Miracle-dz/UniFAR-FTUs huggingface dataset:
Rethinking Offensive Text Detection as a Multi-Hop Reasoning Problem

We introduce the task of implicit offensive text detection in dialogues, where a statement may have either an offensive or non-offensive interpretation, depending on the listener and context. We argue that reasoning is crucial for understanding this broader class of offensive utterances, and release SLIGHT, a dataset to support research on this task. Experiments using the data show that state-of-the-art methods of offense detection perform poorly when asked to detect implicitly offensive statements, achieving only {\sim} 11\% accuracy. In contrast to existing offensive text detection datasets, SLIGHT features human-annotated chains of reasoning which describe the mental process by which an offensive interpretation can be reached from each ambiguous statement. We explore the potential for a multi-hop reasoning approach by utilizing existing entailment models to score the probability of these chains, and show that even naive reasoning models can yield improved performance in most situations. Analysis of the chains provides insight into the human interpretation process and emphasizes the importance of incorporating additional commonsense knowledge.
Optimal Scaling for the Proximal Langevin Algorithm in High Dimensions

: The Metropolis-adjusted Langevin (MALA) algorithm is a sampling algorithm that incor- porates the gradient of the logarithm of the target density in its proposal distribution. In an earlier joint work [PST12], the author had extended the seminal work of [RR98] and showed that in stationarity, MALA applied to an N − dimensional approximation of the target will take O ( N 13 ) steps to explore its target measure. It was also shown in [RR98, PST12] that, as a consequence of the diﬀusion limit, the MALA algorithm is optimized at an average acceptance probability of 0 . 574. In [Per16], Pereyra intro- duced the proximal MALA algorithm where the gradient of the log target density is replaced by the proximal function (mainly aimed at implementing MALA non-diﬀerentiable target densities). In this paper, we show that for a wide class of twice diﬀerentiable target densities, the proximal MALA enjoys the same optimal scaling as that of MALA in high dimensions and also has an average optimal accep- tance probability of 0 . 574. The results of this paper thus give the following practically useful guideline: for smooth target densities where it is expensive to compute the gradient while implementing MALA, users may replace the gradient with the corresponding proximal function (that can be often computed relatively cheaply via convex optimization) without losing any eﬃciency. We show this for two class of examples. First, for the product of Gaussians, we identify the optimal scale for proximal MALA and show that it is identical to MALA; our calculations further suggest that the same result should hold for a wide class of twice diﬀerentiable, log-concave product measures. Next, following the exact framework used in [PST12], we deﬁne a version of the proximal MALA algorithm in a Hilbert space. We show that for a certain class of twice diﬀerentiable, inﬁnite dimensional non-product measures commonly used in applications, the proximal MALA applied to an N − dimensional approximation of the target also will take O ( N 13 ) steps to explore the invariant measure, with an optimal acceptance probability of 0 . 574. This conﬁrms some of the empirical observations made in [Per16].
Shopping Behavioral Intentions Contributed by Store Layout and Perceived Crowding: An Exploratory Study Using Computer Walk–Through Simulation

The purpose of this study is to understand the role of store layout in predicting shopping behaviors by influencing the perception of crowding. With today's growing popularity of online shopping, brick–and–mortar stores are faced with increased challenges to create positive shopping experiences and attract more customers to the store. The customer's shopping experience often starts with the environment's physical appearance and navigation. Crowding is one of the known factors contributing to how stores look and feel. Using 3D simulation technology capable of offering a realistic virtual experience, we tested the hypothesis—that the negative impact of crowding could be effectively controlled through physical space configuration—by exploring the role of store layout and human density on perceived crowding using a 2 × 2 between–subjects design with 60 college students. Two distinct store layouts and their effects on perceived crowding and approach intentions were investigated. In addition, we also considered the role of individual differences in crowding perception. The findings demonstrate the critical role of store layout in controlling retail crowding and shopping motivations, potentially contributing to store success. We found increased shopping intentions in the store environment with a linear and symmetrical configuration than with a curvilinear and asymmetrical one. Such tendency was found to be significantly greater for male customers. A significant mediating role for perceived crowding on the relationship between the effects of store layout on shopping intentions was also found. This confirms the critical effect of store layout on perceived crowding that indirectly, yet significantly, influences customers’ shopping behavioral intentions. No prior work in retail literature has examined retail crowding on the basis of an empirical manipulation of the environment. By using virtual walk–through simulation, this study joins a limited body of knowledge pertaining to the store environment and aims to expand our understanding of the distinct effects of retail environments on consumers’ experience and behavior.
A note on graph drawings with star-shaped boundaries in the plane

