Miracle-dz/UniFAR-FTUs huggingface dataset:
TOWARDS COUNTERING HATE SPEECH AGAINST JOURNALISTS ON SOCIAL MEDIA

The damaging effects of hate speech on social media are evident during the last few years, and several organizations, researchers and social media platforms tried to harness them in various ways. Despite these efforts, social media users are still affected by hate speech. The problem is even more apparent to social groups that promote public discourse, such as journalists. In this work, we focus on countering hate speech that is targeted to journalistic social media accounts. To accomplish this, a group of journalists assembled a definition of hate speech, taking into account the journalistic point of view and the types of hate speech that are usually targeted against journalists. We then compile a large pool of tweets referring to journalism-related accounts in multiple languages. In order to annotate the pool of unlabeled tweets according to the definition, we follow a concise annotation strategy that involves active learning annotation stages. The outcome of this paper is a novel, publicly available collection of Twitter datasets in five different languages. Additionally, we experiment with state-of-the-art deep learning architectures for hate speech detection and use our annotated datasets to train and evaluate them. Finally, we propose an ensemble detection model that outperforms all individual models.
On the parameter learning for Perturb-and-MAP models

Probabilistic graphical models encode hidden dependencies between random variables for data modelling. Parameter estimation is a crucial and necessary part of handling such probabilistic models. These very general models have been used in plenty of fields such as computer vision, signal processing, natural language processing and many more. We mostly focused on log-supermodular models, which is a specific part of exponential family distributions, where the potential function is assumed to be the negative of a submodular function. This property will be very handy for maximum a posteriori and parameter learning estimations. Despite the apparent restriction of the models of interest, they cover a broad part of exponential families, since there are plenty of functions that are submodular, e.g., graph cuts, entropy and others. It is well known that a probabilistic treatment is challenging for most models, however we were able to tackle some of the challenges at least approximately. In this manuscript, we exploit perturb-and-MAP ideas for partition function approximation and thus efficient parameter learning. Moreover, the problem can be also interpreted as a structure learning task, where each estimated parameter or weight represents the importance of the corresponding term. We propose a way of approximating parameter estimation and inference for models where exact learning and inference is intractable in general case due to the partition function calculation complexity. The first part of the thesis is dedicated to theoretical guarantees. Given the log-supermodular models, we take advantage of the efficient minimization property related to submodularity. Introducing and comparing two existing upper bounds of the partition function, we are able to demonstrate their relation by proving a theoretical result. We introduce an approach for missing data as a natural subroutine of probabilistic modelling. It appears that we can apply a stochastic technique over the proposed perturb-and-map approximation approach and still maintain convergence while make it faster in practice. The second main contribution of this thesis is an efficient and scalable generalization of the parameter learning approach. In this section we develop new algorithms to perform parameter estimation for various loss functions, different levels of supervision and we also work on the scalability. In particular, working with mostly graph cuts, we were able to incorporate various acceleration techniques. As a third contribution we deal with the general problem of learning continuous signals. In this part, we focus on the sparse graphical models representations. We consider common sparsity-inducing regularizers as negative log-densities for the prior distribution. The proposed denoising techniques do not require choosing any precise regularizer in advance. To perform sparse representation learning, the signal processing
1200V 4H-SiC Trench Devices

The expectations for Silicon Carbide (SiC) devices in advanced power electronics applications for saving energy continue to grow. This paper presents the ROHM 3rd generation, 1200V, 4H-SiC MOSFETs and 1200V 4H-SiC ultralow forward voltage drop (VF) Schottky barrier diodes (SBDs) with trench structure. Firstly, SiC trench MOSFETs, of which mass production is in preparation to start in 2014, exhibits half of the on-resistance (Ron) of the same size, 1200 V, ROHM SiC planar MOSFETs. Secondly, the developed SiC SBDs successfully showed about 0.3V lower VF than that of ROHM conventional 2G SBDs.
Spin Matrix Theory in near 18-BPS corners of N = 4 super -

