Miracle-dz/UniFAR-FTUs huggingface dataset:
What if Interactive Explanation in a Scientific Literature Recommender System

Despite the vast amount of research on interactive recommender systems (RS) and explainable recommendation, there is a lack of research on how to incorporate interactivity features in explainable RS. To address this research gap, a possible solution to achieve interactive explanation could be to provide What if explanations that can help users iteratively build better mental models of how the RS works. Through an iterative human-centered design (HCD) process, we designed What if interactive explanations in the transparent Recommendation and Interest Modeling Application (RIMA) and explored how this type of explanation could impact the understandability of and interaction with an explainable RS. Our investigation showed that providing What if explanations has a positive effect on different aspects of a RS such as transparency, trust, user control, satisfaction
The Corpus of British Isles Spoken English (CoBISE): A. New Resource of Contemporary British, Irish Speech 187-194 Steven Coats: Mending Fractured Texts. A Heuristic Procedure for Correcting OCR data

In this paper we present an OCR correction pipeline for 19 th century printed Danish fraktur (gothic/blackletter). The work has been carried out at the University of Copenhagen in relation to a research project involving digital explorations of a corpus of some 900 Danish and Norwegian novels from 1870 to 1899, totalling app. 65 million words. Roughly 25% of these novels are printed in the traditional fraktur font, which was almost totally dominating in the beginning of the 19th century. These texts are important culturally, since they represent mostly forgotten, popular novels, however they pose technical and methodological challenges in terms of processing the text from printed page to digital corpus. In order to provide the best possible material for digital literary analysis as well as more linguistic studies, we designed an OCR correction pipeline for the fraktur part of the corpus consisting of several different heuristic correction steps, with reference to a gold standard. The first step is a preprocessing step which takes care of obvious and unambiguous OCR errors. In the second step we align our primary OCR output candidate (the output from Tesseract using the Fraktur.traineddata pretrained OCR model) with several other OCR output candidates and perform selective correction with reference to these. Especially the Danish “æ” and “ø” characters can be successfully recovered with reference to the Danish, non-fraktur dan.traineddata Tesseract model. Finally, in the third step, we employ the SymSpell spell checker to perform spelling correction backed by a word form dictionary hand-crafted from various relevant sources. The pipeline reduces the word error rate by 7.6 percentage points from 10.5% (89.5% correctly recognized word forms) to 2.8% (97.2% correctly recognized word forms) - an improvement of almost 73%. The character error rate (CER) similarly decreased from 1.94% to 0.54%.
The Quality Guardian: Improving Activity Label Quality in Event Logs Through Gamification

Data cleaning, the most tedious task of data analysis, can turn into a fun experience when performed through a game. This thesis shows that the use of gamification and crowdsourcing techniques can mitigate the problem of poor quality of process data. The Quality Guardian, a family of gamified systems, is proposed, which exploits the motivational drives of domain experts to engage with the detection and repair of imperfect activity labels in process data. Evaluation of the developed games using real-life data sets and domain experts shows quality improvement as well as a positive user experience.
Plugin for automatisation of phonetic-phonological analysis and obtaining analytical feedback for Spanish learners

We present in this article the Plugin for phonetic-phonological analysis in Spanish (PAFe), which consists of a series of scripts (a code written with a programming language (Python) that, implement three different intonation comparison algorithms of an ELE (Spanish as a foreign language) student and a native speaker of Spanish), allowing, in turn, three different types of analysis: global, tonal tendency and intersyllabic. In addition, PAFe has a database to keep a history of different types of data (user profile, pronunciation exercises and audios) and a graphical interface to include reports on pronunciation evolution in Praat, a tool for acoustic analysis. PAFe is a software solution that offers new functionalities of Praat and allows the following: (i) to perform a comparative analysis between the intonational patterns of an ELE student and a native speaker; (ii) to report the evolution of the acquisition of such patterns in Spanish thanks to the history of the stored data. In this way, automated feedback is provided to both students and teachers.
Distant Viewing of the Harry Potter Movies via Computer Vision