In this note, we propose a straightforward method to produce an straight-line embedding of a planar graph where one face of a graph is fixed in the plane as a star-shaped polygon. It is based on minimizing discrete Dirichlet energies, following the idea of Tutte's embedding theorem. We will call it a geodesic triangulation of the star-shaped polygon. Moreover, we study the homotopy property of spaces of all straight-line embeddings. We give a simple argument to show that this space is contractible if the boundary is a non-convex quadrilateral. We conjecture that the same statement holds for general star-shaped polygons.
Genus 2 Goeritz Equivalence in $S^3$

The Goeritz group of a genus $g$ Heegaard splitting of a 3-manifold is the group of isotopy classes of orientation-preserving automorphisms of the manifold that preserve the Heegaard splitting. In the context of the standard genus 2 Heegaard splitting of $S^3$, we introduce the concept of Goeritz equivalence of curves, present two algebraic obstructions to Goeritz equivalence of simple closed curves that are straightforward to compute, and provide families of examples demonstrating how these obstructions may be used.
E2E Segmenter: Joint Segmenting and Decoding for Long-Form ASR

Improving the performance of end-to-end ASR models on long utterances ranging from minutes to hours in length is an ongoing challenge in speech recognition. A common solution is to segment the audio in advance using a separate voice activity detector (VAD) that decides segment boundary locations based purely on acoustic speech/non-speech information. VAD segmenters, however, may be sub-optimal for real-world speech where, e.g., a complete sentence that should be taken as a whole may contain hesitations in the middle ("set an alarm for. 5 o'clock"). We propose to replace the VAD with an end-to-end ASR model capable of predicting segment boundaries in a streaming fashion, allowing the segmentation decision to be conditioned not only on better acoustic features but also on semantic features from the decoded text with negligible extra computation. In experiments on real world long-form audio (YouTube) with lengths of up to 30 minutes, we demonstrate 8.5% relative WER improvement and 250 ms reduction in median end-of-segment latency compared to the VAD segmenter baseline on a state-of-the-art Conformer RNN-T model.
Steering law considering biased loads for control moment gyroscopes

A new steering law of a pyramid configuration of four control moment gyroscopes (CMGs) is proposed to suppress radical motion of gimbals, reduce the gimbal angular displacement and level the biased ones of the four CMGs. Additionally analytical findings on the execution timing of the proposed method in relation to multiple attitude maneuvers are newly presented. Because major failure mode of the CMG is the defective lubrication of the spin bearings, resulting from being put on the excessive radial loads by radical motion of gimbals, the proposed method focuses the relation between the gimbal angular displacement of each CMG and the initial condition of gimbal angles. A suitable set of initial gimbal angles is selected using the defined evaluation function in an off-line preliminarily calculation. The evaluation function considers the manipulability of the upcoming maneuver related to the radical motion of gimbals and the biased gimbal angular displacements of four CMGs accumulated during the operational time. Simultaneously, the criteria for judgment and analytical findings on the execution timing of the proposed method in relation to multiple attitude maneuvers are newly presented, which consider the extra load by null motion and the accumulated gaps of gimbal angles between the initial condition and the terminal ones. The dynamics of a typical pyramid configuration of four single-gimbal CMGs was modeled and a numerical simulation in case the CMGs do not pass through the singularity was carried out. Numerical simulation confirmed that the proposed method not only keeps the manipulability greater but also levels the gimbal angular displacements of the CMGs without degrading attitude control.
On the Rotor Lifting Line Wake Model

This article comments on the wake model used in moderately-loaded rotor lifting line theory for the preliminary design of propellers and horizontal-axis turbines. Mathematical analysis of the classical wake model reveals an inconsistency between the induced velocities numerically computed by the model versus those theoretically predicted by the model. An improved wake model is presented, which better agrees with theory than previous models and thus improves the numerical consistency and robustness of rotor lifting line design algorithms. The present wake model analytically relates the pitch of the trailing vortices to the pitch of the total inflow computed at the lifting line control points. For conciseness, the article focuses on the propeller case, although both propeller and horizontal-axis turbine examples are presented.
DP-VAE: Human-Readable Text Anonymization for Online Reviews with Differentially Private Variational Autoencoders

While vast amounts of personal data are shared daily on public online platforms and used by companies and analysts to gain valuable insights, privacy concerns are also on the rise: Modern authorship attribution techniques have proven effective at identifying individuals from their data, such as their writing style or behavior of picking and judging movies. It is hence crucial to develop data sanitization methods that allow sharing of users’ data while protecting their privacy and preserving quality and content of the original data. In this paper, we tackle anonymization of textual data and propose an end-to-end differentially private variational autoencoder architecture. Unlike previous approaches that achieve differential privacy on a per-word level through individual perturbations, our solution works at an abstract level by perturbing the latent vectors that provide a global summary of the input texts. Decoding an obfuscated latent vector thus not only allows our model to produce coherent, high-quality output text that is human-readable, but also results in strong anonymization due to the diversity of the produced data. We evaluate our approach on IMDb movie and Yelp business reviews, confirming its anonymization capabilities and preservation of the semantics and utility of the original sentences.
Fograph: Enabling Real-Time Deep Graph Inference with Fog Computing