We consider limits of N = 4 super-Yang-Mills (SYM) theory that approach BPS bounds. These limits result in non-relativistic theories that describe the effective dynamics near the BPS bounds and upon quantization are known as Spin Matrix Theories. The near-BPS theories can be obtained by reducing N = 4 SYM on a three-sphere and integrating out the fields that become non-dynamical in the limits. In the previous works [1–3] we have considered various SU(1,1) and SU(1,2) types of subsectors in this limit. In the current work, we will construct the remaining Spin Matrix Theories defined near the 8 -BPS subsectors, which include the PSU(1,1|2) and SU(2|3) cases. We derive the Hamiltonians by applying the spherical reduction algorithm and show that they match with the spin chain result, coming from the loop corrections to the dilatation operator. In the PSU(1,1|2) case, we prove the positivity of the spectrum by constructing cubic supercharges using the enhanced PSU(1|1)2 symmetry and show that they close to the interacting Hamiltonian. We finally analyse the symmetry structure of the sectors in view of an interpretation of the interactions in terms of fundamental blocks. baiguera@post.bgu. ac.il, harmark@nbi.ku. dk, leiyang@ucas.ac. cn ar X iv :2 11 1. 10 14 9v 1 [ he pth ] 1 9 N ov 2 02 1
A quasi‐experimental evaluation of the impact of forensic property marking in decreasing burglaries

Property marking is a popular tool used by police agencies in burglary prevention programmes. 345 property marking kits were distributed to households in a treatment area in an English city. Changes in burglary in the treatment area were compared to three control areas. Crime type displacement to vehicle crime, criminal damage and violent crime, and changes in crime while controlling for geographic displacement were examined. Burglary decreased significantly by 82% in the treatment area in comparison to control areas during the first six months of the intervention. A significant diffusion of benefit effect to vehicle crime and criminal damage was also observed. The decreases, however, were short-lived with burglary levels returning to pre-intervention levels in the treatment area after 12 months.
Finite element calculation of double-cage rotor equivalent circuit parameters

This paper describes the use of a saturable single slot finite element model to determine the resistances and reactances of a double-cage rotor, allowing for the MMF balancing effect of the stator currents. The method is verified by comparing the impedances calculated for an unsaturated rotor with rectangular bars with their analytically determined counterparts. The model requires the user to specify both rotor frequency and current per slot. The effect of magnetic saturation is examined, and it is demonstrated that care must be taken in the choice of current at low frequencies, to avoid conditions of excessive saturation, leading to unrealistic values for inductances.
Theoretical and experimental study of nematic liquid crystal display cells using the in-plane-switching mode

In this paper a two-dimensional (2-D) dynamic model, based on a tensor formulation and solved numerically by combining finite elements and finite differences, is proposed and used for analyzing nematic liquid crystal (LC) test cells with interdigital electrodes. We compare theoretical and experimental results concerning the switching behavior, response mechanism, and viewing angle characteristics of nematic LC pixel structures which use the in-plane-switching (IPS) mode. The good agreement observed between theory and experiment in terms of electro-optical properties validates our modeling and demonstrates its potential for design optimization. We show that the proposed LC test cells, using the in-plane-switching mode, ensure switching-ON and -OFF response times of 22 and 28 ms, respectively, and excellent viewing angle characteristics.
Threshold vector field detectors

The problem of threshold or weak-signal detection in highly nonGaussian EMI is extended to vector fields, and narrow-band signals and interference. The emphasis is on a canonical formulation, illustrated by a number of specific examples. Spatial sampling with adaptive beam forming, as well as temporal sampling and all relevant vector field components, must be included for maximum processing gain. New results for a canonical theory of these vector detection cases are presented. Jointly and asymptotically locally optimum algorithms and performance measures are obtained. These results provide statistical-physical models of the EMI environment, and they include first-order probability distributions of vector EMI noise fields and received processes, with specific examples of EMI fields generated by randomly distributed electric and magnetic dipole sources, as well as more general sources. The effects of beamforming, selfdirecting beams, multiple field components, fading, and Doppler 'smear' on signal detectability are included. >
Effects of Pain From Atopic Dermatitis: Interview and Focus Group Study With Patients and Their Families

