Miracle-dz/UniFAR-FTUs huggingface dataset:
Query Obfuscation by Semantic Decomposition

We propose a method to protect the privacy of search engine users by decomposing the queries using semantically related and unrelated distractor terms. Instead of a single query, the search engine receives multiple decomposed query terms. Next, we reconstruct the search results relevant to the original query term by aggregating the search results retrieved for the decomposed query terms. We show that the word embeddings learnt using a distributed representation learning method can be used to find semantically related and distractor query terms. We derive the relationship between the obfuscity achieved through the proposed query anonymisation method and the reconstructability of the original search results using the decomposed queries. We analytically study the risk of discovering the search engine users’ information intents under the proposed query obfuscation method, and empirically evaluate its robustness against clustering-based attacks. Our experimental results show that the proposed method can accurately reconstruct the search results for user queries, without compromising the privacy of the search engine users.
BenchIE: A Framework for Multi-Faceted Fact-Based Open Information Extraction Evaluation

Intrinsic evaluations of OIE systems are carried out either manually—with human evaluators judging the correctness of extractions—or automatically, on standardized benchmarks. The latter, while much more cost-effective, is less reliable, primarily because of the incompleteness of the existing OIE benchmarks: the ground truth extractions do not include all acceptable variants of the same fact, leading to unreliable assessment of the models’ performance. Moreover, the existing OIE benchmarks are available for English only. In this work, we introduce BenchIE: a benchmark and evaluation framework for comprehensive evaluation of OIE systems for English, Chinese, and German. In contrast to existing OIE benchmarks, BenchIE is fact-based, i.e., it takes into account informational equivalence of extractions: our gold standard consists of fact synsets, clusters in which we exhaustively list all acceptable surface forms of the same fact. Moreover, having in mind common downstream applications for OIE, we make BenchIE multi-faceted; i.e., we create benchmark variants that focus on different facets of OIE evaluation, e.g., compactness or minimality of extractions. We benchmark several state-of-the-art OIE systems using BenchIE and demonstrate that these systems are significantly less effective than indicated by existing OIE benchmarks. We make BenchIE (data and evaluation code) publicly available.
Multilingual Event Linking to Wikidata

We present a task of multilingual linking of events to a knowledge base. We automatically compile a large-scale dataset for this task, comprising of 1.8M mentions across 44 languages referring to over 10.9K events from Wikidata. We propose two variants of the event linking task: 1) multilingual, where event descriptions are from the same language as the mention, and 2) crosslingual, where all event descriptions are in English. On the two proposed tasks, we compare multiple event linking systems including BM25+ (Lv and Zhai, 2011) and multilingual adaptations of the biencoder and crossencoder architectures from BLINK (Wu et al., 2020). In our experiments on the two task variants, we find both biencoder and crossencoder models significantly outperform the BM25+ baseline. Our results also indicate that the crosslingual task is in general more challenging than the multilingual task. To test the out-of-domain generalization of the proposed linking systems, we additionally create a Wikinews-based evaluation set. We present qualitative analysis highlighting various aspects captured by the proposed dataset, including the need for temporal reasoning over context and tackling diverse event descriptions across languages.
Electric Motor Design Optimization: A Convex Surrogate Modeling Approach

This paper instantiates a convex electric powertrain design optimization framework, bridging the gap between high-level powertrain sizing and low-level components design. We focus on the electric motor and transmission of electric vehicles, using a scalable convex motor model based on surrogate modeling techniques. Specifically, we first select relevant motor design variables and evaluate high-fidelity samples according to a predefined sampling plan. Second, using the sample data, we identify a convex model of the motor, which predicts its losses as a function of the operating point and the design parameters. We also identify models of the remaining components of the powertrain, namely a battery and a fixed-gear transmission. Third, we frame the minimum-energy consumption design problem over a drive cycle as a second-order conic program that can be efficiently solved with optimality guarantees. Finally, we showcase our framework in a case study for a compact family car and compute the optimal motor design and transmission ratio. We validate the accuracy of our models with a high-fidelity simulation tool and calculate the drift in battery energy consumption. We show that our model can capture the optimal operating line and the error in battery energy consumption is low. Overall, our framework can provide electric motor design experts with useful starting points for further design optimization.
Grand Canonical Ensembles of Sparse Networks and Bayesian Inference

