Reviewerly/exHarmony huggingface dataset:
Extending Actor Models in Data Spaces

In today’s internet almost any party can share sets of data with each other. However, creating frameworks and regulated realms for the sharing of data is very complex when multiple parties are involved and complicated regulation comes into play. As solution data spaces were introduced to enable participating parties to share data among themselves in an organized, regulated and standardized way. However, contract data processors, acting as data space participants, are currently unable to execute data requests on behalf of their contract partners. Here we show that an on-behalf-of actor model can be easily added to existing data spaces. We demonstrate how this extension can be realized using verifiable credentials. We provide a sample use case, a detailed sequence diagram and discuss necessary architectural adaptations and additions to established protocols. Using the extensions explained in this work numerous real life use cases which previously could technically not be realized can now be covered. This enables future data spaces to provide more dynamic and complex real world use cases.
Search-based Recommendation: the Case for Difficult Predictions

Recommender systems have achieved impressive results on benchmark datasets. However, the numbers are often influenced by assumptions made on the data and evaluation mode. This work questions and revises these assumptions, to study and improve the quality, particularly for the difficult case of search-based recommendations. Users start with a personally liked item as a query and look for similar items that match their tastes. User satisfaction requires discovering truly unknown items: new authors of books rather than merely more books of known writers. We propose a unified system architecture that combines interaction-based and content-based signals and leverages language models for Transformer-powered predictions. We present new techniques for selecting negative training samples, and investigate their performance in the underexplored search-based evaluation mode.
CLIME: Completeness-Constrained LIME

We evaluate two popular local explainability techniques, LIME and SHAP, on a movie recommendation task. We discover that the two methods behave very differently depending on the sparsity of the data set, where sparsity is defined by the amount of historical viewing data available to explain a movie recommendation for a particular data instance. We find that LIME does better than SHAP in dense segments of the data set and SHAP does better in sparse segments. We trace this difference to the differing bias-variance characteristics of the underlying estimators of LIME and SHAP. We find that SHAP exhibits lower variance in sparse segments of the data compared to LIME. We attribute this lower variance to the completeness constraint property inherent in SHAP and missing in LIME. This constraint acts as a regularizer and therefore increases the bias of the SHAP estimator but decreases its variance, leading to a favorable bias-variance trade-off especially in high sparsity data settings. With this insight, we introduce the same constraint into LIME and formulate a novel local explainabilty framework called Completeness-Constrained LIME (CLIME) that is superior to LIME and much faster than SHAP.
Learned Temporal Aggregations for Fraud Classification on E-Commerce Platforms

Fraud and other types of adversarial behavior are serious problems on customer-to-customer (C2C) e-commerce platforms, where harmful behaviors by bad actors erode user trust and safety. Many modern e-commerce integrity systems utilize machine learning (ML) to detect fraud and bad actors. We discuss the practical problems faced by integrity systems which utilize data associated with user interactions with the platform. Specifically, we focus on the challenge of representing the user interaction events, and aggregating their features. We compare the performance of two paradigms to handle the feature temporality when training the ML models: hand-engineered temporal aggregation and a learned aggregation using a sequence encoder. We show that a model which learns a time-aggregation using a sequence encoder outperforms models trained on handcrafted aggregations on the fraud classification task with a real-world dataset.
The Web and Linked Data as a Solid Foundation for Dataspaces

The concepts for dataspaces range from database management systems to cross-company platforms for data and applications. In this short paper, we present the “Solid Data Space” (SDS), a concept for dataspaces that build on top of the (Semantic) Web and Social Linked Data (Solid). Existing Web technologies and Linked Data principles form the foundation for open, decentralized networks for sovereign data exchange between citizens, organizations and companies. Domain-specific dataspace implementations can extend the agreements for communication and collaboration to enable specific functionality. We compare the SDS with principles and components of the emerging International Data Spaces to identify similarities and point out technological differences.
Katti: An Extensive and Scalable Tool for Website Analyses

Research on web security and privacy frequently relies on tools that analyze a set of websites. One major obstacle to the judicious analysis is the employment of a rock-solid and feature-rich web crawler. For example, the automated analysis of ad-malware campaigns on websites requests crawling a vast set of domains on multiple real web browsers, while simultaneously mitigating bot detections and applying user interactions on websites. Further, the ability to attach various threat analysis frameworks lacks current tooling efforts in web crawling and analyses.
Multi-Agent Reinforcement Learning with Shared Policy for Cloud Quota Management Problem

