches.
An efficient LSTM based cross domain aspect based sentiment analysis (CD-ABSA)

This research study focuses the cross-domain aspect-based sentiment analysis (CD-ABSA) for existing source domain annotation data. The CD-ABSA tries to use the valuable information in a source domain to extract aspect terms and evaluate their sentiment polarities in a target domain. It can considerably increase the usage of the source domain annotation resources while also reducing the workload of newer domain data annotation. one of the main components of the CD-ABSA is aspect extraction. In this paper, we utilized the most common topic modelling techniques: LDA and LSA to extract aspects from the reviews as it does not require labelled data. The topics are extracted from the education domain of the Course and Teacher Performance Evaluation (CTPE) dataset. In this paper, we also evaluated the different hyper-parameters on the CD-ABSA model and selected the best and optimal combination. The proposed methodology train on domain-dependent and independent word embedding that achieves CD-ABSA, in particularly end-to-end fashion. The experiment carries out on Academica dataset, which consists of students’ comments/feedback and SemEval-2014 dataset, which includes laptops and restaurants reviews. The evaluation metrics such as (precision, recall, F1 score and validation Accuracy) is considered while judging the LSTM classifier performance for CD-ABSA as a result.
VC-dimension and Rademacher Averages of Subgraphs, with Applications to Graph Mining

Frequent subgraph mining is a fundamental task in the analysis of collections of graphs. While several exact approaches have been proposed, it remains computationally challenging on large graph datasets due to its inherent link to the subgraph isomorphism problem and the huge number of candidate patterns even for fairly small subgraphs.In this work, we study two statistical learning measures of complexity, VC-dimension and Rademacher averages, for subgraphs, and derive efficiently computable bounds for both. We show how such bounds can be applied to devise efficient sampling-based approaches for rigorously approximating the solution of the frequent subgraph mining problem. We also show that our bounds can be used for true frequent subgraph mining, which requires to identify subgraphs generated with probability above a given threshold from an unknown generative process using samples from such process. Our extensive experimental evaluation on real datasets shows that our bounds lead to efficiently computable, high-quality approximations for both applications.
Reprojection-Free VR Passthrough

Virtual reality (VR) passthrough uses external cameras on the front of a headset to allow the user to see their environment. However, passthrough cameras cannot physically be co-located with the user's eyes, so the passthrough images have a different perspective than what the user would see without the headset. Although the images can be computationally reprojected into the desired view, errors in depth estimation and missing information at occlusion boundaries can lead to undesirable artifacts.
Fast Unsupervised Graph Embedding via Graph Zoom Learning

Unsupervised graph representation learning, i.e., learning node or graph embeddings from graph data in an unsupervised manner, has become an important problem when we study graph data. With the development of self-supervised learning, researchers have designed graph-level self-supervised learning paradigms and learn embeddings under these paradigms. The learned embeddings can serve as a fine initial solution to downstream tasks such as node classification or graph classification. In this paper, we propose a fast unsupervised graph embedding method, which follows the way of self-supervised learning. This method performs representation learning on the graph under a novel concept called Graph Zoom Learning (abbr. GZL), which is orthogonal to the existing concepts of unsupervised graph embedding, such as random walk and contrastive learning. Two crucial components, graph zoom-out and point-to-point contrast, help GZL reduce the overall training time cost. Specifically, on the one hand, a lightweight miniature graph is generated from the raw graph by graph zoom-out and the learning on the miniature graph is more efficient than the learning on the raw graph; on the other hand, we design the miniature-scale learning on the miniature graph and introduce community structure into this learning pattern, which contributes to the final point-to-point contrast. Since point-to-point contrast is independent of negatives, it makes the whole training more efficient. We conduct extensive experiments to verify the advantage of GZL on representation learning. On two downstream tasks of node classification and graph classification, GZL outperforms the state-of-the-art unsupervised graph embedding methods. Particularly, on the largest experimental graph dataset (ogbn-arxiv) with 169k nodes and 1.1m edges, GZL outperforms the runner-up by 3.3% relative accuracy and achieves up to 22.6x speedup over it.
GAN-Based Facial Attribute Manipulation

Facial Attribute Manipulation (FAM) aims to aesthetically modify a given face image to render desired attributes, which has received significant attention due to its broad practical applications ranging from digital entertainment to biometric forensics. In the last decade, with the remarkable success of Generative Adversarial Networks (GANs) in synthesizing realistic images, numerous GAN-based models have been proposed to solve FAM with various problem formulation approaches and guiding information representations. This paper presents a comprehensive survey of GAN-based FAM methods with a focus on summarizing their principal motivations and technical details. The main contents of this survey include: (i) an introduction to the research background and basic concepts related to FAM, (ii) a systematic review of GAN-based FAM methods in three main categories, and (iii) an in-depth discussion of important properties of FAM methods, open issues, and future research directions. This survey not only builds a good starting point for researchers new to this field but also serves as a reference for the vision community.
Image vectorization and editing via linear gradient layer decomposition

A key advantage of vector graphics over raster graphics is their editability. For example, linear gradients define a spatially varying color fill with a few intuitive parameters, which are ubiquitously supported in standard vector graphics formats and libraries. By layering regions filled with linear gradients, complex appearances can be created. We propose an automatic method to convert a raster image into layered regions of linear gradients. Given an input raster image segmented into regions, our approach decomposes the resulting regions into opaque and semi-transparent linear gradient fills. Our approach is fully automatic (e.g., users do not identify a background as in previous approaches) and exhaustively considers all possible decompositions that satisfy perceptual cues. Experiments on a variety of images demonstrate that our method is robust and effective.