Miracle-dz/UniFAR-FTUs huggingface dataset:
Reasoning on Multi-Relational Contextual Hierarchies via Answer Set Programming with Algebraic Measures (Extended Abstract)

This extended abstract summarizes our previous work on a defeasible extension of Description Logic (DL) for contextual reasoning. 1 Here, we considered on the one hand the addition of multiple dimensions of defeasibility, allowing us to express for example that a rule has to be satisfied no matter the geographical context but that the rule can change in the next years. On the other hand, we showed that Answer Set Programming (ASP) especially when enhanced with algebraic measures provide a powerful tool to implement our framework and open up perspectives for the future
Towards A Statistic Ontology for Data Analysis in Smart Manufacturing

Statistical analytics has been playing an important role for uncovering patterns and trends from data in smart manufacturing. However, how statistical analytics are performed is usually stored in the underlying code and suffers from transparency and re-usability, which is vital for modern industry. To this end, we propose a statistical ontology StatsOnto that models not only the concepts in statistics, but also allows encoding the procedure of statistical analytics, which is limitedly addressed in past works. We present a preliminary evaluation of StatsOnto with a Bosch use case with a user study, competence question and coverage discussion in this poster paper. statistical methods, but they insufficiently study the procedures of the statistical pipelines.
Rapid Explainability for Skill Description Learning

We tackle the problem of learning the description of skills of machines within an Industry 4.0 setting using Class Expression Learning (CEL). CEL deals with learning description logic concepts from an RDF knowledge base and input examples. The goal is to learn a concept that covers all positive examples while not covering any negative examples. Although state-of-the-art models have been successfully applied to tackle this problem, their application at large scale, e.g., for skill learning, have been severely hindered due to their impractical runtimes. We report on the initial results of the RAKI project jointly carried out by Paderborn University, Leipzig University, and Siemens AG. We designed a framework to facilitate CEL on large industrial RDF knowledge bases. Our framework learns concepts significantly faster than state-of-the-art models. Our verbalisation approaches ensure that our framework yields interpretable results. Our framework is open-source to foster large-scale applications.
A Secure Canary-Based Hardware Approach Against ROP

With the growing demand for embedded systems, from home appliances to industrial usage, security is a significant challenge. Return-oriented programming (ROP) and Code reuse attacks are among the most dangerous attacks. They aim to hijack the control flow program to bypass system restrictions and/or execute malicious code. Stack canary is a well-known defense mechanism against these types of attacks. Generally, they employ a secret value to sit in the memory right before a specific location of the memory that is about to be protected. The security of such a system relies on keeping the canary value secret. However, for a single, unaltered key, it is difficult to guarantee secrecy. On the other hand, having limited processing capabilities and restricted resources presupposes embedded designers. Security is critical for many real-time applications (like industrial IoT devices), and any protection must comply with the processor speed and memory capacity. This paper proposes a lightweight hardware extension to protect the memory against ROP attacks. The proposed method is a canary-based technique that utilizes Physical Unclonable Functions (PUF) to generate dynamic, unpredictable values. The canary generator security module works in parallel with the processor to avoid any extra performance overhead. In general, our technique is independent of system architecture and even supports processors with multi-execution units.
Autonomous critical help provided by an artificial agent in the field of cultural heritage

In this work we introduce a computational cognitive model that provide an intelligent agent (e.g. robot, virtual assistant) with the capability to personalize a museum visit. The personalization is based on the goals, interests of the user that intends to visit the museum and considers also the goals, interests of the museum curators that have designed the exhibition. We introduce and evaluate a special type of help provided by the agent, called Critical Help, that can lead to a change of the user’s request, with the goal to consider needs that the same user cannot or has not been able to assess. The computational model has been implemented by exploiting the well known agent oriented programming framework Jason. We recruited 26 real participants in a preliminary robotic experiment that we conducted in order to test the computational model. Each participant has interacted with the humanoid robot Nao, widely used in Human-Robot Interaction (HRI) scenarios.
Towards quality-of-service metrics for symbolic knowledge injection