We present an exploratory study performing distant viewing via computer vision methods in the genre of fantasy movies. As a case study we use 10 modern fantasy movies of the Harry Potter franchise (also referred to as Wizarding World franchise). We apply methods and state-of-the-art models for color and brightness analysis, object detection, location classification as well as facial emotion recognition. We present descriptive results as well as inference statistics. Furthermore, we discuss the results and the quality of the methods for this unique use case and give examples. We were able to find significant differences in our statistical analysis in the results of the methods across the movies with the movies of the Harry Potter series getting darker and negative emotional expressions on faces becoming more frequent.
Test of Significance When Data Are Curves

With modern technology, massive data can easily be collected in a form of multiple sets of curves. New statistical challenge includes testing whether there is any statistically signi cant di erence among these sets of curves. In this paper, we propose some new tests for comparing two groups of curves based on the adaptive Neyman test and the wavelet thresholding techniques introduced in Fan (1996). We demonstrate that these tests inherit the properties outlined in Fan (1996) and they are simple and powerful for detecting di erences between two sets of curves. We then further generalize the idea to compare multiple sets of curves, resulting in an adaptive high-dimensional analysis of variance, called HANOVA. These newly developed techniques are illustrated by using a dataset on pizza commercial where observations are curves and an analysis of cornea topography in ophthalmology where images of individuals are observed. A simulation example is also presented to illustrate the power of the adaptive Neyman test.
Map of the Maps: Conceptualization of the Knowledge Maps

The company’s knowledge assets and ability to coordinate them become increasingly critical. Knowledge maps can scaffold knowledge search and decision-making by creating visible links between knowledge, its application, source, and owner. The term Knowledge map was used with various meanings in several scientific communities: knowledge management, education studies, organization studies, decision analysis, artificial intelligence, etc. The term itself is still rather vague. To generalize the variety of the definitions, one can use the term knowledge map as a description of the sources, ﬂows, owners, and application areas of knowledge within the organization. The current study considers knowledge maps from a knowledge management perspective and is devoted to generalizing the numerous scattered knowledge mapping research and practices. It aims to identify and consolidate knowledge maps interpretations by analyzing their contents. The discussed research results in developing the ontology of knowledge maps. Existing types of knowledge maps are associated with fragments of this ontology. The such ontology may help select the necessary building blocks (templates and elements) for knowledge mapping in specific companies and situations. The research methodology is based on literature review, semantic analysis, and ontological engineering. The paper also provides the knowledge mapping ontology application guidelines and demonstrates them via a case study.
Detection of soluble suppression of tumorigenicity 2 and N-terminal B-type natriuretic peptide in a rat model of aortic regurgitation: differential responses to omecamtiv mecarbil

Abstract Objectives Both N-terminal fragment of B-type natriuretic peptide (NT-proBNP) and soluble isoform of ST2 (sST2) have been identified as biomarkers of heart failure. We evaluated the plasma levels of NT-proBNP and sST2 in a rat model of severe aortic valve regurgitation (AR) and correlated these findings with echocardiographic measurements. We also examined the impact of omecamtiv mecarbil (OM) on these parameters. Methods The plasma levels of NT-proBNP and sST2 were measured in 18 rats both before and 2 months after surgical induction of AR, and at these same time points, in six rats assigned to a sham-procedure control group. Plasma biomarkers were then measured again after infusion of OM or placebo in rats with AR (n=8 and 10, respectively) and OM alone in the sham control rats (n=6). Echocardiographic measurements were collected before and 2 months after induction of AR. Results Our results revealed increased levels of plasma NT-proBNP (219 ± 34 pg/mL vs. 429 ± 374 pg/mL; p<0.001) in rats with AR at day 7 after infusion of placebo, whereas plasma levels of sST2 were higher in this cohort after infusion of either OM or placebo. We identified a significant positive correlation between plasma sST2 with posterior wall thickness in diastole (r=0.34, p<0.05) and total body weight (r=0.45, p<0.01) in rats with surgically induced AR. Conclusions Because sST2 increased markedly, whereas NT-proBNP remained unchanged, when OM was administered, we hypothesize that sST2 has a distinct capability to detect deleterious effects of passive muscle tension, not reliably assessed by NT-proBNP, in the setting of AR.
CHEMICAL ANALYSIS OF A CARBON-ENHANCED VERY METAL-POOR STAR: CD-27 14351