Maximum entropy network ensembles have been very successful in modelling sparse network topologies and in solving challenging inference problems. However the sparse maximum entropy network models proposed so far have fixed number of nodes and are typically not exchangeable. Here we consider hierarchical models for exchangeable networks in the sparse limit, i.e., with the total number of links scaling linearly with the total number of nodes. The approach is grand canonical, i.e., the number of nodes of the network is not fixed a priori: it is finite but can be arbitrarily large. In this way the grand canonical network ensembles circumvent the difficulties in treating infinite sparse exchangeable networks which according to the Aldous-Hoover theorem must vanish. The approach can treat networks with given degree distribution or networks with given distribution of latent variables. When only a subgraph induced by a subset of nodes is known, this model allows a Bayesian estimation of the network size and the degree sequence (or the sequence of latent variables) of the entire network which can be used for network reconstruction.
Understanding the compromise between skyline and ranking queries

Skyline and Ranking queries have gained great popularity in the recent years. These two techniques are crucial for multi-criteria decision support applications, which are now more popular than ever before. Skyline and Ranking queries are, however, affected by well-known limitations. In the past recent years, the database community provided numerous studies in this field with the aim to overcome the weaknesses of these two approaches. This survey introduces the reader to Skyline and Ranking queries, explaining the concepts on which they are based, with the intent to present the compromise between the two techniques: flexible skylines.
Secure Formation Control via Edge Computing Enabled by Fully Homomorphic Encryption and Mixed Uniform-Logarithmic Quantization

Recent developments in communication technologies, such as 5G, together with innovative computing paradigms, such as edge computing, provide further possibilities for the implementation of real-time networked control systems. However, privacy and cyber-security concerns arise when sharing private data between sensors, agents and a third-party computing facility. In this letter, a secure version of the distributed formation control is presented, analyzed and simulated, where gradient-based formation control law is implemented in the edge, with sensor and actuator information being secured by fully homomorphic encryption method based on learning with error (FHE-LWE) combined with a proposed mixed uniform-logarithmic quantizer (MULQ). The novel quantizer is shown to be suitable for realizing secure control systems with FHE-LWE where the critical real-time information can be quantized into a prescribed bounded space of plaintext while satisfying a sector bound condition whose lower and upper-bound can be made sufficiently close to an identity. An absolute stability analysis is presented, that shows the asymptotic stability of the closed-loop secure control system.
Topological Multipartite Entanglement in a Fermi Liquid

We show that the topology of the Fermi sea of a $D$-dimensional Fermi gas is reflected in the multipartite entanglement characterizing $D+1$ regions that meet at a point. For odd $D$ we introduce the multipartite mutual information, and show that it exhibits a $\log^D L$ divergence as a function of system size $L$ with a universal coefficient that is proportional to the Euler characteristic $\chi_F$ of the Fermi sea. This provides a generalization, for a Fermi gas, of the well-known result for $D=1$ that expresses the $\log L$ divergence of the bipartite entanglement entropy in terms of the central charge $c$ characterizing a conformal field theory. For even $D$ we introduce a charge-weighted entanglement entropy that is manifestly odd under a particle-hole transformation. We show that the corresponding charge-weighted mutual information exhibits a similar $\log^D L$ divergence proportional to $\chi_F$. Our analysis relates the universal behavior of the multipartite mutual information in the absence of interactions to the $D+1$'th order equal-time density correlation function, which we show exhibits a universal behavior in the long wavelength limit proportional to $\chi_F$. Our analytic results are based on the replica method. In addition we perform a numerical study of the charge-weighted mutual information for $D=2$ that confirms several aspects of the analytic theory. Finally, we consider the effect of interactions perturbatively within the replica theory. We show that for $D=3$ the $\log^3 L$ divergence of the topological mutual information is not perturbed by weak short-ranged interactions, though for $D=2$ the charge-weighted mutual information is perturbed. Thus, for $D=3$ the multipartite mutual information provides a robust classification that distinguishes distinct topological Fermi liquid phases.
The Ceresa class and tropical curves of hyperelliptic type

A BSTRACT . We deﬁne a new algebraic invariant of a graph G called the Ceresa-Zharkov class and show that it is trivial if and only if G is of hyperelliptic type, equivalently, G does not have as a minor the complete graph on 4 vertices or the loop of 3 loops. After choosing edge-lengths, this class specializes to an algebraic invariant of a tropical curve with underlying graph G that is closely related to the Ceresa cycle for an algebraic curve deﬁned over C (( t )) .
Stealing Malware Classifiers and AVs at Low False Positive Conditions