Graph Neural Networks (GNNs) have gained growing interest in miscellaneous applications owing to their outstanding ability in extracting latent representation on graph structures. To render GNN-based service for IoT-driven smart applications, the traditional model serving paradigm resorts to the cloud by fully uploading the geo-distributed input data to the remote datacenter. However, our empirical measurements reveal the significant communication overhead of such cloud-based serving and highlight the profound potential in applying the emerging fog computing. To maximize the architectural benefits brought by fog computing, in this paper, we present Fograph, a novel distributed real-time GNN inference framework that leverages diverse resources of multiple fog nodes in proximity to IoT data sources. By introducing heterogeneity-aware execution planning and GNN-specific compression techniques, Fograph tailors its design to well accommodate the unique characteristics of GNN serving in fog environment. Prototype-based evaluation and case study demonstrate that Fograph significantly outperforms the state-of-the-art cloud serving and vanilla fog deployment by up to 5.39 × execution speedup and 6.84 × throughput improvement.
Accurate and Explainable Recommendation via Review Rationalization

Auxiliary information, such as reviews, have been widely adopted to improve collaborative filtering (CF) algorithms, e.g., to boost the accuracy and provide explanations. However, most of the existing methods cannot distinguish between co-appearance and causality when learning from the reviews, so that they may rely on spurious correlations rather than causal relations in the recommendation — leading to poor generalization performance and unconvincing explanations. In this paper, we propose a Recommendation via Review Rationalization (R3) method including 1) a rationale generator to extract rationales from reviews to alleviate the effects of spurious correlations; 2) a rationale predictor to predict user ratings on items only from generated rationales; and 3) a correlation predictor upon both rationales and correlational features to ensure conditional independence between spurious correlations and rating predictions given causal rationales. Extensive experiments on real-world datasets show that the proposed method can achieve better generalization performance than state-of-the-art CF methods and provide causal-aware explanations even when the test data distribution changes.
Translating Place-Related Questions to GeoSPARQL Queries

Many place-related questions can only be answered by complex spatial reasoning, a task poorly supported by factoid question retrieval. Such reasoning using combinations of spatial and non-spatial criteria pertinent to place-related questions is increasingly possible on linked data knowledge bases. Yet, to enable question answering based on linked knowledge bases, natural language questions must first be re-formulated as formal queries. Here, we first present an enhanced version of YAGO2geo, the geospatially-enabled variant of the YAGO2 knowledge base, by linking and adding more than one million places from OpenStreetMap data to YAGO2. We then propose a novel approach to translate the place-related questions into logical representations, theoretically grounded in the core concepts of spatial information. Next, we use a dynamic template-based approach to generate fully executable GeoSPARQL queries from the logical representations. We test our approach using the Geospatial Gold Standard dataset and report substantial improvements over existing methods.
Feasibility about the Direct Measurement of 226 Ra Using the

U should be corrected to calculate the net peak area in the energy spec-trum. In general, it is very difficult to conduct peaks stripping with difference of about 0.5 keV, although a HPGe with the superior resolution is applied and the maximum channels is applied to the spectrometer. In this study, several interference correction techniques in the direct measurement were surveyed to evaluate the feasibility for the measurement of
Distributing Fast EV Charging Stations in Saudi Highways

With the leaps and strides Saudi Arabia has been making in recent years, and its clear interest in making Saudi Arabia more dependent on Electric Vehicles taking form in the green initiative of the Saudi 2030 Vision. In this paper, a model that locates and distributes fast charging EV stations (FCS) among the highways connecting the major cities in Saudi Arabia and plan the most ideal distribution of stations is created using methods from Industrial Engineering. The first problem is determining the charging station's locations on a certain highway. The second problem is deciding how many charging stations will be placed in every specific location considering the parameters of each. For this study, the model is applied on the Jeddah-Riyadh-Dammam highway crossing the most important cities of Saudi Arabia. For the methodology, 15 locations are located along this route. For each location, the Criteria Priority Weight system (CPWS), a system used to calculate the composite weight of each location, is applied. Finally, we will apply our mathematical model with the set parameters to find the maximum user utility score, which corresponds to the optimal distribution of EV chargers upon the locations. The results yield a certain distribution of charging stations in those locations under budget, cost, and capacity constraints.
Preferences on a Budget: Prioritizing Document Pairs when Crowdsourcing Relevance Judgments

