nils-herrmann/clean_scirepeval huggingface dataset:
A Discrete-Time Direct Torque Control for Direct-Drive PMSG-Based Wind Energy Conversion Systems

This paper proposes a novel flux-space-vector-based direct torque control (DTC) scheme for permanent-magnet synchronous generators (PMSGs) used in variable-speed direct-drive wind energy conversion systems (WECSs). The discrete-time control law, which is derived from the perspective of flux space vectors and load angle, predicts the desired stator flux vector for the next time-step with the torque and stator flux information only. The space vector modulation (SVM) is then employed to generate the reference voltage vector, leading to a fixed switching frequency, as well as lower flux and torque ripples, when compared to the conventional DTC. Compared with other SVM-based DTC methods in the literature, the proposed DTC scheme eliminates the use of proportional-integral regulators and is less dependent on machine parameters, e.g., stator inductances and permanent-magnet flux linkage, while the main advantages of the DTC, e.g., fast dynamic response and no need of coordinate transform, are preserved. The proposed DTC scheme is applicable for both nonsalient-pole and salient-pole PMSGs. The overall control scheme is simple to implement and is robust to parameter uncertainties and variations of the PMSGs. The effectiveness of the proposed discrete-time DTC scheme is verified by simulation and experimental results on a 180-W salient-pole PMSG and a 2.4-kW nonsalient-pole PMSG used in variable-speed direct-drive WECSs.
Gender identities and gender dysphoria in the Netherlands.

Several studies estimate the prevalence of gender dysphoria among adults by examining the number of individuals turning to health services. Since individuals might be hesitant to seek medical care related to gender dysphoria, these studies could underestimate the prevalence. The studies also lack information regarding the variance among different aspects of gender dysphoric conditions. Therefore, the current study estimated the prevalence by examining self-reported gender identity and dysphoria in a Dutch population sample (N = 8,064, aged 15-70 years old). Three measures assessed aspects of gender dysphoria: gender identity, dislike of the natal female/male body, and wish to obtain hormones/sex reassignment surgery. Results showed that 4.6 % of the natal men and 3.2 % of the natal women reported an ambivalent gender identity (equal identification with other sex as with sex assigned at birth) and 1.1 % of the natal men and 0.8 % of the natal women reported an incongruent gender identity (stronger identification with other sex as with sex assigned at birth). Lower percentages reported a dislike of their natal body and/or a wish for hormones/surgery. Combining these figures estimated the percentage of men reporting an ambivalent or incongruent gender identity combined with a dislike of their male body and a wish to obtain hormones/surgery at 0.6 %. For women, this was 0.2 %. These novel findings show that studies based on the number of individuals seeking medical care might underestimate the prevalence of gender dysphoria. Furthermore, the findings argue against a dichotomous approach to gender dysphoria.
Phytobioactive compound-based nanodelivery systems for the treatment of type 2 diabetes mellitus – current status

Type 2 diabetes mellitus (T2DM) is a major chronic disease that is prevalent worldwide, and it is characterized by an increase in blood glucose, disturbances in the metabolism, and alteration in insulin secretion. Nowadays, food-based therapy has become an important treatment mode for type 2 diabetes, and phytobioactive compounds have gained an increasing amount of attention to this end because they have an effect on multiple biological functions, including the sustained secretion of insulin and regeneration of pancreatic islets cells. However, the poor solubility and lower permeability of these phyto products results in a loss of bioactivity during processing and oral delivery, leading to a significant reduction in the bioavailability of phytobioactive compounds to treat T2DM. Recently, nanotechnological systems have been developed for use as various types of carrier systems to improve the delivery of bioactive compounds and thus obtain a greater bioavailability. Furthermore, carrier systems in most nanodelivery systems are highly biocompatible, with nonimmunologic behavior, a high degree of biodegradability, and greater mucoadhesive strength. Therefore, this review focuses on the various types of nanodelivery systems that can be used for phytobioactive compounds in treating T2DM with greater antidiabetic effects. There is also additional focus on improving the effects of various phytobioactive compounds through nanotechnological delivery to ensure a highly efficient treatment of type 2 diabetes.
Enhancing Machine Learning Based Malware Detection Model by Reinforcement Learning

