Miracle-dz/UniFAR-FTUs huggingface dataset:
TRANSIENT SECURITY RISK ASSESSMENT OF GLOBAL POWER SYSTEM BASED ON TIME MARGIN

An index of risk of contingencies utilizing the time margin of contingency and probability of contingency occurrence is defined, and an index of transient security risk of the system are also defined under certain kind of contingency set. Based on the proposed indices a method of transient security assessment is presented. It makes it possible to assess the transient security of the system with the combination of assessment result of certain kind of contingency set. Results of some test systems prove the effectiveness of the proposed method.
A Pragmatics-Centered Evaluation Framework for Natural Language Understanding

New models for natural language understanding have recently made an unparalleled amount of progress, which has led some researchers to suggest that the models induce universal text representations. However, current benchmarks are predominantly targeting semantic phenomena; we make the case that pragmatics needs to take center stage in the evaluation of natural language understanding. We introduce PragmEval, a new benchmark for the evaluation of natural language understanding, that unites 11 pragmatics-focused evaluation datasets for English. PragmEval can be used as supplementary training data in a multi-task learning setup, and is publicly available, alongside the code for gathering and preprocessing the datasets. Using our evaluation suite, we show that natural language inference, a widely used pretraining task, does not result in genuinely universal representations, which presents a new challenge for multi-task learning.
The inf-sup constant for Crouzeix-Raviart triangular elements of any polynomial order

In this paper, we consider the discretization of the two-dimensional stationary Stokes equation by Crouzeix-Raviart elements for the velocity of any polynomial order k ≥ 1 on conforming triangulations and discontinuous pressure approximations of order k − 1. We will bound the inf-sup constant from below independent of the mesh size and analyse its k -dependence. Our assumptions on the mesh are quite general, for odd k we require that the triangulations contain at least one inner vertex while for even k we assume that the triangulations consist of more than a single triangle.
Kernel Extreme Learning Machine Optimized by the Sparrow Search Algorithm for Hyperspectral Image Classification

To improve the classification performance and generalization ability of the hyperspectral image classification algorithm, this paper uses Multi-Scale Total Variation (MSTV) to extract the spectral features, local binary pattern (LBP) to extract spatial features, and feature superposition to obtain the fused features of hyperspectral images. A new swarm intelligence optimization method with high convergence and strong global search capability, the Sparrow Search Algorithm (SSA), is used to optimize the kernel parameters and regularization coefficients of the Kernel Extreme Learning Machine (KELM). In summary, a multiscale fusion feature hyperspectral image classification method (MLS-KELM) is proposed in this paper. The Indian Pines, Pavia University and Houston 2013 datasets were selected to validate the classification performance of MLS-KELM, and the method was applied to ZY1-02D hyperspectral data. The experimental results show that MLS-KELM has better classification performance and generalization ability compared with other popular classification methods, and MLS-KELM shows its strong robustness in the small sample case.
Dually affine Information Geometry modeled on a Banach space

In this chapter, we study Information Geometry from a particular nonparametric or functional point of view. The basic model is a probabilities subset usu-ally speciﬁed by regularity conditions. For example, probability measures mutually absolutely continuous or probability densities with a given degree of smoothness. We construct a manifold structure by giving an atlas of charts as mappings from probabilities to a Banach space. The charts we use are quite peculiar in that we consider only instances where the transition mappings are aﬃne. We chose a particular expression of the tangent and cotangent bundles in this aﬃne setting.
SAM-kNN Regressor for Online Learning in Water Distribution Networks

Water distribution networks are a key component of modern infrastructure for housing and industry. They transport and distribute water via widely branched networks from sources to consumers. In order to guarantee a working network at all times, the water supply company continuously monitors the network and takes actions when necessary – e.g. reacting to leakages, sensor faults and drops in water quality. Since real world networks are too large and complex to be monitored by a human, algorithmic monitoring systems have been developed. A popular type of such systems are residual based anomaly detection systems that can detect events such as leakages and sensor faults. For a continuous high quality monitoring, it is necessary for these systems to adapt to changed demands and presence of various anomalies. In this work, we propose an adaption of the incremental SAM-kNN classiﬁer for regression to build a residual based anomaly detection system for water distribution networks that is able to adapt to any kind of change.
Self-propelled deformable particle model for keratocyte galvanotaxis