Background Pain is an underappreciated symptom of atopic dermatitis that can affect the health-related quality of life (HRQL) of patients. Objective The aim of this study is to understand the effect of pain on patients with atopic dermatitis and their family members and to recognize how this symptom affects HRQL. Methods We conducted focus groups and interviews with patients with atopic dermatitis and their family members. Researchers independently coded the transcripts and reached a consensus on the major themes. Results A total of 33 adult participants, consisting of 21 patients with atopic dermatitis (median age 47 years, range 22-77) and 12 family members (median age 50, range 22-72), attended either focus groups (23/33, 70%) or interviews (10/33, 30%), where we assessed their experiences of pain. Four themes emerged in our study. Itchiness and pain can be intertwined: pain was often caused by or otherwise associated with itchiness and could result from open sores and excoriated skin. Characteristics of pain: pain was most often described as burning. Other descriptors included mild, persistent discomfort; stinging; and stabbing. Effects of pain: pain negatively affected various aspects of daily life, including choice of clothing, sleep, social activities, and relationships. The location of painful areas could also limit physical activity, including sex. Pain management: pain from atopic dermatitis could be managed to varying degrees with different over-the-counter and prescription treatments. Systemic agents that cleared the skin also resolved the pain associated with atopic dermatitis. Conclusions Pain can be a significant factor in the HRQL of patients with atopic dermatitis and should be considered by clinicians when caring for patients with atopic dermatitis.
Environmental Justice Issues Related to Transponder Ownership and Road Pricing

Electronic transponders used on toll roads often reduce delays at toll facilities and sometimes offer customers a discount in the toll. Nonetheless, many people avoid using toll roads or acquiring electronic transponders. Their reasons include a dislike for toll roads based on a belief that roads should be a public good or on infrequent use of the roadway. Obtaining a transponder requires an application process and an initial prepayment of tolls as a minimum requirement. In worst cases, applications must be mailed (rather than obtained at a distribution center, online, or through a phone call), transponder deposits are demanded, and credit cards are required for automatic replenishment of tolls. In addition to the impediment of submitting an application, at least 20% of U.S. households do not have a credit card, and 10% do not have a bank account; such households would essentially be barred from obtaining transponders from most U.S. toll authorities. This paper analyzes these barriers by presenting the costs and benefits of the various toll transponder “tags” available in the United States in addition to models of toll road corridor users. Results show that higher-income households are more likely to have transponders and to use toll roads frequently. Other demographic and trip characteristic variables have less influence on toll road frequency choices but do influence transponder acquisition. Several agencies insist on large transponder deposits and initial prepayment amounts and require payment methods that are not available to a large percentage of the U.S. population.
Design and performance of denitrific ation bioreactors for agricultural drainage

Abstract Denitrification drainage bioreactors are emerging as an innovative practice to address water quality concerns stemming from nitrate leaching from drained agricultural lands. Although installation of these systems has begun in farms in the Midwestern United States, the understanding of their design and in-field performance remains deficient. This study utilized a set of pilot-scale drainage bioreactors to evaluate the impact of bioreactor geometry on reactor hydraulic properties and to determine nitrate removal under steady state conditions and during a simulated storm event. Bioreactors with different cross-sectional geometries but similar depths and total volumes were evaluated. Percent reduction of the influent nitrate mass was linearly correlated to the theoretical hydraulic retention time (HRT) with 30 to 70% NO 3- -N removals observed within the four to eight h of retention time suggested for field installations. Tracer tests revealed that in-situ HRTs were substantially greater (i.e. at least 1.5 times as large) than theoretical HRTs.
Brazil nuts a non-timber potential: Uncertainties and investments

