Miracle-dz/UniFAR-FTUs huggingface dataset:
Notion of Explainable Artificial Intelligence - an Empirical Investigation from a User's Perspective

The growing attention to artificial intelligence-based applications has led to research interest in explainability issues. This emerging research attention on explainable AI (XAI) advocates the need to investigate end user-centric explainable AI. Thus, this study aims to investigate usercentric explainable AI and considered recommendation systems as the study context. We conducted focus group interviews to collect qualitative data on the recommendation system. We asked participants about the end users' comprehension of a recommended item, its probable explanation, and their opinion of making a recommendation explainable. Our findings reveal that end users want a non-technical and tailor-made explanation with on-demand supplementary information. Moreover, we also observed users requiring an explanation about personal data usage, detailed user feedback, and authentic and reliable explanations. Finally, we propose a synthesized framework that aims at involving the end user in the development process for requirements collection and validation.
Sampling reflectance functions for image-based relighting

A popular research topic in computer graphics is the acquisition of the appearance of real-world objects. An important aspect of the appearance of an object is how it reacts to incident illumination. The goal of image-based relighting is to visualize real objects under novel incident illumination, and this without explicit knowledge of the object's geometry or its material properties. The appearance of an object under incident illumination is characterized by its reflectance field. To acquire such a reflectance field, a series of photographs, from a fixed viewpoint, of the object is recorded under different controlled illumination conditions. Applying different illumination conditions to an object is mathematically equivalent to sampling its reflectance field. In the first part of this work, the physical and practical constraints imposed on image-based relighting are studied, and a mathematical framework is derived that encodes these constraints. This framework allows to describe, study, and compare existing relighting techniques, and allows to develop new, more efficient, methods. In the second part, the implications of sampling the reflectance field are studied in detail, and several reconstruction techniques are presented to enhance the visual and numerical quality of the relit results. Additionally, it is shown that an equivalent downsampling operator can be defined on the incident illumination that yields identical results with less computations as the advanced reflectance field reconstruction techniques. Two novel acquisition methods are presented in the third part of this dissertation. These methods differ from other acquisition methods in that they sample a wavelet represented reflectance field directly. The first of these methods samples the reflectance field selectively in the wavelet domain by progressively emitting selected wavelet basis illumination conditions. A feedback loop is used during acquisition to determine what part of the wavelet transformed reflectance field is worthwhile to sample in greater detail. The second technique also samples the reflectance field selectively in the wavelet domain, but decouples the acquisition process and the sampling of the wavelet represented reflectance field. To achieve this, the object is observed under a fixed number of wavelet noise illumination conditions. Afterwards, a progressive algorithm is used to determine the reflectance function of each pixel separately using the observed wavelet noise responses of this pixel. All of the previous techniques are restricted to 2D incident light fields. In the final part of this work, a novel method is presented that is able to relight objects with 4D incident illumination. This allows to visualize objects with spatially varying illumination effects such as spotlights effects and partial shadowing.
Sélection de variables pour l'apprentissage simultanée de tâches

Recently, there has been a lot of interest around multi-task learning (MTL) problem with the constraints that tasks should share common features. Such a problem can be addressed through a regularization framework where the regularizer induces a joint-sparsity pattern between task decision functions. We follow this principled framework but instead we focus on lp − l2 (with p ≤ 1) mixed-norms as sparsity-inducing penalties. After having shown that the l1 − l2 MTL problem is a general case of Multiple Kernel Learning (MKL), we adapted the available efficient tools of solving MKL to the sparse MTL problem. Then, for the more general case when p < 1, the use of a DC program provides an iterative scheme solving at each iteration a weighted 1 − 2 sparse MTL problem.
Making Data Engineering Declarative

1 ETL and ELT are required to prepare comprehensive, clean, and correct derived data that can fuel successful analytics and ML. Based on our observations from thousands of customers processing data in the cloud at Databricks, the preparation of derived data typically involves a complex DAG of transformations, which are split into two activities: (a) Ingestion: At the sources of the DAG, raw data are fetched from streaming platforms, like Apache Kafka TM and Amazon Kinesis, and from cloud storage that stages incoming data. This data is typically in blob stores such as AWS S3. The majority of our customers store it in Delta Lake tables, the data format that enables transaction processing on data lakes [1,2]. (b) Downstream ELT: Multiple transformations clean, enrich, and aggregate the ingested data. The downstream ELT typically leads to many transformations, using popular frameworks such as DBT, or individual jobs that transform the data. In the former case, the transformations are typically in SQL as CREATE-TABLE-AS (CTAS) statements. In all cases, the underlying processing engine sees a series of seemingly independent SQL queries. Delta
A NEW MODIFICATION IN DECOUPLED SCALED BOUNDARY METHOD WITH DIAGONAL COEFFICIENT MATRICES FOR ANALYSIS OF 2D ELASTOSTATIC AND TRANSIENT ELASTODYNAMIC PROBLEMS