During wound healing, fish keratocyte cells undergo galvanotaxis where they follow a woundinduced electric field. In addition to their stereotypical persistent motion, keratocytes can develop circular motion without a field or oscillate around the field direction. We developed a coarse-grained phenomenological model that captures these keratocyte behaviors. We fit this model to experimental data on keratocyte response to an electric field being turned on. A critical element of our model is a tendency for cells to turn toward their long axis, arising from a coupling between cell shape and velocity, which gives rise to oscillatory and circular motion. Galvanotaxis is influenced not only by the field-dependent responses, but also cell speed and cell shape relaxation rate. When the cell reacts to an electric field being turned on, our model predicts that stiff, slow cells react slowly but follow the signal reliably. Cells that polarize and align to the field at a faster rate react more quickly and follow the signal more reliably. When cells are exposed to a field that switches direction rapidly, cells follow the average of field directions, while if the field is switched more slowly, cells follow a “staircase” pattern.
Technical Report on Subspace Pyramid Fusion Network for Semantic Segmentation

The following is a technical report to test the validity of the proposed Subspace Pyramid Fusion Module (SPFM) to capture multi-scale feature representations, which is more useful for semantic segmentation. In this investigation, we have proposed the Efficient Shuffle Attention Module(ESAM) to reconstruct the skip-connections paths by fusing multi-level global context features. Experimental results on two well-known semantic segmentation datasets, including Camvid and Cityscapes, show the effectiveness of our proposed method.
Adaptive Merging on Phase Change Memory

Indexing is a well-known database technique used to facilitate data access and speed up query processing. Nevertheless, the construction and modification of indexes are very expensive. In traditional approaches, all records in the database table are equally covered by the index. It is not effective, since some records may be queried very often and some never. To avoid this problem, adaptive merging has been introduced. The key idea is to create an index adaptively and incrementally as a side-product of query processing. As a result, the database table is indexed partially depending on the query workload. This paper faces the problem of adaptive merging for phase change memory (PCM). The most important features of this memory type are limited write endurance and high write latency. As a consequence, adaptive merging should be investigated from the scratch. We solve this problem in two steps. First, we apply several PCM optimization techniques to the traditional adaptive merging approach. We prove that the proposed method (eAM) outperforms a traditional approach by 60%. After that, we invent the framework for adaptive merging (PAM) and propose a new variant of the PCM-optimized index. It further improves the system performance by 20% for databases where search queries interleave with data modifications.
An Initialization Scheme for Meeting Separation with Spatial Mixture Models

Spatial mixture model (SMM) supported acoustic beamforming has been extensively used for the separation of simultaneously active speakers. However, it has hardly been considered for the separation of meeting data, that are characterized by long recordings and only partially overlapping speech. In this contribution, we show that the fact that often only a single speaker is active can be utilized for a clever initialization of an SMM that employs time-varying class priors. In experiments on LibriCSS we show that the proposed initialization scheme achieves a significantly lower Word Error Rate (WER) on a downstream speech recognition task than a random initialization of the class probabilities by drawing from a Dirichlet distribution. With the only requirement that the number of speakers has to be known, we obtain a WER of 5.9 %, which is comparable to the best reported WER on this data set. Furthermore, the estimated speaker activity from the mixture model serves as a diarization based on spatial information.
Amenable covers of right‐angled Artin groups

Let AL$A_L$ be the right‐angled Artin group associated with a finite flag complex L$L$ . We show that the amenable category of AL$A_L$ equals the virtual cohomological dimension of the right‐angled Coxeter group WL$W_L$ . In particular, right‐angled Artin groups satisfy a question of Capovilla–Löh–Moraschini proposing an inequality between the amenable category and Farber's topological complexity.
Disentangling Abstraction from Statistical Pattern Matching in Human and Machine Learning

The ability to acquire abstract knowledge is a hallmark of human intelligence and is believed by many to be one of the core differences between humans and neural network models. Agents can be endowed with an inductive bias towards abstraction through meta-learning, where they are trained on a distribution of tasks that share some abstract structure that can be learned and applied. However, because neural networks are hard to interpret, it can be difficult to tell whether agents have learned the underlying abstraction, or alternatively statistical patterns that are characteristic of that abstraction. In this work, we compare the performance of humans and agents in a meta-reinforcement learning paradigm in which tasks are generated from abstract rules. We define a novel methodology for building “task metamers” that closely match the statistics of the abstract tasks but use a different underlying generative process, and evaluate performance on both abstract and metamer tasks. We find that humans perform better at abstract tasks than metamer tasks whereas common neural network architectures typically perform worse on the abstract tasks than the matched metamers. This work provides a foundation for characterizing differences between humans and machine learning that can be used in future work towards developing machines with more human-like behavior.
Investigating Possibilities and Threats in Performing an Audit of Internal Control Systems - An Empirical Study on KPMG Company

