Reviewerly/exHarmony huggingface dataset:
Ready, Aim, Snipe! Analysis of Sniper Bots and their Impact on the DeFi Ecosystem

In the world of cryptocurrencies, public listing of a new token often generates significant hype, in many cases causing its price to skyrocket in a few seconds. In this scenario, timing is crucial to determine the success or failure of an investment opportunity. In this work, we present an in-depth analysis of sniper bots, automated tools designed to buy tokens as soon as they are listed on the market. We leverage GitHub open-source repositories of sniper bots to analyze their features and how they are implemented. Then, we build a dataset of Ethereum and BNB Smart Chain (BSC) liquidity pools to identify addresses that serially take advantage of sniper bots. Our findings reveal 14,029 sniping operations on Ethereum and 1,395,042 in BSC that bought tokens for a total of $10,144,808 dollars and $18,720,447, respectively. We find that Ethereum operations have a higher success rate but require a larger investment. Finally, we analyze token smart contracts to identify mechanisms that can hinder sniper bots.
The 2nd Workshop on Interactive and Scalable Information Retrieval Methods for eCommerce (ISIR-eCom)

introduction Share on The 2nd Workshop on Interactive and Scalable Information Retrieval Methods for eCommerce (ISIR-eCom) Authors: Vachik S. Dave Walmart Global Tech, USA Walmart Global Tech, USA 0009-0004-1963-8415Search about this author , Linsey Pang Salesforce, USA Salesforce, USA 0000-0002-4784-9795Search about this author , Xiquan Cui The Home Depot, USA The Home Depot, USA 0009-0005-5306-8839Search about this author , Lingfei Wu Pinterest, USA Pinterest, USA 0009-0008-8081-6275Search about this author , Hamed Zamani University of Massachusetts Amherst, USA University of Massachusetts Amherst, USA 0000-0002-0800-3340Search about this author , George Karypis University of Minnesota in the Twin Cities of Minneapolis and Saint Paul, USA University of Minnesota in the Twin Cities of Minneapolis and Saint Paul, USA 0000-0003-2753-1437Search about this author Authors Info & Claims WWW '23 Companion: Companion Proceedings of the ACM Web Conference 2023April 2023Pages 850–853https://doi.org/10.1145/3543873.3589753Published:30 April 2023Publication History 0citation39DownloadsMetricsTotal Citations0Total Downloads39Last 12 Months39Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Injecting data into ODRL privacy policies dynamically with RDF mappings

The privacy of the data provided by available sources is one of the major concerns of our era. In order to address this challenge, the W3C has promoted recommendations to allow expressing privacy policies. One of these recommendations is the Open Digital Rights Language (ODRL) vocabulary. Although this standard has wide adoption, it is not suitable in domains such as IoT, Ubiquitous and Mobile Computing, or discovery. The reason behind is the fact that ODRL privacy policies are not able to cope with dynamic information that may come from external sources of data and, therefore, these policies can not define privacy restrictions upon data that is not already written in the policy beforehand. In this demo paper, a solution to this challenge is presented. It is shown how ODRL policies can overcome the aforementioned limitation by being combined with a mapping language for RDF materialisation. The article shows how ODRL policies are able to consider data coming from an external data source when they are solved, in particular, a weather forecast API that provides temperature values. The demonstration defines an ODRL policy that grants access to a resource only when the temperature of the API is above a certain value.
Depicting Vocabulary Summaries with Devos

Communicating ontologies to potential users is still a difficult and time-consuming task. Even for small ones, users need to invest time to determine whether to reuse them. Providing diagrams together with the ontologies facilitates the task of understanding the model from a user perspective. While some tools are available for depicting ontologies, and the code could also be inspected using ontology editors’ graphical interfaces, in many cases, the diagrams are too big or complex. The main objective of this demo is to present Devos, a system to generate ontology diagrams based on different strategies for summarizing the ontology.
Persona Consistent Dialogue Generation via Contrastive Learning

The inclusion of explicit personas in generation models has gained significant attention as a means of developing intelligent dialogue agents. However, large pretrained generation models often produce inconsistent responses with persona. We investigate the model generation behavior to identify signs of inconsistency and observe inconsistent behavior patterns. In this work, we introduce contrastive learning into persona consistent dialogue generation, building on the idea that humans learn not just from positive feedback, but also from identifying and correcting undesirable behaviors. According to the inconsistent patterns, we design two strategies to construct high-quality negative samples, which are critical for contrastive learning efficacy. Experimental results demonstrate that our method can effectively improve the consistency of the responses while improving its dialogue quality on both automatic and human evaluation.
Public Spot Instance Dataset Archive Service

