81 research outputs found
Automated Interactive Visualization on Abstract Concepts in Computer Science
The paper presents CSVisFrame, a framework formaking visualizations, which solves the understanding difficultyon learning abstract concepts in computer science including datastructures and algorithms. With the framework, instructors anddevelopers can develop all varieties of interactive visualizations,with which students can learn and understand abstract conceptsin computer science more easily.CSVisFrame has been applied to both offline and onlinecomputer courses. Students from Sichuan Normal Universityhave enjoyed visualizations based on CSVisFrame in their AlgorithmDesign and Analysis course, and thousands of students ofJisuanke have benefitted from online CSVisFrame-based visualizedcomputer science courses. The effectiveness of CSVisFramebasedvisualizations has been demonstrated by our sample survey,which shows that visualizations are widely accepted, and almostall students can achieve a better learning. CSVisFrame is opensourced1,and demonstrations based on CSVisFrame are free2
Identifying Crypto Addresses with Gambling Behaviors: A Graph Neural Network Approach
The development of blockchain technology has brought prosperity to the cryptocurrency market and has made the blockchain platform a hotbed of crimes. As one of the most rampant crimes, crypto gambling has more high risk of illegal activities due to the lack of regulation. As a result, identifying crypto addresses with gambling behaviors has emerged as a significant research topic. In this work, we propose a novel detection approach based on Graph Neural Networks named CGDetector, consisting of Graph Construction, Subgraph Extractor, Statistical Feature Extraction, and Gambling Address Classification. Extensive experiments of large-scale and heterogeneous Ethereum transaction data are implemented to demonstrate that our proposed approach outperforms state-of-the-art address classifiers of traditional machine learning methods. This work makes the first attempt to detect suspicious crypto gambling addresses via Graph Neural Networks by all EVM-compatible blockchain systems, providing new insights into the field of cryptocurrency crime detection and blockchain security regulation
Constructing an Interaction Behavior Model for Web Image Search
User interaction behavior is a valuable source of implicit relevance
feedback. In Web image search a different type of search result presentation is
used than in general Web search, which leads to different interaction
mechanisms and user behavior. For example, image search results are
self-contained, so that users do not need to click the results to view the
landing page as in general Web search, which generates sparse click data. Also,
two-dimensional result placement instead of a linear result list makes browsing
behaviors more complex. Thus, it is hard to apply standard user behavior models
(e.g., click models) developed for general Web search to Web image search.
In this paper, we conduct a comprehensive image search user behavior analysis
using data from a lab-based user study as well as data from a commercial search
log. We then propose a novel interaction behavior model, called grid-based user
browsing model (GUBM), whose design is motivated by observations from our data
analysis. GUBM can both capture users' interaction behavior, including cursor
hovering, and alleviate position bias. The advantages of GUBM are two-fold: (1)
It is based on an unsupervised learning method and does not need manually
annotated data for training. (2) It is based on user interaction features on
search engine result pages (SERPs) and is easily transferable to other
scenarios that have a grid-based interface such as video search engines. We
conduct extensive experiments to test the performance of our model using a
large-scale commercial image search log. Experimental results show that in
terms of behavior prediction (perplexity), and topical relevance and image
quality (normalized discounted cumulative gain (NDCG)), GUBM outperforms
state-of-the-art baseline models as well as the original ranking. We make the
implementation of GUBM and related datasets publicly available for future
studies.Comment: 10 page
Constructing Tree-based Index for Efficient and Effective Dense Retrieval
Recent studies have shown that Dense Retrieval (DR) techniques can
significantly improve the performance of first-stage retrieval in IR systems.
Despite its empirical effectiveness, the application of DR is still limited. In
contrast to statistic retrieval models that rely on highly efficient inverted
index solutions, DR models build dense embeddings that are difficult to be
pre-processed with most existing search indexing systems. To avoid the
expensive cost of brute-force search, the Approximate Nearest Neighbor (ANN)
algorithm and corresponding indexes are widely applied to speed up the
inference process of DR models. Unfortunately, while ANN can improve the
efficiency of DR models, it usually comes with a significant price on retrieval
performance.
To solve this issue, we propose JTR, which stands for Joint optimization of
TRee-based index and query encoding. Specifically, we design a new unified
contrastive learning loss to train tree-based index and query encoder in an
end-to-end manner. The tree-based negative sampling strategy is applied to make
the tree have the maximum heap property, which supports the effectiveness of
beam search well. Moreover, we treat the cluster assignment as an optimization
problem to update the tree-based index that allows overlapped clustering. We
evaluate JTR on numerous popular retrieval benchmarks. Experimental results
show that JTR achieves better retrieval performance while retaining high system
efficiency compared with widely-adopted baselines. It provides a potential
solution to balance efficiency and effectiveness in neural retrieval system
designs.Comment: 10 pages, accepted at SIGIR 202
Evaluating Interpolation and Extrapolation Performance of Neural Retrieval Models
A retrieval model should not only interpolate the training data but also
extrapolate well to the queries that are different from the training data.