Model stealing attacks have been successfully used in many machine learning domains, but there is little understanding of how these attacks work in the malware detection domain. Malware detection and, in general, security domains have very strong requirements of low false positive rates (FPR). However, these requirements are not the primary focus of the existing model stealing literature. Stealing attacks create surrogate models that perform similarly to a target model using a limited amount of queries to the target. The first stage of this study is the evaluation of active learning model stealing attacks against publicly available stand-alone machine learning malware classifiers and antivirus products (AVs). We propose a new neural network architecture for surrogate models that outperforms the existing state of the art on low FPR conditions. The surrogates were evaluated on their agreement with the targeted models. Good surrogates of the stand-alone classifiers were created with up to 99% agreement with the target models, using less than 4% of the original training dataset size. Good AV surrogates were also possible to train, but with a lower agreement. The second stage used the best surrogates as well as the target models to generate adversarial malware using the MAB framework [29] to test stand-alone models and AVs (offline and online). Results showed that surrogate models could generate adversarial samples that evade the targets but are less successful than the targets themselves. Using surrogates, however, is a necessity for attackers, given that attacks against AVs are extremely time-consuming and easily detected when the AVs are connected to the internet.
Sentiment Analysis of Political Tweets for Israel using Machine Learning

Sentiment Analysis is a vital research topic in the field of Computer Science. With the accelerated development of Information Technology and social networks, a massive amount of data related to comment texts has been generated on web applications or social media platforms like Twitter. Due to this, people have actively started proliferating general information and the information related to political opinions, which becomes an important reason for analyzing public reactions. Most researchers have used social media specifics or contents to analyze and predict public opinion concerning political events. This research proposes an analytical study using Israeli political Twitter data to interpret public opinion towards the Palestinian-Israeli conflict. The attitudes of ethnic groups and opinion leaders in the form of tweets are analyzed using Machine Learning algorithms like Support Vector Classifier (SVC), Decision Tree (DT), and Naive Bayes (NB). Finally, a comparative analysis is done based on experimental results from different models.
Mitigating Bias in Facial Analysis Systems by Incorporating Label Diversity

Facial analysis models are increasingly applied in real-world applications that have significant impact on peoples' lives. However, as literature has shown, models that automatically classify facial attributes might exhibit algorithmic discrimination behavior with respect to protected groups, potentially posing negative impacts on individuals and society. It is therefore critical to develop techniques that can mitigate unintended biases in facial classifiers. Hence, in this work, we introduce a novel learning method that combines both subjective human-based labels and objective annotations based on mathematical definitions of facial traits. Specifically, we generate new objective annotations from two large-scale human-annotated dataset, each capturing a different perspective of the analyzed facial trait. We then propose an ensemble learning method, which combines individual models trained on different types of annotations. We provide an in-depth analysis of the annotation procedure as well as the datasets distribution. Moreover, we empirically demonstrate that, by incorporating label diversity, our method successfully mitigates unintended biases, while maintaining significant accuracy on the downstream tasks.
What Formulation Should One Choose for Modeling a 3-D HTS Motor Pole With Ferromagnetic Materials?

We discuss the relevance of several finite-element formulations for nonlinear systems containing high-temperature superconductors (HTS) and ferromagnetic materials (FM) in the context of a 3-D motor pole model. The formulations are evaluated in terms of their numerical robustness and efficiency. We propose a coupled <inline-formula> <tex-math notation="LaTeX">$h$ </tex-math></inline-formula>-<inline-formula> <tex-math notation="LaTeX">$\phi $ </tex-math></inline-formula>-<inline-formula> <tex-math notation="LaTeX">$a$ </tex-math></inline-formula>-formulation as an optimal choice, modeling the problem with an <inline-formula> <tex-math notation="LaTeX">$a$ </tex-math></inline-formula>-formulation in the FM and an <inline-formula> <tex-math notation="LaTeX">$h$ </tex-math></inline-formula>-<inline-formula> <tex-math notation="LaTeX">$\phi $ </tex-math></inline-formula>-formulation in the remaining domains. While maintaining a low number of degrees of freedom (DOFs), the <inline-formula> <tex-math notation="LaTeX">$h$ </tex-math></inline-formula>-<inline-formula> <tex-math notation="LaTeX">$\phi $ </tex-math></inline-formula>-<inline-formula> <tex-math notation="LaTeX">$a$ </tex-math></inline-formula>-formulation guarantees a robust resolution and strongly reduces the number of iterations required for handling the nonlinearities of HTS and FM compared to standard formulations.
On Signs of Fourier Coefficients of Hecke-Maass Cusp Forms on $\mathrm{GL}_3$

We consider sign changes of Fourier coeﬃcients of Hecke-Maass cusp forms for the group SL 3 ( Z ). When the underlying form is self-dual, we show that there are ≫ ε X 5 / 6 − ε sign changes among the coeﬃcients { A ( m, 1) } m ≤ X and that there is a positive proportion of sign changes for many self-dual forms. Similar result concerning the positive proportion of sign changes also hold for the real-valued coeﬃcients A ( m, m ) for generic GL 3 cusp forms, a result which is based on a new eﬀective Sato-Tate type theorem for a family of GL 3 cusp forms we establish. In addition, non-vanishing of the Fourier coeﬃcients is studied under the Ramanujan-Petersson conjecture.
Text and Team: What Article Metadata Characteristics Drive Citations in Software Engineering?