The integration of symbolic knowledge and sub-symbolic predictors represents a recent popular trend in AI. Among the set of integration approaches, Symbolic Knowledge Injection (SKI) proposes the exploitation of human-intelligible knowledge to steer sub-symbolic models towards some desired behaviour. The vast majority of works in the field of SKI aim at increasing the predictive performance of the sub-symbolic model at hand and, therefore, measure SKI strength solely based on performance improvements. However, a variety of artefacts exist that affect this measure, mostly linked to the quality of the injected knowledge and the underlying predictor. Moreover, the use of injection techniques introduces the possibility of producing more efficient sub-symbolic models in terms of computations, energy, and data required. Therefore, novel and reliable Quality-of-Service (QoS) measures for SKI are clearly needed, aiming at robustly identifying the overall quality of an injection mechanism. Accordingly, in this work, we propose and mathematically model the first – up to our knowledge – set of QoS metrics for SKI, focusing on measuring injection robustness and efficiency gain.
Chiral gravity in higher dimensions

We construct a chiral theory of gravity in seven and eight dimensions, which is equivalent to Einstein? Cartan theory using fewer variables. In these dimensions, we can construct such higher-dimensional chiral gravity because of the existence of the gravitational instanton. The octonionic-valued variables in the theory represent the deviation from the gravitational instanton, and from their non-associativity, prevent the theory being SO(n) gauge invariant. Still the chiral gravity holds G2 (7D), and spin(7) (8D) gauge symmetry.
Wikidata to Bootstrap an Enterprise Knowledge Graph: How to Stay on Topic?

An Enterprise Knowledge Graph (EKG) is a major asset for companies as it supports several downstream tasks, e.g. , business vocabulary sharing, search, or question answering. The bootstrap of an EKG can rely on internal business data sets or open knowledge graphs. In this paper, we consider the latter approach. We propose to build the nucleus of an EKG class hierarchy by mapping internal business terms to Wikidata entities and performing an expansion along the ontology hierarchy. This nucleus thus contains additional business terms that will, in turn, support further knowledge extraction approaches from texts or tables. However, since Wikidata contains numerous classes, there is a need to limit this expansion by pruning classes unrelated to the business topics of interest. To this aim, we propose to rely on the distance between node embeddings and node degree. We show that considering the embedding of a class as the centroid of the embeddings of its instances improves pruning and that node degree is a necessary feature to consider.
Semantic memory and creativity: the costs and benefits of semantic memory structure in generating original ideas

Abstract Despite its theoretical importance, little is known about how semantic memory structure facilitates and constrains creative idea generation. We examine whether the semantic richness of a concept has both benefits and costs to creative idea generation. Specifically, we tested whether cue set size—an index of semantic richness reflecting the average number of elements associated with a given concept—impacts the quantity (fluency) and quality (originality) of responses generated during the Alternate Uses Task (AUT). Across four studies, we show that low-association, sparse, AUT cues benefit originality at the cost of fluency compared to high-association, rich, AUT cues. Furthermore, we found an interaction with individual differences in fluid intelligence in the low-association AUT cues, suggesting that constraints of sparse semantic knowledge can be overcome with top-down intervention. Our findings indicate that semantic richness differentially impacts the quality and quantity of generated ideas, and that cognitive control processes can facilitate idea production when conceptual knowledge is limited.
PI EXCITATION OF THE T-J MODEL