The scaled boundary based methods are commonly known as semi-analytical approaches, which have very good accuracy and efficiency for solving various kinds of problems. In this paper, a new trend for improvement of the decoupled scaled boundary-finite element method (DSBFEM) in order to solve the 2D elastostatic and elastodynamic problems is provided. In this technique, only the boundaries of the problem domain are discretized by specific subparametric elements. Mapping functions are employed as a class of higher-order Lagrange polynomials which are set at Gauss-Lobatto-Legendre control points,so, the special shape functions, Gauss-Lobatto-Legendre numerical integration, and the integral form of the weighted residual method lead to the diagonal coefficient matrices in the governing equations. The main differences between the study conducted and the prior researches regarding decoupled scaled boundary-finite element method is that here in, geometry production procedure of the interpolation function, integration of the different is selected, and using this approach, we could reduce the complexity of the DSBFEM. Validity and accuracy of the present method are demonstrated through two benchmark elastostatic problems and three benchmark elastodynamic problems that are successfully modeled using a few numbers of DOFs. The numerical results agree very well with the analytical solutions and the results from other numerical methods.
Covert Resistance against Algorithmic control on Online Labor Platforms - a Systematic literature Review

Online labor platforms (OLPs) such as Uber or Upwork heavily rely on algorithms instead of human managers to control workers’ behavior. While algorithmic control (AC) allows platform providers to control their workers efficiently, it is often perceived by workers as a tighter control (compared to human-based control) which increases their motivation to resist. Especially covert resistance (i.e., workers’ hard-to-observe oppositional actions) provides essential insights into how workers deal with AC that affect platforms’ longevity. In this study, we conducted a systematic literature review to develop a theoretical framework showing how and why workers perform covert resistance against AC. Further, our analysis reveals the enabling role of sensemaking for diverse forms of covert resistance. Overall, our study expands the literature on AC by shedding light on the formation of workers’ covert resistance. Therefore, we offer platform providers and policymakers crucial insights to create fairer working environments for workers under AC.
Artificial Intelligence in Augmentative and Alternative Communication Systems - a literature-based Assessment and Implications of Different Conversation phases and Contexts

Even though AAC systems and corresponding AI approaches have been investigated within the extant research, they still show remarkable drawbacks, resulting in a low prevalence among speech-impaired individuals. As the suggestions and adaptions proposed by AI within AAC systems may show insuffi-ciencies in certain situations (e.g., unreliable suggestions, low conversational rates, unauthentic adap-tions towards users), we take an up-close look, especially at the conversation phases and contexts in which the supporting AI is applied. Therefore, we have conducted a systematic literature review as well as a literature analysis. Thereby, we could reveal that there are indeed several gaps within the extant research on AI regarding the coverage of the conversational context “informativeness” and the conversation phases “beginning” and “closing”. To dismantle the existing communication barriers that speech-impaired individuals suffer from, several implications for investigating AI in the context of AAC systems are derived and proposed for future (IS) research.
Mediating the tension between Data Sharing and Privacy: the Case of DMA and GDPR

The Digital Markets Act (DMA) constitutes a crucial part of the European legislative framework addressing the dominance of ‘Big Tech’. It intends to foster fairness and competition in Europe’s digital platform economy by imposing obligations on ‘gatekeepers’ to share end-user-related information with business users. Yet, this may involve the processing of personal data subject to the General Data Protection Regulation (GDPR). The obligation to provide access to personal data in a GDPR-compliant manner poses a regulatory and technical challenge and can serve as a justification for gatekeepers to refrain from data sharing. In this research-in-progress paper, we analyze key tensions between the DMA and the GDPR through the paradox perspective. We argue through a task-technology fit approach how privacy-enhancing technologies – particularly anonymization techniques – and portability could help mediate tensions between data sharing and privacy. Our contribution provides theoretical and practical insights to facilitate legal compliance.
Visualization Literacy for General Audiences - Can We Make A Difference?