Quota is often used in resource allocation and management scenarios to prevent abuse of resource and increase the efficiency of resource utilization. Quota management is usually fulfilled with a set of rules maintained by the system administrator. However, maintaining these rules usually needs deep domain knowledge. Moreover, arbitrary rules usually cannot guarantee both high resource utilization and fairness at the same time. In this paper, we propose a reinforcement learning framework to automatically respond to quota requests in cloud computing platforms with distinctive usage characteristics for users. Extensive experimental results have demonstrated the superior performance of our framework on achieving a great trade-off between efficiency and fairness.
Social Re-Identification Assisted RTO Detection for E-Commerce

E-commerce features like easy cancellations, returns, and refunds can be exploited by bad actors or uninformed customers, leading to revenue loss for organization. One such problem faced by e-commerce platforms is Return To Origin (RTO), where the user cancels an order while it is in transit for delivery. In such a scenario platform faces logistics and opportunity costs. Traditionally, models trained on historical trends are used to predict the propensity of an order becoming RTO. Sociology literature has highlighted clear correlations between socio-economic indicators and users’ tendency to exploit systems to gain financial advantage. Social media profiles have information about location, education, and profession which have been shown to be an estimator of socio-economic condition. We believe combining social media data with e-commerce information can lead to improvements in a variety of tasks like RTO, recommendation, fraud detection, and credit modeling. In our proposed system, we find the public social profile of an e-commerce user and extract socio-economic features. Internal data fused with extracted social features are used to train a RTO order detection model. Our system demonstrates a performance improvement in RTO detection of 3.1% and 19.9% on precision and recall, respectively. Our system directly impacts the bottom line revenue and shows the applicability of social re-identification in e-commerce.
Life cycle ontology of software engineering

The article highlights the problem of presenting knowledge on the models of software life cycle, the importance of which can be explained by the rapid progress of software engineering methods, by the absence of a formally easily extensible knowledge model in this subject area, and by the fact that cycle time selection models and the proposed development methodology have a significant impact on the success of software projects. System analysis &#x0D; of the main types of software development methodologies, life cycle models and their phases has been carried out. The results of studying the representation of software life cycle models in the form of ontologies are presented. The ontology “Software development life cycle” (SDLC) has been developed. It is designed to represent knowledge about various models of the software life cycle, phases (stages) of the life cycle inherent in different models, and the possibility of describing the recurrence of phases. The ontology allows describing models both within predictive development methodologies (waterfall, incremental) and within agile development methodologies (Scrum, Kanban). Classes, properties and axioms of the ontology are described, on the basis of which it is possible to produce a formal logical inference. The SDLC ontology is developed on top of the Semantic Web formats (in OWL language), published in the public domain and presents a developing, easily extensible project. This can probably be used in the field of software development for practical or research purposes. There is also introduced the idea of a software shell that uses the presented ontology, which will allow, according to the given parameters, to choose the most appropriate methodology for the project, which will simplify the development process, avoid errors and reduce development time.
Entity and Event Topic Extraction from Podcast Episode Title and Description Using Entity Linking

To improve Amazon Music podcast services and customer engagements, we introduce Entity-Linked Topic Extraction (ELTE) to identify well-known entity and event topics from podcast episodes. An entity can be a person, organization, work-of-art, etc., while an event, such as the Opioid epidemic, occurs at specific point(s) in time. ELTE first extracts key-phrases from episode title and description metadata. It then uses entity linking to canonicalize them against Wikipedia knowledge base (KB), ensuring that the topics exist in the real world. ELTE also models NIL-predictions for entity or event topics that are not in the KB, as well as topics that are not of entity or event type. To test the model, we construct a podcast topic database of 1166 episodes from various categories. Each episode comes with a Wiki-link annotated main topic or NIL-prediction. ELTE produces the best overall Exact Match EM score of .84, with by-far the best EM of .89 among the entity or event type episodes, as well as NIL-predictions for episodes without entity or event main topic (EM score of .86).
Why are Hyperlinks Blue?