Spot instances offered by major cloud vendors allow users to use cloud instances cost-effectively but with the risk of sudden instance interruption. To enable efficient use of spot instances by users, cloud vendors provide various datasets that reflect the current status of spot instance services, such as savings ratio, interrupt ratio, and instant availability. However, this information is scattered, and they require distinct access mechanisms and pose query constraints. Hence, ordinary users find it difficult to use the dataset to optimize spot instance usage. To resolve this issue, we propose a multi-cloud spot instance dataset service that is publicly available. This will help cloud users and system researchers to use spot instances from multiple cloud vendors to build a cost-efficient and reliable environment expediting cloud system research.
On Graph Time-Series Representations for Temporal Networks

Representations of temporal networks arising from a stream of edges lie at the heart of models learned on it and its performance on downstream applications. While previous work on dynamic modeling and embedding have focused on representing a stream of timestamped edges using a time-series of graphs based on a specific time-scale τ (e.g., 1 month), we introduce the notion of an ϵ -graph time-series that uses a fixed number of edges for each graph, and show its effectiveness in capturing fundamental structural graph statistics over time. The results indicate that the ϵ -graph time-series representation effectively captures the structural properties of the graphs across time whereas the commonly used τ -graph time-series representation captures the frequency of edges and temporal patterns with respect to their arrival in the application time. These results have many important implications especially on the design of new GNN-based models for temporal networks as well as for understanding existing models and their limitations.
MPKGAC: Multimodal Product Attribute Completion in E-commerce

Product attributes can display the selling points of products, helping users find their desired products in search results. However, product attributes are typically incomplete. In e-commerce, products have multimodal features, including original attributes, images, and texts. How to make full use of the multimodal data to complete the missing attributes is the key challenge. To this end, we propose MPKGAC, a powerful three-stream framework that handles multimodal product data for attribute completion. We build a multimodal product knowledge graph (KG) from the multimodal features, and then convert the attribute completion problem into a multimodal KG completion task. MPKGAC encodes each modality separately, fuses them adaptively, and integrates multimodal decoders for prediction. Experiments show that MPKGAC outperforms the best baseline by 6.2% in [email protected] MPKGAC is employed to enrich selling points of the women’s clothing industry at Alibaba.com.cn and improves the click-through rate (CTR) by a relative 2.14%.
Task-Specific Data Augmentation for Zero-shot and Few-shot Stance Detection

Various targets keep coming up on social media, and most of them lack labeled data. In this paper, we focus on zero-shot and few-shot stance detection, which aims to identify stances with few or even no training instances. In order to solve the lack of labeled data and implicit stance expression, we propose a self-supervised data augment approach based on coreference resolution. The method is specific for stance detection to generate more stable data and reduce the variance within and between classes to achieve a balance between validity and robustness. Considering the diversity of comments, we propose a novel multi-task stance detection framework of target-related fragment extraction and stance detection, which can enhance attention on target-related fragments and reduce the noise of other fragments. Experiments show that the proposed approach achieves state-of-the-art performance in zero-shot and few-shot stance detection.
Augmenting Visualizations with Predictive and Investigative Insights to Facilitate Decision Making

Many people find it difficult to comprehend basic charts on the web, let alone make effective decisions from them. To address this gap, several ML models aim to automatically detect useful insights from charts and narrate them in a simpler textual format. However, most of these solutions can only detect basic factual insights (a.k.a. descriptive insights) that are already present in the chart, which may help with chart comprehension, but not decision-making. In this work, we study whether more advanced predictive and investigative insights can help users understand what will happen next and what actions they should take. These advanced insights can help decision-makers better understand the reasons behind anomaly events, predict future unfolding trends, and recommend possible actions for optimizing business outcomes. Through a study with 18 participants, we found that predictive and investigative insights lead to more insights recorded by users on average and better effectiveness ratings.
Incorporating Embedding to Topic Modeling for More Effective Short Text Analysis

With the growing abundance of short text content on websites, analyzing and comprehending these short texts has become a crucial task. Topic modeling is a widely used technique for analyzing short text documents and uncovering the underlying topics. However, traditional topic models face difficulties in accurately extracting topics from short texts due to limited content and their sparse nature. To address these issues, we propose an Embedding-based topic modeling (EmTM) approach that incorporates word embedding and hierarchical clustering to identify significant topics. Experimental results demonstrate the effectiveness of EmTM on two datasets comprising web short texts, Snippet and News. The results indicate a superiority of EmTM over baseline topic models by its exceptional performance in both classification accuracy and topic coherence metrics.
Dimension shifting based intelligent algorithm framework to solve conditional nonlinear optimal perturbation