: The purpose of this study is to investigate the threats and possibilities of auditing internal control. T o achieve the purpose, data was collected using a qualitative research approach. The empirical data has been collected through face-to-face interviews with respondents from KPMG Company in Karlstad, Sweden. The study concludes that that there are weaknesses in auditing internal control and possibilities as well and author is not saying that his recommendations can completely solve these problems or weaknesses; rather he say that if they are properly applied it may go a long way to reduce such weaknesses.
Learning-based Safe Motion Control of Vehicle Ski-Stunt Maneuvers

This paper presents a safety guaranteed control method for an autonomous vehicle ski-stunt maneuver, i.e., a vehicle moving with two side wheels. To capture the vehicle dynamics precisely, a Gaussian process model is used as additional correction. We construct a probabilistic exponential control barrier function (CBF) to guarantee the planar motion safety. The CBF and the balance equilibrium manifold are enforced as the constraints into a safety critical control. Under the proposed control method, the vehicle can avoid the collision and safely maintain the balance for autonomous ski-stunt maneuvers. We conduct numerical simulation validation to demonstrate the control design. Preliminary experiments are also presented to confirm the learning-based motion control using a scaled truck for autonomous ski-stunt maneuvers.
Visual explanations for polyp detection: How medical doctors assess intrinsic versus extrinsic explanations

Deep learning has in recent years achieved immense success in all areas of computer vision and has the potential of assisting medical doctors in analyzing visual content for disease and other abnormalities. However, the current state of deep learning is very much a black box, making medical professionals highly skeptical about integrating these methods into clinical practice. Several methods have been proposed in order to shine some light onto these black boxes, but there is no consensus on the opinion of the medical doctors that will consume these explanations. This paper presents a study asking medical doctors about their opinion of current state-of-the-art explainable artificial intelligence methods when applied to a gastrointestinal disease detection use case. We compare two different categories of explanation methods, intrinsic and extrinsic, and gauge their opinion of the current value of these explanations. The results indicate that intrinsic explanations are preferred and that explanation.
Virtual extrapolation technique for retracing line of response of single scattered events in positron emission tomography

Purpose: The scattering phenomenon creates degrading effects in positron emission tomography (PET) and the corresponding events are rejected conventionally. We have proposed a mathematical model to retrace the original line of response of the single-scattered coincident events with the aim to incorporate such events in PET. Methods: We have devised a new Virtual extrapolation technique based on the concept of the probability density functions. Through which we transformed the original two-parameter list mode data for the coincident photon pairs to a one-parameter data set. The procedure of random sampling and sampling distribution – by utilizing some unique properties like a collective difference and the length compensation – was employed in the data analysis. We studied the effect of finite timing and energy resolution of detectors on the performance of the proposed model. Results: We determined the frequency of occurrence of the data values corresponding to real as well as fictitious scattering points – placed on the arc of a circle drawn with the constant scattering angle – to observe the highest counts. As expected, we found the highest counts at the location of real scattering points. The model was evaluated for a uniform attenuating phantom medium. The results were found impressive for the case of ideal time and energy information, less so for the cases of finite resolutions. Conclusions: Our work outlines a completely new approach; though, a lot more sophistication is required for its practical utility. Nonetheless, the technique seems promising in developing a potential approach to improve the sensitivity of PET imaging.
Distributed MPC with ALADIN—A Tutorial

This paper consists of a tutorial on the Augmented Lagrangian based Alternating Direction Inexact Newton method (ALADIN) and its application to distributed model predictive control (MPC). The focus is—for simplicity of presentation—on convex quadratic programming (QP) formulations of MPC. It is explained how ALADIN can be used to synthesize sparse QP solvers for large-scale linear-quadratic optimal control by combining ideas from augmented Lagrangian methods, sequential quadratic programming, as well as barrier or interior point methods. The highlight of this tutorial is a real-time ALADIN variant that can be implemented with a few lines of code yet arriving at a sparse QP solver that can compete with mature open-source and commercial QP solvers in terms of both run-time as well as numerical accuracy. It is discussed why this observation could have far reaching consequences on the future of algorithm and software development in the field of large-scale optimization and MPC.
Indoor Navigation Assistance for Visually Impaired People via Dynamic SLAM and Panoptic Segmentation with an RGB-D Sensor

Exploring an unfamiliar indoor environment and avoiding obstacles is challenging for visually impaired people. Currently, several approaches achieve the avoidance of static obstacles based on the mapping of indoor scenes. To solve the issue of distinguishing dynamic obstacles, we propose an assistive system with an RGB-D sensor to detect dynamic information of a scene. Once the system captures an image, panoptic segmentation is performed to obtain the prior dynamic object information. With sparse feature points extracted from images and the depth information, poses of the user can be estimated. After the ego-motion estimation, the dynamic object can be identified and tracked. Then, poses and speed of tracked dynamic objects can be estimated, which are passed to the users through acoustic feedback.
Generating recommendations for entity-oriented exploratory search