The internet has ingrained itself into every aspect of our lives, but there's one aspect of the digital world that some take for granted. Did you ever notice that many links, specifically hyperlinks, are blue? When a coworker casually asked me why links are blue, I was stumped. As a user experience designer who has created websites since 2001, I've always made my links blue. I have advocated for the specific shade of blue, and for the consistent application of blue, yes, but I've never stopped and wondered, why are links blue? It was just a fact of life. Grass is green and hyperlinks are blue. Culturally, we associate links with the color blue so much that in 2016, when Google changed its links to black, it created quite a disruption [1].
Privacy-Preserving Online Content Moderation with Federated Learning

Users are exposed to a large volume of harmful content that appears daily on various social network platforms. One solution to users’ protection is developing online moderation tools using Machine Learning (ML) techniques for automatic detection or content filtering. On the other hand, the processing of user data requires compliance with privacy policies. This paper proposes a privacy–preserving Federated Learning (FL) framework for online content moderation that incorporates Central Differential Privacy (CDP). We simulate the FL training of a classifier for detecting tweets with harmful content, and we show that the performance of the FL framework can be close to the centralized approach. Moreover, it has a high performance even if a small number of clients (each with a small number of tweets) are available for the FL training. When reducing the number of clients (from fifty to ten) or the tweets per client (from 1K to 100), the classifier can still achieve AUC. Furthermore, we extend the evaluation to four other Twitter datasets that capture different types of user misbehavior and still obtain a promising performance (61% – 80% AUC).
DSNet: Efficient Lightweight Model for Video Salient Object Detection for IoT and WoT Applications

The most challenging aspects of deploying deep models in IoT and embedded systems are extensive computational complexity and large training and inference time. Although various lightweight versions of state-of-the-art models are also being designed, maintaining the performance of such models is difficult. To overcome these problems, an efficient, lightweight, Deformable Separable Network (DSNet) is proposed for video salient object detection tasks, mainly for mobile and embedded vision applications. DSNet is equipped with a Deformable Convolution Network (DeCNet), Separable Convolution Network (SCNet), and Depth-wise Attention Response Propagation (DARP) module, which makes it maintain the trade-off between accuracy and latency. The proposed model generates saliency maps considering both the background and foreground simultaneously, making it perform better in unconstrained scenarios (such as partial occlusion, deformable background/objects, and illumination effect). The extensive experiments conducted on six benchmark datasets demonstrate that the proposed model outperforms state-of-art approaches in terms of computational complexity, number of parameters, and latency measures.
A Comparative Study of Secure Outsourced Matrix Multiplication Based on Homomorphic Encryption

Homomorphic encryption (HE) is a promising solution for handling sensitive data in semi-trusted third-party computing environments, as it enables processing of encrypted data. However, applying sophisticated techniques such as machine learning, statistics, and image processing to encrypted data remains a challenge. The computational complexity of some encrypted operations can significantly increase processing time. In this paper, we focus on the analysis of two state-of-the-art HE matrix multiplication algorithms with the best time and space complexities. We show how their performance depends on the libraries and the execution context, considering the standard Cheon–Kim–Kim–Song (CKKS) HE scheme with fixed-point numbers based on the Microsoft SEAL and PALISADE libraries. We show that Windows OS for the SEAL library and Linux OS for the PALISADE library are the best options. In general, PALISADE-Linux outperforms PALISADE-Windows, SEAL-Linux, and SEAL-Windows by 1.28, 1.59, and 1.67 times on average for different matrix sizes, respectively. We derive high-precision extrapolation formulas to estimate the processing time of HE multiplication of larger matrices.
Contrastive Fine-tuning on Few Shot Intent Detection with Topological Intent Tree

We present a few-shot intent detection model for an enterprise’s conversational dialogue system. The model uses an intent topological tree to guide the search for the user intent using large language models (LLMs). The intents are resolved based on semantic similarities between user utterances and the text descriptions of the internal nodes of the intent tree or the intent examples in the leaf nodes of the tree. Our results show that an off-the-shelf language model can work reasonably well in a large enterprise deployment without fine-tuning, and its performance can be further improved with fine-tuning as more domain-specific data becomes available. We also show that the fine-tuned language model meets and outperforms the state-of-the-art (SOTA) results in resolving conversation intents without training classifiers. With the use of a topological intent tree, our model provides more interpretability to cultivate people’s trust in their decisions.
EnhancE: Enhanced Entity and Relation Embedding for Knowledge Hypergraph Link Prediction

Knowledge Hypergraphs, as the generalization of knowledge graphs, have attracted increasingly widespread attention due to their friendly compatibility with real-world facts. However, link prediction in knowledge hypergraph is still an underexplored field despite the ubiquity of n-ary facts in the real world. Several recent representative embedding-based knowledge hypergraph link prediction methods have proven to be effective in a series of benchmarks, however, they only consider the position (or role) information, ignoring the neighborhood structure among entities and rich semantic information within each fact. To this end, we propose a model named EnhancE for effective link prediction in knowledge hypergraphs. On the one hand, a more expressive entity representation is obtained with both position and neighborhood information added to the initial embedding. On the other hand, rich semantic information of the involved entities within each tuple is incorporated into relation embedding for enhanced representation. Extensive experimental results over real datasets of both knowledge hypergraph and knowledge graph demonstrate the excellent performance of EnhancE compared with a variety of state-of-the-art baselines.
Model and algorithm for supporting decision on selection of products for recommendation to user based on analysis of statistical implication