In this paper, we present analytical and numerical calculations of the pi resonance in the t-J model. We show in detail how the pi resonance in the particle-particle channel couples to and appears in the dynamical spin correlation function in a superconducting state. The contribution of the pi resonance to the spin excitation spectrum can be estimated from general model-independent sum rules, and it agrees with our detailed calculations. The results are in overall agreement with the exact diagonalization studies of the t-J model. Earlier calculations predicted the correct doping dependence of the neutron resonance peak in the YBCO superconductor, and in this paper detailed energy and momentum dependence of the spin correlation function is presented. The microscopic equations of motion obtained within current formalism agree with that of the SO(5) nonlinear sigma model, where the pi resonance is interpreted as a pseudo Goldstone mode of the spontaneous SO(5) symmetry breaking.
New Jersey Psychologist Positive Psychology

ositive psychology focuses on the factors that allow individuals (and organizations and communities) to thrive and to be optimally functioning. One important question becomes “How best to attain such a level of high func-tioning? ” We suggest that one answer is having a passion for a specific activity. Indeed, regularly feeling enthusiastic, alive, and passionate while engaging in an enjoyable and meaningful activity should affect people’s lives in a positive and significant way. Research supports this assumption, but also reveals that passion may represent a risk factor with respect to experiencing some negative outcomes.
Myths about Treatment of Diabetes in Common Public

Diabetes can be associated with major health hazards and long term complications. The problem of diabetics has reached a pandemic. The society of India has a long record of the stage of the diabetes epidemic. The large public health problem of diabetics is growing quickly every year. Treatment of diabetes and awareness can be generated through a health education process. Myths of diabetics is shared by a large group of people to be part of cultural identity. Strong influence can effected during the treatment by the individuals in real life. The correct understanding of misconceptions and myths about the diabetic disease is important in providing health education for healthy individuals.
One-dimensional edge contacts to a monolayer semiconductor.

Integration of electrical contacts into van der Waals (vdW) heterostructures is critical for realizing electronic and optoelectronic functionalities. However, to date no scalable methodology for gaining electrical access to buried monolayer two-dimensional (2D) semiconductors exists. Here we report viable edge contact formation to hexagonal boron nitride (hBN) encapsulated monolayer MoS2. By combining reactive ion etching, in-situ Ar+ sputtering and annealing, we achieve a relatively low edge contact resistance, high mobility and high on-current density, comparable to top contacts. Furthermore, the atomically smooth hBN environment also preserves the intrinsic MoS2 channel quality during fabrication, leading to a steep subthreshold swing of 116 mV/dec with a negligible hysteresis. Hence, edge contacts are highly promising for large-scale practical implementation of encapsulated heterostructure devices, especially those involving air sensitive materials, and can be arbitrarily narrow, which opens the door to further shrinkage of 2D device footprint.
Research on Insulator Defect Detection Based on an Improved MobilenetV1-YOLOv4

Insulator devices are important for transmission lines, and defects such as insulator bursting and string loss affect the safety of transmission lines. In this study, we aim to investigate the problems of slow detection speed and low efficiency of traditional insulator defect detection algorithms, and to improve the accuracy of insulator fault identification and the convenience of daily work; therefore, we propose an insulator defect detection algorithm based on an improved MobilenetV1-YOLOv4. First, the backbone feature extraction network of YOLOv4 ‘Backbone’ is replaced with the lightweight module Mobilenet-V1. Second, the scSE attention mechanism is introduced in stages of preliminary feature extraction and enhanced feature extraction, sequentially. Finally, the depthwise separable convolution substitutes the 3 × 3 convolution of the enhanced feature extraction network to reduce the overall number of network parameters. The experimental results show that the weight of the improved algorithm is 57.9 MB, which is 62.6% less than that obtained by the MobilenetV1-YOLOv4 model; the average accuracy of insulator defect detection is improved by 0.26% and reaches 98.81%; and the detection speed reaches 190 frames per second with an increase of 37 frames per second.
User Interfaces for Exploratory Data Analysis: A Survey of Open-Source and Commercial Tools

Exploratory Data Analysis (EDA) is an important step in data processing pipelines, for which many tools have been developed. In this article we take an initial step in evaluating the relationship between users and EDA tools, as mediated by their interfaces, across different stages of the EDA process. We compare 11 commercial and open-sourced tools, categorizing their interfaces with respect to three stages of EDA: 1) generating initial questions, 2) creating visualizations, and 3) examining visualizations. Finally, we discuss trade-offs between different interfaces, and identify future work and research opportunities for the broader EDA technical community.
EXPLAINING THE DECISIONS OF ANOMALOUS SOUND DETECTORS