We present, for the first time, an abundance analysis of a very metal-poor carbon-enhanced star CD-27 14351 based on a high-resolution (R ∼ 48,000) FEROS spectrum. Our abundance analysis performed using local thermodynamic equilibrium model atmospheres shows that the object is a cool star with stellar atmospheric parameters, effective temperature Teff = 4335 K, surface gravity log g = 0.5, microturbulence ξ = 2.42 km s−1, and metallicity [Fe/H] = −2.6. The star exhibits high carbon and nitrogen abundances with [C/Fe] = 2.89 and [N/Fe] = 1.89. Overabundances of neutron-capture elements are evident in Ba, La, Ce, and Nd, with estimated [X/Fe] > 1, the largest enhancement being seen in Ce with [Ce/Fe] = 2.63. While the first peak s-process elements Sr and Y are found to be enhanced with respect to Fe, ([Sr/Fe] = 1.73 and [Y/Fe] = 1.91), the third peak s-process element Pb could not be detected in our spectrum at the given resolution. Europium, primarily an r-process element also shows an enhancement with [Eu/Fe] = 1.65. With [Ba/Eu] = 0.12, the object CD-27 14351 satisfies the classification criterion for a CEMP-r/s star. The elemental abundance distributions observed in this star are discussed in light of the chemical abundances observed in other CEMP stars in the literature.
A Reference Architecture for Participatory Budgeting in an eGovernment Landscape

Participatory Budgeting (PB) puts a part of a city’s public budget directly in the responsibility of its constituents – most notably, the citizens – who can make proposals on how to spend the money and vote on the proposals they like best. This type of resource allocation can increase democratic understanding and the efficiency of public spending. However, integrating or adding such IT-supported PB applications into eGovernment systems is far from a trivial task as a PB should not be isolated from the rest of the governmental IT systems but integrated. Thus, the complex system landscapes of the municipalities have to be touched. Reference architectures can guide the creation of future-proof software. Hence this paper is meant to assist the implementation of PB into an existing eGovernment landscape by proposing a PB reference architecture. It shall support administrations who plan to implement a PB by proposing a possible system architecture and integration scenarios. As PB is part of an eGovernment system, it also presents a state-of-the-art eGovernment IT architecture on which other applications can build.
Explainable Robo-Advisors: Empirical Investigations to Specify and Evaluate a User-Centric Taxonomy of Explanations in the Financial Domain

Even though Recommender Systems (RS) have been widely applied in various financial domains such as Robo-advisors (RA), these systems still operate as a black box with no or limited explanations. Even in cases, where explanations are provided, such systems are mostly designed from the developers’ perspective where the user needs and perspective of explanations are not taken into account. In this work, we aim to address the challenges of designing eXplainable Robo-Advisors (XRA) – by adopting a user-centric methodology. For this purpose, we applied a mixed-method approach, in which we conducted three qualitative focus group discussions (FGD) and supplemented the results with a quantitative survey insight. More specifically, we made two major contributions: 1) We extended the existing explanation categories to contextualize it for the financial domain – by identifying the user’s specific needs for explainability in the context of the financial domain, 2) We quantified the user preferences of specific explanations with regard to the financial domain and explainable RA – by evaluating the user’s personal relevance (PRE) and perceived quality (PQE) of explanations.
Robust entanglement generation by reservoir engineering