The article considers the issues of analyzing data that the consumer encounters when choosing products and services. The problem is extracting useful information that allows offering the user new products and services depending on his preferences. This problem is localized by recommender systems focused on using the data mining methods, such as classification, clustering, analysis of association rules - a machine learning method that detects relationships between variables in databases. Compared to other methods, the advantage of the association rule-based recommender method is its transparency: the method can show the user the inference mechanism used to make decisions. Association rule-based recommender systems use two measures that are widely popular and evaluate sets of elements and create sets of association rules, the support measure and the confidence measure. However, to get better recommendations, the quality of association rules and the way sentence ranking should be measured by some objective measure. There has been developed a model and a decision support algorithm for the choice of products for recommendations to the user based on the statistical implication analysis method. In the proposed solutions, support and credibility measures are used to create association rules; a measure of the intensity of the statistical implication is used to filter the set of rules and to rank the recommendations.
Influence of information presentation form on online learning based on dynamics theory of visual perception form

With the development of information technology, online learning has become an important way of teaching in colleges and universities. The importance of online learning is particularly prominent, especially during the COVID-19 pandemic. How to improve online learning quality is a common problem faced by educators. Online learning quality is closely related to information presentation form, so it is necessary to study the influence of information presentation form on online learning. Based on the dynamics theory of visual perception form and its operating principle, this study compares the differences in post-test scores, cognitive load and satisfaction between the information dynamics presentation form and the traditional information presentation form through a two-factor random experiment. The data analysis shows that information presentation form plays a significant role in improving students’ academic performance and reducing cognitive load. To a certain extent, there search proves the effectiveness of the information presentation form based on dynamics theory of visual perception form in promoting online learning. Relevant improvement suggestions are proposed to provide a reference and basis for the in-depth development of online learning and the improvement of online learning quality.
Theory and computation of Markovian quantum antenna systems

We describe a general framework for the modeling and analysis of Markovian quantum antenna systems viewed as a special problem in quantum stochastic dynamics. The quantum radiator, which can be used to spatially direct radiated quantum states in future quantum communication systems, is modeled as an open quantum system capable of controlling the composition of its radiation modes through external source manipulations while in continuous interaction with the surrounding thermal and random environments. Our analysis and computational methods are based on deploying the Gorini–Kossakowski–Sudarshan–Lindblad (GKSL) master equation to account for environment-induced jump processes such as quantum dissipation and decoherence. We construct a definition of the quantum antenna directivity inspired by the corresponding formulas in classical antenna theory and present several versions of the quantum directivity formula. As a computational example, we study the flow of the density operator of a coupled two-level quantum dot (qubit) array, excited by classical external signals with variable amplitude and phase, which is evolved in time using the quantum master equation. It is shown that by manipulating the amplitude and phase excitations of individual quantum dots, one may significantly enhance the directive radiation properties of the Markovian quantum antenna system.
Weedle: Composable Dashboard for Data-Centric NLP in Computational Notebooks

Data-centric NLP is a highly iterative process requiring careful exploration of text data throughout entire model development lifecycle. Unfortunately, existing data exploration tools are not suitable to support data-centric NLP because of workflow discontinuity and lack of support for unstructured text. In response, we propose Weedle, a seamless and customizable exploratory text analysis system for data-centric NLP. Weedle is equipped with built-in text transformation operations and a suite of visual analysis features. With its widget, users can compose customizable dashboards interactively and programmatically in computational notebooks.
Semantics in Dataspaces: Origin and Future Directions

The term dataspace was coined two decades ago [12] and has evolved since then. Definitions range from (i) an abstraction for data management in an identifiable scope [15] over (iii) a multi-sided data platform connecting participants in an ecosystem [21] to (iii) interlinking data towards loosely connected (global) information [17]. Many implementations and scientific notions follow different interpretations of the term dataspace, but agree on some use of semantic technologies. For example, dataspaces such as the European Open Science Cloud and the German National Research Data Infrastructure are committed to applying the FAIR principles [11, 16]. Dataspaces built on top of Gaia-X are using semantic methods for service Self-Descriptions [13]. This paper investigates ongoing dataspace efforts and aims to provide insights on the definition of the term dataspace, the usage of semantics and FAIR principles, and future directions for the role of semantics in dataspaces.