We introduce the task of recommendation set generation for entity-oriented exploratory search. Given an input search query which is open-ended or under-speciﬁed, the task is to present the user with an easily-understandable collection of query recommendations , with the goal of facilitating domain exploration or clarifying user intent. Traditional query recommendation systems select recommendations by identifying salient keywords in retrieved documents, or by querying an existing taxonomy or knowledge base for related concepts. In this work, we build a text-to-text model capable of generating a collection of recommendations directly, using the language model as a “soft“ knowledge base capable of proposing new concepts not found in an existing taxonomy or set of retrieved documents. We train the model to generate recommendation sets which optimize a cost function designed to encourage comprehensiveness, interestingness, and non-redundancy. In thorough evaluations performed by crowd workers, we con-ﬁrm the generalizability of our approach and the high quality of the generated recommendations.
Selective Kernel Attention for Robust Speaker Verification

Recent state-of-the-art speaker veriﬁcation architectures adopt multi-scale processing and frequency-channel attention techniques. However, their full potential may not have been exploited because these techniques’ receptive ﬁelds are ﬁxed where most convolutional layers operate with speciﬁed kernel sizes such as 1, 3 or 5. We aim to further improve this line of research by introducing a selective kernel attention (SKA) mechanism. The SKA mechanism allows each convolutional layer to adaptively select the kernel size in a data-driven fashion based on an attention mechanism that exploits both frequency and channel domain using the previous layer’s output. We pro-pose three module variants using the SKA mechanism whereby two modules are applied in front of an ECAPA-TDNN model, and the other is combined with the Res2Net backbone block. Experimental results demonstrate that our proposed model consistently outperforms the conventional counterpart on the three different evaluation protocols in terms of both equal error rate and minimum detection cost function. In addition, we present a detailed analysis that helps understand how the SKA module works.
The potential method for price-formation models

We consider the mean-field game price formation model introduced by Gomes and Saúde. In this MFG model, agents trade a commodity whose supply can be deterministic or stochastic. Agents maximize profit, taking into account current and future prices. The balance between supply and demand determines the price. We introduce a potential function that converts the MFG into a convex variational problem. This variational formulation is particularly suitable for machine learning approaches. Here, we use a recurrent neural network to solve this problem. In the last section of the paper, we compare our results with known analytical solutions.
Telescoping continued fractions for the error term in Stirling's formula

In this paper, we introduce telescoping continued fractions to find lower bounds for the error term $r_n$ in Stirling's approximation $\displaystyle n! = \sqrt{2\pi}n^{n+1/2}e^{-n}e^{r_n}. $ This improves lower bounds given earlier by Ces\`{a}ro (1922), Robbins (1955), Nanjundiah (1959), Maria (1965) and Popov (2017). The expression is in terms of a continued fraction, together with an algorithm to find successive terms of this continued fraction. The technique we introduce allows us to experimentally obtain upper and lower bounds for a sequence of convergents of a continued fraction in terms of a difference of two continued fractions.
JShelter: Give Me My Browser Back

The web is used daily by billions. Even so, users are not protected from many threats by default. This position paper builds on previous web privacy and security research and introduces JShelter, a webextension that fights to return the browser to users. Moreover, we introduce a library helping with common webextension development tasks and fixing loopholes misused by previous research. JShelter focuses on fingerprinting prevention, limitations of rich web APIs, prevention of attacks connected to timing, and learning information about the device, the browser, the user, and surrounding physical environment and location. We discovered a loophole in the sensor timestamps that lets any page observe the device boot time if sensor APIs are enabled in Chromium-based browsers. JShelter provides a fingerprinting report and other feedback that can be used by future security research and data protection authorities. Thousands of users around the world use the webextension every day.
Detection of Dangerous Events on Social Media: A Perspective Review

Social media is an essential gateway of information and communication for people worldwide. The amount of time spent and reliance of people on social media makes it a vital resource for detecting events happening in real life. Thousands of significant events are posted by users every hour in the form of multimedia. Some individuals and groups target the audience to promote their agenda among these users. Their cause can threaten other groups and individuals who do not share the same views or have specific differences. Any group with a definitive cause cannot survive without the support which acts as a catalyst for their agenda. A phenomenon occurs where people are fed information that motivates them to act on their behalf and carry out their agenda. One is benefit results in the loss of the others by putting their lives, assets, physical and emotional health in danger. This paper introduces a concept of dangerous events to approach this problem and their three main types based on their characteristics: action, scenarios, and sentiment-based dangerous events.