The Brazil nut is one of the main non-timber forest products in Brazil, but its price fluctuations generate uncertainties and risks for both extractivists and investors. Econometric models or other simpler methods can estimate price changes and indicate the investment attractiveness of the Brazil nut. The objective of the present study was to analyze the risk-return relationship and the export price for both volatility of the Brazil nut over a 15 years period. The historical series of Brazil nut export prices, shelled and unshelled nuts, was evaluated from 2002 to 2016. The geometric growth rate and the variation coefficient indicate the return and risk respectively, associated with its price series. The price volatility of shelled and unshelled Brazil nuts was estimated with the standard deviation of the price series and with generalized models of ARCH (GARCH, EGARCH and TARCH). The shelled or unshelled Brazil nut coefficient increased over 15 years, with a low risk-return ratio. The shelled Brazil nut volatility was lower in the 2002 to 2006, 2007 to 2011 and 2012 to 2016 periods than for the unshelled nut when estimated by the standard deviation method than for the unshelled nut. The shelled Brazil nut price was higher from 2002 to 2016, with low volatility and persistent shocks. The estimate of the shelled and unshelled Brazil nut price volatility was better with the TARCH and the EGARCH models, respectively.
Design and Implementation of Power Harmonic Monitoring System based on Robust Kalman Filtering

The inverters consisted of Power Electronic Devices are widely used in dredger, which makes Dredger Power System lots of Upper Harmonic. In order to achieve precise harmonic monitoring of cutter suction dredger power system, the paper proposes a power harmonic monitoring system based on Robust Kalman Filtering (RKF). In the simulation experiments, it showed that the Robust Kalman Filtering (RKF) is superior to the Kalman Filtering (KF) because the optimal estimate value is almost in agreement with the ideal value after adding the RKF. It gives experimental set-up and algorithm principle of RKF, and explains the correctness and feasibility of Power Harmonic Monitoring System based on Robust Kalman Filtering in the theory.
ELLIPTICAL LOCAL BINARY PATTERN FOR FACIAL EXPRESSION RECOGNITION

: In real time, Facial expression recognition has always been a difficult task in the field Computer Vision and Image Analysis. Expressions are generated from very bendable features and differ in sexes, ages and cultures among people. Furthermore, varying illuminations, complicate surroundings and complex backgrounds makes the recognition more complex. The Local Binary Pattern (LBP) which acquires the local area characteristics from the images are widely for facial expression recognition. But still LBP are able to solve the few issues of the local regions such as occlusions, varying lighting conditions and so on. This study presents the local feature descriptor, Elliptical Local Binary Pattern (ELBP) for facial expression recognition. In ELBP, horizontal and vertical ellipse patterns are used to acquire micro facial features in both horizontal and vertical directions. For comparing the ELBP histograms, a template matching strategy called Chi-Square distance is suggested. The efficiency of the proposed method is evaluated on bench-marking datasets JAFFE.
Deconstructing Candy Crush: what instructional design can learn from game design

Purpose – The purpose of this paper is to explore four general design features of King Digital Entertainment’s game “Candy Crush Saga” – structural, social, cognitive, and emotional – that reflect the principles of Universal Design for Learning and discusses how these features can be applied to course design in order to motivate learner persistence and increase student success. Design/methodology/approach – Both authors are casual Candy Crush game players intrigued by how the game motivates users to continue. The methodology began with participant observation and expanded to “deconstruction” of game features and application of research findings in multiple disciplines to build the argument that game design strategies can be applied to course design to enhance learning outcomes. Findings – Many factors influence game play, but it is crucial for each level to provide increasing challenges that motivate increased mastery but do not frustrate a player to the point of quitting. Similarly, course design that pr.
RETROACTIVE ANALYSIS OF DENIAL OF SERVICE ATTACKS AS PART OF NETWORK FORENSIC