In Information Retrieval (IR) evaluation, preference judgments are collected by presenting to the assessors a pair of documents and asking them to select which of the two, if any, is the most relevant. This is an alternative to the classic relevance judgment approach, in which human assessors judge the relevance of a single document on a scale; such an alternative allows to make relative rather than absolute judgments of relevance. While preference judgments are easier for human assessors to perform, the number of possible document pairs to be judged is usually so high that it makes it unfeasible to judge them all. Thus, following a similar idea to pooling strategies for single document relevance judgments where the goal is to sample the most useful documents to be judged, in this work we focus on analyzing alternative ways to sample document pairs to judge, in order to maximize the value of a fixed number of preference judgments that can feasibly be collected. Such value is defined as how well we can evaluate IR systems given a budget, that is, a fixed number of human preference judgments that may be collected. By relying on several datasets featuring relevance judgments gathered by means of experts and crowdsourcing, we experimentally compare alternative strategies to select document pairs and show how different strategies lead to different IR evaluation result quality levels. Our results show that, by using the appropriate procedure, it is possible to achieve good IR evaluation results with a limited number of preference judgments, thus confirming the feasibility of using preference judgments to create IR evaluation collections.
Cross-modal Ambiguity Learning for Multimodal Fake News Detection

Cross-modal learning is essential to enable accurate fake news detection due to the fast-growing multimodal contents in online social communities. A fundamental challenge of multimodal fake news detection lies in the inherent ambiguity across different content modalities, i.e., decisions made from unimodalities may disagree with each other, which may lead to inferior multimodal fake news detection. To address this issue, we formulate the cross-modal ambiguity learning problem from an information-theoretic perspective and propose CAFE — an ambiguity-aware multimodal fake news detection method. CAFE consists of 1) a cross-modal alignment module to transform the heterogeneous unimodality features into a shared semantic space, 2) a cross-modal ambiguity learning module to estimate the ambiguity between different modalities, and 3) a cross-modal fusion module to capture the cross-modal correlations. CAFE improves fake news detection accuracy by judiciously and adaptively aggregating unimodal features and cross-modal correlations, i.e., relying on unimodal features when cross-modal ambiguity is weak and referring to cross-modal correlations when cross-modal ambiguity is strong. Experimental studies on two widely used datasets (Twitter and Weibo) demonstrate that CAFE outperforms state-of-the-art fake news detection methods by 2.2-18.9% and 1.7-11.4% on accuracy, respectively.
Prohibited Item Detection via Risk Graph Structure Learning

Prohibited item detection is an important problem in e-commerce, where the goal is to detect illegal items online for evading risks and stemming crimes. Traditional solutions usually mine evidence from individual instances, while current efforts try employing advanced Graph Neural Networks (GNN) to utilize multiple risk-relevant structures of items. However, it still remains two essential challenges, including weak structure and weak supervision. This work proposes the Risk Graph Structure Learning model (RGSL) for prohibited item detection. RGSL first introduces structure learning into large-scale risk graphs, to reduce noisy connections and add similar pairs. It then designs the pairwise training mechanism, which transforms the detection process as a metric learning from candidates to their similar prohibited items. Furthermore, RGSL generates risk-aware item representations and searches risk-relevant pairs for structure learning iteratively. We test RGSL on three real-world scenarios, and the improvements to baselines are up to 21.91% in AP and 18.28% in MAX-F1. Meanwhile, RGSL has been deployed on an e-commerce platform, and the improvements to traditional solutions are up to 23.59% in ACC@1000 and 6.52% in ACC@10000.
The Price to Play: A Privacy Analysis of Free and Paid Games in the Android Ecosystem

With an ever growing number of smartphone users, the mobile gaming industry is booming and reached more than 2.6 billion players worldwide in 2020. While some mobile games charge a relatively modest fee to be played, the vast majority are free and rely exclusively on ads or tracking for their revenue streams. Over the years, Google and Apple have tightened their privacy requirements for apps. They perform thorough app scanning to detect abusive behaviours and require developers to provide a privacy policy on how they collect and handle user data. Yet, little is known about the data collection that fuels the advertising and tracking industry behind mobile games. Players can see the ads that are presented to them but they may not be aware of the invisible trackers that collect valuable data in the background. In this study, we aim to shine a light on the tracking ecosystem in mobile games on Android and understand how different monetization models can impact user privacy. We introduce a pipeline that collects both free and paid games and we use the static analysis provided by the Exodus audit platform to detect the trackers present in them. We analyse a total of 6,751 games, including 396 paid games. Our results show that paying for a game does not necessarily shield users from data collection. We find that 87% of free games include at least one tracker, compared to 65% of paid games that do. On average, free games have 3.4 times more trackers and request twice more dangerous permissions than paid games. We also notice that the genre of the game and its targeted audience impact the number of trackers. Games in the Casual category presents the most trackers while those in the Educational one have the least.