Deciding whether a sound is anomalous is accomplished by comparing it to a learnt distribution of inliers. Therefore, learning a distribution close to the true population of inliers is vital for anomalous sound detection (ASD). Data engineering is a common strategy to aid training and improve generalisation. However, in the context of ASD, it is debatable whether data engineering indeed facilitates generalisation or whether it obscures characteristics that distinguish anomalies from inliers. We conduct an exploratory investigation into this by focusing on frequency-related data engineering. We adapt local model explanations to anomaly detectors and show that models rely on higher frequencies to distinguish anomalies from inliers. We verify this by filtering the input data’s frequencies and observing the change in ASD performance. Our results indicate that sifting out low frequencies by applying high-pass filters aids down-stream performance, and this could serve as a simple pre-processing step for improving anomaly detectors.
TechnicalNote Papain Adulteration in 11-nor-Ag-Tetrahydrocannabinol-9-carboxylic Acid-Positive Urine Samples

The adulteration of urine samples is an ongoing problem in forensic drug-testing laboratories, even in the military where the practice of observed collections is performed. These adulterants are used to produce a false-negative result when samples are analyzed for drugs of abuse. It has been reported that papain, a cysteine protease, could be successfully used as a urine adulterant, altering the concentration of 11-nor-Ag-tetrahydrocannabinol-9carboxylic acid (THCCOOH) in urine samples. The current study analyzes the effects of latex papain (Sigma, 10 mg/mL) and Lawry's | Adolph's Meat Tenderizer (papain is an active ingredient, 10 mg/mL) on immunoassays (FPIA, EMIT, KIMS) and gas chromatography-mass spectrometry (GC-MS) analysis for biological samples. The samples were analyzed initially between 2 and 4 h and then at 1-, 3-, 7-, and 10-day time intervals after the addition of papain. A decrease in response averaged over the course of the study was observed with FPIA (Abbott, 22%) and EMIT (Syva | Dade Behring, 26%, Microgenics, 10%) screening assays by the addition of latex papain to the samples. An increase in response was found using the KIMS (Roche) assay (156% increase). In addition, the GC-MS results (27% decrease) demonstrate that papain affects both the screening and confirmation assays. The addition of meat tenderizer caused a decrease in the FPIA (Abbott, 11%) screening assay and GC-MS results (22%) similar to the latex papain while having varied results on the other screening assays. This study confirms papain could be a potential problem for urine drug-testing programs.
Park Design Informed by Stated Preference Choice: Integrating User Perspectives into the Development of an Off-Road Vehicle Park in Michigan

At a time when many public park and recreational programs are required by local governments to be financially self-sustaining, it is critical for planners to design a new development with the end-user in mind. Feasibility studies often either do not examine user preferences or use Likert-type surveys to investigate features in isolation without evaluating trade-offs from financial and finite space limitations. This study used stated preference choice method (SPCM) to inform the initial design of an off-road vehicle (ORV) park. The park was developed near Detroit, Michigan, a metropolitan area with many registered ORVs, but few places to legally use them. The SPCM examined trade-offs among desired features and helped planners ensure publicly funded investments resulted in a successful park. Researchers mailed a survey with choice sets to 3935 registered ORV users and 2083 completed surveys were retuned (53%). Additional survey items also allowed researchers to create preference models for specific segments of users (i.e., serious ORV enthusiasts/casual users; residents/visitors; or users of different ORV types). The findings informed the design of the park by revealing preferences for segments, allowing planners to design the park for specific markets. The park’s initial success suggests a study in the design stage of development offers utility, though park managers have noticed unanticipated user segments that influence preferences for park features. The findings based on segments also suggest planners should be cautious when designing to an average user. Implications of this study are helpful to planners of any capital-intensive land-use project, especially in the public sector.
Statistical modelling and latent variables