Context: Citations are a key measure of scientific performance in most fields, including software engineering. However, there is limited research that studies which characteristics of articles’ metadata (title, abstract, keywords, and author list) are driving citations in this field. Objective: In this study, we propose a simple theoretical model for how citations come to be with respect to article metadata, we hypothesize theoretical linkages between metadata characteristics and citations of articles, and we empirically test these hypotheses. Method: We use multiple regression analyses to examine a data set comprising the titles, abstracts, keywords, and authors of 16,131 software engineering articles published between 1990 and 2020 in 20 highly influential software engineering venues. Results: We find that number of authors, number of keywords, number of question marks and dividers in the title, number of acronyms, abstract length, abstract propositional idea density, and corresponding authors in the core Anglosphere are significantly related to citations. Conclusion: Various characteristics of articles’ metadata are linked to the frequency with which the corresponding articles are cited. These results partially confirm and partially go counter to prior findings in software engineering and other disciplines.
Solar Flares and Magnetic Helicity

Solar ﬂares and coronal mass ejections are the largest energy release phenomena in the current solar system. They cause drastic enhancements of electromagnetic waves of various wavelengths and sometimes eject coronal material into the interplanetary space, disturbing the magnetic surroundings of orbiting planets including the Earth. It is generally accepted that solar ﬂares are a phenomenon in which magnetic energy stored in the solar atmosphere above an active region is suddenly released through magnetic reconnection. Therefore, to elucidate the nature of solar ﬂares, it is critical to estimate the complexity of the magnetic ﬁeld and track its evolution. Magnetic helicity, a measure of the twist of coronal magnetic structures, is thus used to quantify and characterize the complexity of ﬂare-productive active regions. This chapter provides an overview of solar ﬂares and discusses how the diﬀerent concepts of magnetic helicity are used to understand and predict solar ﬂares.
Exploiting the Right: Inferring Ideological Alignment in Online Influence Campaigns Using Shared Images

This work advances investigations into the visual media shared by agents in disinformation campaigns by characterizing the images shared by accounts identified by Twitter as being part of such campaigns. Using images shared by US politicians' Twitter accounts as a baseline and training set, we build models for inferring the ideological presentation of accounts using the images they share. Results show that, while our models recover the expected bimodal ideological distribution of US politicians, we find that, on average, four separate influence campaigns -- attributed to Iran, Russia, China, and Venezuela -- all present conservative ideological presentations in the images they share. Given that prior work has shown Twitter accounts used by Russian disinformation agents are ideologically diverse in the text and news they share, these image-oriented findings provide new insights into potential axes of coordination and suggest these accounts may not present consistent ideological positions across modalities.
On the Parallel Reconstruction from Pooled Data

In the pooled data problem the goal is to efficiently reconstruct a binary signal from additive measurements. Given a signal $\sigma\in \{0, 1\}^{n}$, we can query multiple entries at once and get the total number of non-zero entries in the query as a result. We assume that queries are time-consuming and therefore focus on the setting where all queries are executed in parallel. For the regime where the signal is sparse such that $\Vert\sigma\Vert_{1}= o(n)$ our results are twofold: First, we propose and analyze a simple and efficient greedy reconstruction algorithm. Secondly, we derive a sharp information-theoretic threshold for the minimum number of queries required to reconstruct σ with high probability. Our first result matches the performance guarantees of much more involved constructions (Karimi et al. 2019). Our second result extends a result of Alaoui et al. (2014) and Scarlett & Cevher (2017) who studied the pooled data problem for dense signals. Finally, our theoretical findings are complemented with empirical simulations. Our data not only confirm the information-theoretic thresholds but also hint at the practical applicability of our pooling scheme and the simple greedy reconstruction algorithm.
Collaborative governance in Social Impact Bonds: aligning interests within divergent accountabilities?

ABSTRACT Social Impact Bonds (SIBs) are a novel form of cross-sector collaborations and an alternative way of financing innovative social services. Based on an in-depth longitudinal case analysis and drawing from agency theory as well as resource dependence theory, we examine the process of interest alignment among involved partners over the lifespan of a SIB pilot project in Austria. Our study shows the multitude of problems and costs that such a challenging arrangement of multiple stakeholders brings along, thus dampening overoptimistic expectations in SIBs. We also suggest an analytical framework that improves the assessment of SIBs’ probability of success.