While neural retrieval models have demonstrated impressive performance on
ad-hoc search benchmarks, we still know little about how they perform in terms
of interpolation and extrapolation. In this paper, we demonstrate the
importance of separately evaluating the two capabilities of neural retrieval
models. Firstly, we examine existing ad-hoc search benchmarks from the two
perspectives. We investigate the distribution of training and test data and
find a considerable overlap in query entities, query intent, and relevance
labels. This finding implies that the evaluation on these test sets is biased
toward interpolation and cannot accurately reflect the extrapolation capacity.
Secondly, we propose a novel evaluation protocol to separately evaluate the
interpolation and extrapolation performance on existing benchmark datasets. It
resamples the training and test data based on query similarity and utilizes the
resampled dataset for training and evaluation. Finally, we leverage the
proposed evaluation protocol to comprehensively revisit a number of
widely-adopted neural retrieval models. Results show models perform differently
when moving from interpolation to extrapolation. For example,
representation-based retrieval models perform almost as well as
interaction-based retrieval models in terms of interpolation but not
extrapolation. Therefore, it is necessary to separately evaluate both
interpolation and extrapolation performance and the proposed resampling method
serves as a simple yet effective evaluation tool for future IR studies.Comment: CIKM 2022 Full Pape
Controlling Lateral Fano Interference Optical Force with Au-Ge2Sb2Te5 Hybrid Nanostructure
We numerically demonstrate that a pronounced dipole–quadrupole (DQ) Fano resonance (FR) induced lateral force can be exerted on a dielectric particle 80 nm in radius (Rsphere = 80 nm) that is placed 5 nm above an asymmetric bow-tie nanoantenna array based on Au/Ge2Sb2Te5 dual layers. The DQ-FR-induced lateral force achieves a broad tuning range in the mid-infrared region by changing the states of the Ge2Sb2Te5 dielectric layer between amorphous and crystalline and in turn pushes the nanoparticle sideways in the opposite direction for a given wavelength. The mechanism of lateral force reversal is revealed through optical singularity in the Poynting vector. A thermal–electric simulation is adopted to investigate the temporal change of the Ge2Sb2Te5 film’s temperature, which demonstrates the possibility of transiting the Ge2Sb2Te5 state by electrical heating. Our mechanism by tailoring the DQ-FR-induced lateral force presents clear advantages over the conventional nanoparticle manipulation techniques: it possesses a pronounced sideways force under a low incident light intensity of 10 mW/μm2, a fast switching time of 2.6 μs, and a large tunable wavelength range. It results in a better freedom in flexible nanomechanical control and may provide a new means of biomedical sensing and nano-optical conveyor belts
An Intent Taxonomy of Legal Case Retrieval
Legal case retrieval is a special Information Retrieval~(IR) task focusing on
legal case documents. Depending on the downstream tasks of the retrieved case
documents, users' information needs in legal case retrieval could be
significantly different from those in Web search and traditional ad-hoc
retrieval tasks. While there are several studies that retrieve legal cases
based on text similarity, the underlying search intents of legal retrieval
users, as shown in this paper, are more complicated than that yet mostly
unexplored. To this end, we present a novel hierarchical intent taxonomy of
legal case retrieval. It consists of five intent types categorized by three
criteria, i.e., search for Particular Case(s), Characterization, Penalty,
Procedure, and Interest. The taxonomy was constructed transparently and
evaluated extensively through interviews, editorial user studies, and query log
analysis. Through a laboratory user study, we reveal significant differences in
user behavior and satisfaction under different search intents in legal case
retrieval. Furthermore, we apply the proposed taxonomy to various downstream
legal retrieval tasks, e.g., result ranking and satisfaction prediction, and
demonstrate its effectiveness. Our work provides important insights into the
understanding of user intents in legal case retrieval and potentially leads to
better retrieval techniques in the legal domain, such as intent-aware ranking
strategies and evaluation methodologies.Comment: 28 pages, work in proces
Major mergers going Notts: challenges for modern halo finders
Merging haloes with similar masses (i.e. major mergers) pose significant challenges for halo finders. We compare five halo-finding algorithms’ (ahf, hbt, rockstar, subfind, and velociraptor) recovery of halo properties for both isolated and cosmological major mergers. We find that halo positions and velocities are often robust, but mass biases exist for every technique. The algorithms also show strong disagreement in the prevalence and duration of major mergers, especially at high redshifts (z > 1). This raises significant uncertainties for theoretical models that require major mergers for, e.g. galaxy morphology changes, size changes, or black hole growth, as well as for finding Bullet Cluster analogues. All finders not using temporal information also show host halo and subhalo relationship swaps over successive timesteps, requiring careful merger tree construction to avoid problematic mass accretion histories. We suggest that future algorithms should combine phase-space and temporal information to avoid the issues presented
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