Following a recent proposal (Muschik et al 2011 Phys. Rev. A 83 052312), engineered dissipative processes have been used for the generation of stable entanglement between two macroscopic atomic ensembles at room temperature (Krauter et al 2011 Phys. Rev. Lett. 107 080503). This experiment included the preparation of entangled states which are continuously available during a time interval of 1 h. Here, we present additional material, further-reaching data and an extension of the theory developed in Muschik et al (2011 Phys. Rev. A 83 052312). In particular, we show how the combination of the entangling dissipative mechanism with measurements can give rise to a substantial improvement of the generated entanglement in the presence of noise.
A useful way to obtain the central charge of entanglement Hamiltonian -- Nested entanglement entropy

In this paper, we review how to obtain the central charge of a critical entanglement Hamiltonian through the nested entanglement entropy which was first introduced in [J. Lou et al. PRB 84, 245128 (2011)]. The critical phenomena of the entanglement Hamiltonian can be identified by the central charge obtained by the nested entanglement entropy. We review our previous studies[J. Lou et al. PRB 84, 245128 (2011), S. Tanaka et al. PRA 87, 214401 (2013)] in which we investigated certain entanglement nature of two-dimensional valence-bond-solid (VBS) state and quantum hard-square models on square and triangle ladders using the nested entanglement entropy.
A3GAN: Attribute-Aware Anonymization Networks for Face De-identification

Face de-identification (De-ID) removes face identity information in face images to avoid personal privacy leakage. Existing face De-ID breaks the raw identity by cutting out the face regions and recovering the corrupted regions via deep generators, which inevitably affect the generation quality and cannot control generation results according to subsequent intelligent tasks (eg., facial expression recognition). In this work, for the first attempt, we think the face De-ID from the perspective of attribute editing and propose an attribute-aware anonymization network (A3GAN) by formulating face De-ID as a joint task of semantic suppression and controllable attribute injection. Intuitively, the semantic suppression removes the identity-sensitive information in embeddings while the controllable attribute injection automatically edits the raw face along the attributes that benefit De-ID. To this end, we first design a multi-scale semantic suppression network with a novel suppressive convolution unit (SCU), which can remove the face identity along multi-level deep features progressively. Then, we propose an attribute-aware injective network (AINet) that can generate De-ID-sensitive attributes in a controllable way (i.e., specifying which attributes can be changed and which cannot) and inject them into the latent code of the raw face. Moreover, to enable effective training, we design a new anonymization loss to let the injected attributes shift far away from the original ones. We perform comprehensive experiments on four datasets covering four different intelligent tasks including face verification, face detection, facial expression recognition, and fatigue detection, all of which demonstrate the superiority of our face De-ID over state-of-the-art methods.
Scale-flow: Estimating 3D Motion from Video

This paper addresses the problem of normalized scene flow (NSF): given a pair of RGB video frames, estimating the 3D motion, which consisted of optical flow and motion-in-depth estimation. NSF is a powerful tool for action prediction and autonomous robot navigation, presenting the advantage of only needing a monocular and uncalibrated camera. However, most existing methods directly regress motion-in-depth from two RGB frames or optical flow, resulting in sub-accurate and non-robust results. Our key insight is the scale matching scheme-establishing correlations between two frames containing objects in different scales, to estimate dense and continuous motion-in-depth. Based on the scale matching, we propose a unified framework: Scale-flow, which combines scale matching and optical flow estimation. This combination makes optical flow estimation can use dense and continuous scale information for the first time, so that the moving foreground objects can be estimated more accurately. On KITTI, our monocular approach achieves the lowest error in the foreground scene flow task, even compared with the multi-camera method. Moreover, on the motion-in-depth estimation task, Scale-flow reduces the error by 34% compared with the best-published method. Code will be available.
PC-Dance: Posture-controllable Music-driven Dance Synthesis