: Network forensic deals with the unauthorized invasion identification in a network by recording, capturing and analyzing the network traffic. It is a broader term used to identify and prevent different kinds of network threats and attacks. One such category of network attack is Denial of Service attack (DOS). This paper comprehensively examines the DOS attacks available in various datasets. The DOS attacks such as: Fraggle, smurf, NTP, UDP flood, WinNuke, TCP Syn flood, ICMP ﬂood etc. have been extensively examined. A brief description of tools and techniques used to capture and identify these attacks and accuracy obtained is specified analytically. The authors have also made an attempt to uncover the specifics of the tools used by attackers to launch the aforementioned DOS attacks. Various defense mechanisms have been laid down in order to guard the network against DOS attacks. A framework scenario has been proposed to identify the DOS attack from the legitimate network traffic. The proposed scenario seems to accurately identify the network intrusion to a fair extent
Interaction of triacontanol with other plant growth regulators on morphology and yield of field pea (Pisum sativum L.)

The effect of a recent plant growth regulator, triacontanol (TRIA), on plant growth and yield of Pisum sativum L. was investigated. The experiment was carried out under field conditions at the Instituto Nacional de Innovación Agraria (INIA), La Molina, Lima, Peru, using a completely randomized block design with eight treatments and three replicates. Treatments consisted in the foliar application of TRIA alone and in all possible combinations with three plant growth regulators based on auxins (AUX), gibberellins (GA), and cytokinins (CK), on pea plants cv. Rondo. The highest green pod yields were obtained with the application of TRIA+AUX+GA+CK, and TRIA+AUX+CK. The TRIA+AUX increased the values of the yield variables while TRIA+GA increased the values of the morphological variables. TRIA+CK showed a stimulating effect on morphological variables and number of grains per pod, while TRIA+AUX+CK acted synergistically on yield variables since their combined effect overweighed the effect of each growth regulator separately. Treatments with TRIA exceeded the control treatment in yield variables, indicating its great potential to be used in sustainable agriculture to guarantee food security in the future.
4D Cloud Scattering Tomography

We derive computed tomography (CT) of a time-varying volumetric scattering object, using a small number of moving cameras. We focus on passive tomography of dynamic clouds, as clouds have a major effect on the Earth’s climate. State of the art scattering CT assumes a static object. Existing 4D CT methods rely on a linear image formation model and often on significant priors. In this paper, the angular and temporal sampling rates needed for a proper recovery are discussed. Spatiotemporal CT is achieved using gradient-based optimization, which accounts for the correlation time of the dynamic object content. We demonstrate this in physics-based simulations and on experimental real-world data.
Survey on Different Search Engine for Document Element and Ranking Approaches

: Search engine are used to search different kind of information from internet. For searching academic and scientiﬁc paper, CiteseerX search engines are used.CiteseerX search engine provide free access to the document in the internet. These search engine does not provide platform for searching document element such as Algorithm, Tables, Images, Acknowledgements, Collaborators, Mathematical expressions etc. Hence it requires special purpose search engine to extract document elements from articles. The limitation of the existing search engine is a challenging task. This paper present a survey of different search engines, various methods for discovering algorithms and different ranking approach for search engines.
InterFolia: an embedded, educational application to identify plant species in the wild

This paper presents InterFolia, an iOS application dedi-cated to help the general public identify the plant species around them. The application can recognize up to 214 species of trees and shrubs found in France and western Europe. Its classiﬁcation module contains three convolutional neural network models that run locally and are each associated with a plant organ (ﬂower/fruit, leaf, or bark). A fusion module is integrated into the application to combine all available information to help improve the overall accuracy of the classiﬁcation task. The main novelty of InterFolia is its use of embedded models that allow users to explore nature beyond network-covered zones.