—In this panel, we will discuss strategies and challenges associated with increasing the visualization literacy of general audiences (and adjacent data-related ﬁelds). We will hear from experts who have tested various techniques to increase visualization literacy and developed interactive apps/games/websites to inform and educate varying audiences about the fundamentals of data visualization. We will hear from experts who have worked towards increasing the visualization literacy of participants through various strategies such as videos, websites, tutorials, and so on. The panel will discuss open challenges in the ﬁeld of visualization literacy and the problems faced when introducing novel visualization techniques to potential users.
On The Elzaki Transform of Heaviside Step Function with a Bulge Function

The aim of this paper, is study the non-homogeneous second order differential equation of the Heaviside step function with a bulge function. Elzaki transform, inverse Elzaki transform and Power series expansion are mentioned to obtain the solution of differential equation of the Heaviside step function with a bulge function.
Impact of the Atmospheric Boundary Layer Height on the Concentration of Chemical Species in Numerical Model Simulations

The top of the atmospheric boundary layer (ABL) serves as a boundary separating turbulent air in the ABL and free atmosphere in the troposphere. The increase/decrease of the ABL height (ABL-H) can dilute or concentrate chemical species in the ABL. In numerical simulations the ABL-H is involved in computing the vertical diffusivity and the counter-gradient term under the unstable condition in ABL parameterization schemes. Therefore, uncertainties in the ABL-H due to the uncertainties 5 in meteorological ﬁelds and different deﬁnitions can affect the numerical predictions of the concentration of chemical species. To understand the impacts, a 1-D diffusion model with the K-proﬁle scheme and an 3-D air quality forecast model with the turbulent kinetic energy (TKE)-based scheme are employed to examine the sensitivities of diffusion coefﬁcient, counter-gradient term and tracer’s concentration to the ABL-H analytically and numerically. Sensitivity tests with the 1-D model show that the increase/decrease of the ABL-H leads to the decrease/increase of concentration of tracers under both stable and unstable condi-10 tions due to the increase/decrease of the volume of air within the ABL. Under the unstable condition, the increase/decrease of the ABL-H also enhances/weakens the vertical diffusivity, and leads to the decrease/increase of the concentration of tracers for negative/positive vertical gradient of tracer. The impact of the ABL-H through the counter-gradient term is much smaller than the impact through changing the volume of trace and vertical diffusivity in the ABL. Sensitivity tests with the 3-D numerical model with the TKE scheme show increase/decrease of the ABL-H leads to strong/weak vertical diffusivity, but has very small 15 impact of the ABL-H on the concentrations of pollutants over urban centers for the ABL-Hs which is deﬁned based on the Richardson number and temperature.
Design a closed loop PFC based Bridge-less Landsman Converter

: This work focus on design of closed loop BL landsman converter to enhance power factor of the input AC. This is accomplished by substituting the prevalent diode converter with a newer, high quality landsman converter that is equipped with power factor tuning. The recommended PFC converter is regulated with a single entity to attain consistent DC link voltage and to boost power factor closed to unity, with the simulation carried out using MATLAB. At the front end, landsman-based PFC converter is proposed to reduce the harmonics present in AC source filter circuit with a rating of 90 - 250 V as an AC input and the DC link output voltage ratings are from 50 – 200 V.
T WO B IRDS , O NE S TONE : A N E QUIVALENT T RANS - FORMATION FOR H YPER - RELATIONAL K NOWLEDGE G RAPH M ODELING

By representing knowledge in a primary triple associated with additional attributevalue qualifiers, hyper-relational knowledge graph (HKG) that generalizes triplebased knowledge graph (KG) has been attracting research attention recently. Compared with KG, HKG is enriched with the semantic difference between the primary triple and additional qualifiers as well as the structural connection between entities in hyper-relational graph structure. However, to model HKG, existing studies mainly focus on either semantic information or structural information therein, fail to capture both simultaneously. To tackle this issue, in this paper, we propose an equivalent transformation for HKG modeling, referred to as TransEQ. Specifically, the equivalent transformation transforms a HKG to a KG, which considers both semantic and structural characteristics. Then a generalized encoder-decoder framework is developed to bridge the modeling research between KG and HKG. In the encoder part, KG-based graph neural networks are leveraged for structural modeling; while in the decoder part, various HKG-based scoring functions are exploited for semantic modeling. Especially, we design the sharing embedding mechanism in the encoder-decoder framework with semantic relatedness captured. We further theoretically prove that TransEQ preserves complete information in the equivalent transformation, and also achieves full expressivity. Finally, extensive experiments on three benchmarks demonstrate the superior performance of TransEQ in terms of both effectiveness and efficiency. On the largest benchmark WikiPeople, TransEQ significantly improves the state-of-the-art models by 15% on MRR.
Bachelorarbeit Covering Rectilinear Polygons with Few and Small Axis-Aligned Rectangles

Representations of polygons as simple shapes such as sets of rectangles are of interest in many areas. We present a problem that has, to our knowledge, not been studied before in which we seek to represent a polygon with only vertical and horizontal edges which may contain holes as a set of rectangles that are allowed to overlap while minimizing the cost of the rectangles, which is equal to α + β · A ( R ) where A ( R ) is the area of the rectangle and α, β ∈ N 0 are freely chosen, but the same for all rectangles. In this thesis, we deﬁne this special case of the weighted geometric set cover problem, discuss other closely related problems, give several heuristic algorithms as well as an integer linear program formulation for it and evaluate them on a large number of polygons, comparing both the cost of the solutions returned by the algorithms as well as their runtime performance.
Silent enhancement of SERS signal without increasing hot spot intensities