Conditional Nonlinear Optimal Perturbation (CNOP) method is an effective way to study the predictability of oceanic and climatic events. A framework combining the Feature Extraction method and Intelligent Algorithm (FEIA) is frequently used to solve CNOP because it is gradient-free and scalable at high-dimensional scales. However, the fixed latent subspace of FEIA framework makes it challenging to achieve both the quality of solutions and the solving efficiency. To overcome this bottleneck, this paper proposes Dimension Shifting based Intelligent Algorithm (DSIA) framework to solve CNOP in the large-scale model. DSIA framework adopts the dimension shifting strategy, which dynamically shifts search particles in different low-dimensional spaces. To verify the feasibility of DISA framework, we take a Regional Ocean Modeling System (ROMS) model of double-gyre variation as an experimental case. In experiments, we figure out that the selection of feature extraction method and the shifting dimension set are two influential factors for the performance of DSIA framework. Besides, in comparative experiments, DSIA framework yields better objective function values and more valid CNOP than FEIA framework. Moreover, convergence experiments demonstrate DSIA framework can solve CNOP with an appropriate number of function evaluations and has better convergence performance. In conclusion, experimental results prove that DSIA improves both quality of CNOP and solving efficiency.
SEKA: Seeking Knowledge Graph Anomalies

Knowledge Graphs (KGs) form the backbone of many knowledge dependent applications such as search engines and digital personal assistants. KGs are generally constructed either manually or automatically using a variety of extraction techniques applied over multiple data sources. Due to the diverse quality of these data sources, there are likely anomalies introduced into any KG. Hence, it is unrealistic to expect a perfect archive of knowledge. Given how large KGs can be, manual validation is impractical, necessitating an automated approach for anomaly detection in KGs. To improve KG quality, and to identify interesting and abnormal triples (edges) and entities (nodes) that are worth investigating, we introduce SEKA, a novel unsupervised approach to detect anomalous triples and entities in a KG using both the structural characteristics and the content of edges and nodes of the graph. While an anomaly can be an interesting or unusual discovery, such as a fraudulent transaction requiring human intervention, anomaly detection can also identify potential errors. We propose a novel approach named Corroborative Path Algorithm to generate a matrix of semantic features, which we then use to train a one-class Support Vector Machine to identify abnormal triples and entities with no dependency on external sources. We evaluate our approach on four real-world KGs demonstrating the ability of SEKA to detect anomalies, and to outperform comparative baselines.
ProCon: An automated process-centric quality constraints checking framework

When dealing with safety–critical systems, various regulations, standards, and guidelines stipulate stringent requirements for certification and traceability of artifacts, but typically lack details with regards to the corresponding software engineering process. Given the industrial practice of only using semi-formal notations for describing engineering processes – with the lack of proper tool mapping – engineers and developers need to invest a significant amount of time and effort to ensure that all steps mandated by quality assurance are followed. The sheer size and complexity of systems and regulations make manual, timely feedback from Quality Assurance (QA) engineers infeasible. In order to address these issues, in this paper, we propose a novel framework for tracking, and "passively" executing processes in the background, automatically checking QA constraints depending on process progress, and informing the developer of unfulfilled QA constraints. We evaluate our approach by applying it to three case studies: a safety–critical open-source community system, a safety–critical system in the air-traffic control domain, and a non-safety–critical, web-based system. Results from our analysis confirm that trace links are often corrected or completed after the work step has been considered finished, and the engineer has already moved on to another step. Thus, support for timely and automated constraint checking has significant potential to reduce rework as the engineer receives continuous feedback already during their work step.
C-Affinity: A Novel Similarity Measure for Effective Data Clustering

Clustering is widely employed in various applications as it is one of the most useful data mining techniques. In performing clustering, a similarity measure, which defines how similar a pair of data objects are, plays an important role. A similarity measure is employed by considering a target dataset’s characteristics. Current similarity measures (or distances) do not reflect the distribution of data objects in a dataset at all. From the clustering point of view, this fact may limit the clustering accuracy. In this paper, we propose c-affinity, a new notion of a similarity measure that reflects the distribution of objects in the given dataset from a clustering point of view. We design c-affinity between any two objects to have a higher value as they are more likely to belong to the same cluster by learning the data distribution. We use random walk with restart (RWR) on the k-nearest neighbor graph of the given dataset to measure (1) how similar a pair of objects are and (2) how densely other objects are distributed between them. Via extensive experiments on sixteen synthetic and real-world datasets, we verify that replacing the existing similarity measure with our c-affinity improves the clustering accuracy significantly.
Is the Impression Log Beneficial to Effective Model Training in News Recommender Systems? No, It’s NOT