Music-driven dance synthesis is a task to generate high-quality dance according to the music given by the user, which has promising entertainment applications. However, most of the existing methods cannot provide an efficient and effective way for user intervention in dance generation, e.g., posture-controllable. In this work, we propose a powerful framework named PC-Dance to perform adaptive posture-controllable music-driven dance synthesis. Consisting of an music-to-dance alignment embedding network (M2D-Align) and a posture-controllable dance synthesis (PC-Syn), PC-Dance allows fine-grained control by input anchor poses efficiently without artist participation. Specifically, to relieve the cost of artist participation but ensure generating high-quality dance efficiently, a self-supervised rhythm alignment module is designed to further learn the music-to-dance alignment embedding. As for PC-Syn, we introduce an efficient scheme for adaptive motion graph construction (AMGC), which could improve the efficiency of graph-based optimization and preserve the diversity of motions. Since there is few related public dataset, we collect an MMD-ARC dataset for music-driven dance synthesis. The experimental results on MMD-ARC dataset demonstrate the effectiveness of our framework and the feasibility for dance synthesis with adaptive posture controlling.
Box-FaceS: A Bidirectional Method for Box-Guided Face Component Editing

While the quality of face manipulation has been improved tremendously, the ability to control face components, e.g., eyebrows, is still limited. Although existing methods have realized component editing with user-provided geometry guidance, such as masks or sketches, their performance is largely dependent on the user's painting efforts. To address these issues, we propose Box-FaceS, a bidirectional method that can edit face components by simply translating and zooming the bounding boxes. This framework learns representations for every face component, independently, as well as a high-dimensional tensor capturing face outlines. To enable box-guided face editing, we develop a novel Box Adaptive Modulation (BAM) module for the generator, which first transforms component embeddings to style parameters and then modulates visual features inside a given box-like region on the face outlines. A cooperative learning scheme is proposed to impose independence between face outlines and component embeddings. As a result, it is flexible to determine the component style by its embedding, and to control its position and size by the provided bounding box. Box-FaceS also learns to transfer components between two faces while maintaining the consistency of image content. In particular, Box-FaceS can generate creative faces with reasonable exaggerations, requiring neither supervision nor complex spatial morphing operations. Through the comparisons with state-of-the-art methods, Box-FaceS shows its superiority in component editing, both qualitatively and quantitatively. To the best of our knowledge, Box-FaceS is the first approach that can freely edit the position and shape of the face components without editing the face masks or sketches. Our implementation is available at https://github.com/CMACH508/Box-FaceS.
Multi-directional Knowledge Transfer for Few-Shot Learning

Knowledge transfer-based few-shot learning (FSL) aims at improving the recognition ability of a novel object under limited training samples by transferring relevant potential knowledge from other data. Most related methods calculate such knowledge to refine the representation of a novel sample or enrich the supervision to a classifier during a transfer procedure. However, it is easy to introduce new noise during the transfer calculations since: (1) the unbalanced quantity of samples between the known (base) and the novel categories biases the contents capturing of the novel objects, and (2) the semantic gaps existing in different modalities weakens the knowledge interaction during the training. To reduce the influences of these issues in knowledge transfer-based FSL, this paper proposes a multi-directional knowledge transfer (MDKT). Specifically, (1) we use two independent unidirectional knowledge self-transfer strategies to calibrate the distributions of the novel categories from base categories in the visual and the textual space. It aims to yield transferable knowledge of the base categories to describe a novel category. (2) To reduce the inferences of semantic gaps, we first use a bidirectional knowledge connection to exchange the knowledge between the visual and the textual space. Then we adopt an online fusion strategy to enhance the expressions of the textual knowledge and improve the prediction accuracy of the novel categories by combining the knowledge from different modalities. Empirical studies on three FSL benchmark datasets demonstrate the effectiveness of MDKT, which improves the recognition accuracy on novel categories under limited samples, especially on $1$-shot and $2$-shot training tasks.