Abstract Plasmonic nanostructures enhance nonlinear response, such as surface enhanced Raman scattering (SERS), by localizing the incident field into hot spots. The localized hot spot field can be enhanced even further when linear Fano resonances take place in a double resonance scheme. However, hot spot enhancement is limited with the modification of the vibrational modes, the breakdown of the molecule, and the tunneling regime. Here, we present a method which can circumvent these limitations. Our analytical model and solutions of 3D Maxwell equations show that: enhancement due to the localized field can be multiplied by a factor of 102–103. Moreover, this can be performed without increasing the hot spot intensity which also avoids the modification of the Raman modes. Unlike linear Fano resonances, here, we create a path interference in the nonlinear response. We demonstrate on a single equation that enhancement takes place due to cancellation of the contributing terms in the denominator of the SERS response. Our method can be implemented on an atomic force microscope tip, decorated (or “contaminated”) with appropriate quantum emitters.
Improving Text Accessibility using Text Simplification and Conversation Disentanglement

Accessibility to text for machines i.e. for downstream tasks, has been improved greatly using mainstream NLP tasks such as Machine Translation and Summarization. In this dissertation, however, we delve into two lesser-explored research areas: Text Simplification (reducing the linguistic complexity of text) and Conversation Disentanglement (extracting individual conversation threads from multi-party dialogues). We pursue text simplification by analyzing the claim of utilizing control tokens in input text to produce simplified versions with precise variations along key text attributes [1]. The effect of control tokens on simplifications was isolated individually and in particular combinations through dedicated models. We show that though control tokens can help mold the outputs closer to the target simplifications, they often cause undesired interactions between control tokens and text attributes as well as within control tokens. This sometimes results in outputs being trivial source text variants that adhere to the control token constraints but do not furnish true simplifications. We offer methods to curb some of these trivial outputs e.g. masking named-entities to shield them from unwanted replacements and to curb the extent of data sparsity using pre-trained input embeddings. Our modifications produce fewer trivial outputs and show better resilience to unseen contexts. As part of our work in conversation disentanglement, we explore the most widely used expertannotated resource in the field: Ubuntu-IRC (IRC) dataset [2]. We question IRC’s utility for training generalizable models for other domains which currently lack similar large-scale, high-quality datasets, e.g., meeting transcripts & movie dialogues. We address this concern by analyzing IRC for potential sources of platform-based specificity. We find that using direct mentions (explicit references to a message’s recipient) in IRC is one such instance of platform specificity that occurs abundantly in the dataset. To measure its impact on model performance, we create Ubuntu-IRC-Hard – a variant of IRC without direct mentions. We show that the performance of past approaches (and proposed baselines) degrades significantly without direct mentions. An in-depth analysis of models is conducted using qualitative metrics based on dialogue properties which reveal brittleness and variations among models across properties. We introduce two methods that leverage these insights to alleviate this brittleness: (i) A weighted-loss formulation to better represent less frequent sub-ranges of dialogue properties in the data, and (ii) An informed-ensembling technique that determines a subset of models best suited for each property sub-range. Both methods surpass SOTA [3] performance and score more consistently across dialogue properties.
Quantifying the seasonal variations and regional transport of PM 2.5 1 in the Yangtze River Delta region, China: Characteristics, sources, 2 and health risks

Given the increasing complexity of the chemical composition of PM 2.5 , identifying and quantitatively assessing 11 the contributions of pollution sources has played an important role in formulating policies to control particle pollution. This 12 study provides a comprehensive assessment between PM 2.5 chemical characteristics, sources, and health risks based on 13 sampling data conducted over one year (March 2018 to February 2019) in Nanjing. Results show that PM 2.5 exhibits a 14 distinct variation across different seasons, which is primarily driven by emissions, meteorological conditions, and chemical 15 conversion of gaseous pollutants. First, the chemical mass reconstruction shows that secondary inorganic aerosols (SIA, 16 62.5 %) and carbonaceous aerosols (21.3%) contributed most to the PM 2.5 mass. The increasing oxidation rates of SO 2 and 17 NO 2 from summer to winter indicate that the secondary transformation of gaseous pollutants is strongly positively correlated 18 with relative humidity. Second, the positive matrix factorization (PMF) method shows that identified PM 2.5 sources include 19 SIA (42.5%), coal combustion (CC, 22.4%), industry source (IS, 17.3%), vehicle emission (VE, 10.7%), fugitive dust (FD, 20 5.8%) and other sources (1.3%). The Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model and the 21 concentration-weighted trajectory (CWT) analysis are used to further explore different spatial distributions and regional