The MIND dataset, one of the most-popular real-world news datasets, has been used in many news recommendation researches. They all employ the impression log as training data to train their models (i.e., impression-based training). In this paper, we claim that the impression log has the preference bias issue and thus should not be used for model training in news recommendation. We validate our claim via extensive experiments; we also demonstrate the dramatic improvement of recommendation accuracy in five existing state-of-the-art models up to 82.1% by simply ignoring the impression log. We believe this surprising result provides a new insight towards better model training for both researchers and practitioners working in a news recommendation area.
Machine Learning for Streaming Media

Streaming media has become a popular medium for consumers of all ages, with people spending several hours a day streaming videos, games, music, or podcasts across devices. Most global streaming services have introduced Machine Learning (ML) into their operations to personalize consumer experience, improve content, and further enhance the value proposition of streaming services. Despite the rapid growth, there is a need to bridge the gap between academic research and industry requirements and build connections between researchers and practitioners in the field. This workshop aims to provide a unique forum for practitioners and researchers interested in Machine Learning to get together, exchange ideas and get a pulse for the state of the art in research and burning issues in the industry.
Search Personalization at Netflix

At Netflix, personalization plays a key role in several aspects of our user experience, from ranking titles to constructing an optimal Homepage. Although personalization is a well established research field, its application to search presents unique problems and opportunities. In this paper, we describe the evolution of Search personalization at Netflix, its unique challenges, and provide a high level overview of relevant solutions.
A text-based syntax completion method using LR parsing and its evaluation

This paper presents a text-based syntax completion method that uses an LR parser. We propose formal definitions of candidate text to be completed based on the sentential forms. Moreover, we design basic algorithms for computing candidate texts through reductions in the LR parsing. This is unlike most existing methods, wherein the definition of candidates that are intended to be generated are given informally. In addition, this is unlike grammar transformation approaches that use LR parsers and is a currently burdensome task. The proposed method allows LR parsers to be adopted without modification and a syntax completion system to be built without incurring efforts. For practical purposes, we extended the basic algorithms using a new definition of refined candidates and a new strategy. The extended algorithms can compute more useful candidates for realistic programming language grammars than those of existing ones; Further, we implemented the algorithms on an Emacs server to demonstrate the feasibility of their application. We evaluated the extended algorithm with three real-world programming languages, Small Basic, C, and Haskell. The extended algorithm computes half of all candidates in less than or equal to 0.2 seconds and 89.2% in approximately one second in the evaluation while computing the remaining candidates took a long time. We discuss its evaluation in detail.
Dynamic Embedding-based Retrieval for Personalized Item Recommendations at Instacart

Personalization is essential in e-commerce, with item recommendation as a critical task. In this paper, we describe a hybrid embedding-based retrieval system for real-time personalized item recommendations on Instacart. Our system addresses unique challenges in the multi-source retrieval system, and includes several key components to make it highly personalized and dynamic. Specifically, our system features a hybrid embedding model that includes a long-term user interests embedding model and a real-time session-based model, which are combined to capture users’ immediate intents and historical interactions. Additionally, we have developed a contextual bandit solution to dynamically adjust the number of candidates from each source and optimally allocate retrieval slots given a limited computational budget. Our modeling and system optimization efforts have enabled us to provide highly personalized item recommendations in real-time at scale to all our customers, including new and long-standing users.
Curating and extending data for language comparison in Concepticon and NoRaRe

<ns4:p>Language comparison requires user-friendly tools that facilitate the standardization of linguistic data. We present two resources built on the basis of a standardized cross-linguistic format and show how the data is curated and extended. The first resource, the Concepticon, is a reference catalog for standardized concepts from linguistic research. While curating the Concepticon, we found that a variety of studies in distinct research fields collected information on word properties. However, until recently, no resource existed that contained these data to enable the comparison of the different word properties across languages. This gap was filled by the Database of Norms, Ratings, and Relations (NoRaRe), which is an extension of the Concepticon. Here, we present the major release of both resources - Concepticon Version 3.0 and NoRaRe Version 1.0 - which represents an important step in our data development. We show that extending and adapting the data curation workflow in Concepticon to NoRaRe is useful for the standardization of cross-linguistic datasets. In addition, combining datasets from different research fields enables studies grounded in language comparison. Concepticon and NoRaRe include lexical data for various languages, tools for test-driven data curation, and the possibility for data reuse. The first major release of NoRaRe is also accompanied by a new web application that allows convenient access to the data.</ns4:p>