Criminometrics, Latent Variables, and Panel Data (J. Aasness, E. Eide, T. Skjerpen). Scale Construction by Maximizing Reliability (D.J. Bartholomew, M. Knott). Finite Sample Properties of Limited Information Estimation: An Algebraic Approach (P.A. Bekker). Structural Equation Models as Nonlinear Regression Models (P.M. Bentler). Stochastic Frontier and Switching Regression Models with Latent Variables (R. Colombi). Latent Class Models with Ordered Latent Classes: An Approach to Nonparametric Latent Trait Modelling (M.A. Croon). System Identification and Errors in the Variables (M. Deistler, W. Scherrer). Principal Components and Proportionality in Patterned Covariance Matrices (B.D. Flury, B. Neuenschwander). Errors-in-Variables Identification and Model Uniqueness (R.P. Guidorzi). Measurement Error Models with Unequal Error Variances (N.A. Hasabelnaby, W.A. Fuller). Latent Variable Modelling with Ordinal Variables (K.G. Joereskog). Asymptotic Properties of Statistical Inference Based on Fisher Consistent Estimators in the Analysis of Covariance Structures (Y. Kano). Imposed Quasi-Normality in Covariance Structure Analysis (R. Koning, H. Neudecker, T. Wansbeek). Structural Equation Models with Hierarchical Data (S.-Y. Lee, W.-Y. Poon). Global Optimization Criteria of the PLS-Algorithm in Recursive Path Models with Latent Variables (H. Mathes). Structural Equation Models with Transformed Variables (A. Mooijaart). Individual Unit Models versus Structural Equations: Growth Curve Examples (D. Rogosa). Multi-Sample Analysis of Moment-Structures: Asymptotic Validity of Inferences Based on Second-Order Moments (A. Satorra). Consistency at Large in Models with Latent Variables (H. Schneeweiss). A DYMIMIC Model of Employment: Another Look at Some Issues of Formulation, Identification and Estimation (U. Trivellato, S. Bordignon, C. Gaetan). General Least Squares Regression in Linear Errors-in-Variables Models with Correlated Errors (L. Wang).
A Multimodal Dataset for Gait Recognition in Different Terrains using Wearable Sensors

Gait has been shown to be a profound movement in human activities, and gait recognition is a commonly used biometric recognition in recent years. Gait recognition based on wearable sensors has been involved in various application areas. Especially in the area of medical, gait research is an essential issue. The purpose of this paper is to provide a multimodal public dataset for use with gait recognition. The dataset is derived of data from wearable inertial sensors and ECG sensor. Both sensors provide easy-to-operate and low-cost data recording for gait recognition. The gait dataset is based on the data from 15 healthy adults whose lower limbs have neither been injured nor operated on in the past year. Unlike other well-known datasets in the literature, this dataset contains inertial data (built-in gyroscope, accelerometer, geomagnetic field sensor) recorded from the ankle, as well as ECG data from a cardiac sensor. In this paper, the 15 volunteers were asked to walk at their most comfortable pace in four different terrains and complete the test. These four kinds of terrains are: flat land, sand, grassland and blind road. In addition, in order to verify the effectiveness of this multimodal dataset, this paper uses deep learning to identify the gait patterns of four terrains, and the recognition rate reaches 82%.
Decentralized Online Influence Maximization

We consider the problem of finding the maximally influential node in random networks where each node influences every other node with constant yet unknown probability. We develop an online algorithm that learns the relative influences of the nodes. It relaxes the assumption in the existing literature that a central observer can monitor the influence spread globally. The proposed algorithm delegates the online updates to the nodes on the network; hence requires only local observations at the nodes. We show that using an explore-then-commit learning strategy, the cumulative regret accumulated by the algorithm over horizon $T$ approaches $O(T^{2/3})$ for a network with a large number of nodes. Additionally, we show that, for fixed $T$, the worst case-regret grows linearly with the number $n$ of nodes in the graph. Numerical experiments illustrate this linear dependence for Chung-Lu models. The experiments also demonstrate that $\varepsilon$ -greedy learning strategies can achieve similar performance to the explore-then-commit strategy on Chung-Lu models.