Malware detection is getting more and more attention due to the rapid growth of new malware. As a result, machine learning (ML) has become a popular way to detect malware variants. However, machine learning models can also be cheated. Through reinforcement learning (RL), we can generate new malware samples which can bypass the detection of machine learning. In this paper, a RL model on malware generation named gym-plus is designed. Gym-plus is built based on gym-malware with some improvements. As a result, the probability of evading machine learning based static PE malware detection models is increased by 30%. Based on these newly generated samples, we retrain our detecting model to detect unknown threats. In our test, the detection accuracy of malware increased from 15.75% to 93.5%.
SNIPER: Efficient Multi-Scale Training

We present SNIPER, an algorithm for performing efficient multi-scale training in instance level visual recognition tasks. Instead of processing every pixel in an image pyramid, SNIPER processes context regions around ground-truth instances (referred to as chips) at the appropriate scale. For background sampling, these context-regions are generated using proposals extracted from a region proposal network trained with a short learning schedule. Hence, the number of chips generated per image during training adaptively changes based on the scene complexity. SNIPER only processes 30% more pixels compared to the commonly used single scale training at 800x1333 pixels on the COCO dataset. But, it also observes samples from extreme resolutions of the image pyramid, like 1400x2000 pixels. As SNIPER operates on resampled low resolution chips (512x512 pixels), it can have a batch size as large as 20 on a single GPU even with a ResNet-101 backbone. Therefore it can benefit from batch-normalization during training without the need for synchronizing batch-normalization statistics across GPUs. SNIPER brings training of instance level recognition tasks like object detection closer to the protocol for image classification and suggests that the commonly accepted guideline that it is important to train on high resolution images for instance level visual recognition tasks might not be correct. Our implementation based on Faster-RCNN with a ResNet-101 backbone obtains an mAP of 47.6% on the COCO dataset for bounding box detection and can process 5 images per second during inference with a single GPU. Code is available at https://github.com/mahyarnajibi/SNIPER/.
The Influence of Texting Language on Grammar and Executive Functions in Primary School Children

When sending text messages on their mobile phone to friends, children often use a special type of register, which is called textese. This register allows the omission of words and the use of textisms: instances of non-standard written language such as 4ever (forever). Previous studies have shown that textese has a positive effect on children's literacy abilities. In addition, it is possible that children's grammar system is affected by textese as well, as grammar rules are often transgressed in this register. Therefore, the main aim of this study was to investigate whether the use of textese influences children's grammar performance, and whether this effect is specific to grammar or language in general. Additionally, studies have not yet investigated the influence of textese on children's cognitive abilities. Consequently, the secondary aim of this study was to find out whether textese affects children's executive functions. To investigate this, 55 children between 10 and 13 years old were tested on a receptive vocabulary and grammar performance (sentence repetition) task and various tasks measuring executive functioning. In addition, text messages were elicited and the number of omissions and textisms in children's messages were calculated. Regression analyses showed that omissions were a significant predictor of children's grammar performance after various other variables were controlled for: the more words children omitted in their text messages, the better their performance on the grammar task. Although textisms correlated (marginally) significantly with vocabulary, grammar and selective attention scores and omissions marginally significantly with vocabulary scores, no other significant effects were obtained for measures of textese in the regression analyses: neither for the language outcomes, nor for the executive function tasks. Hence, our results show that textese is positively related to children's grammar performance. On the other hand, use of textese does not affect--positively nor negatively--children's executive functions.
Semantic data provisioning and reasoning for the Internet of Things

Semantic technologies could facilitate realizing features like interoperability and reasoning for Internet of Things (IoT). However, the dynamic and heterogeneous nature of IoT data, constrained resources, and real-time requirements set challenges for applying these technologies. In this paper, we study approaches for delivering semantic data from IoT nodes to distributed reasoning engines and reasoning over such data. We perform experiments to evaluate the scalability of these approaches and also study how reasoning is affected by different data aggregation strategies.
Cyber Threat Intelligence

The ever increasing number of cyber attacks requires the cyber security and forensic specialists to detect, analyze and defend against the cyber threats in almost real-time. In practice, timely dealing with such a large number of attacks is not possible without deeply perusing the attack features and taking corresponding intelligent defensive actions—this in essence defines cyber threat intelligence notion. However, such an intelligence would not be possible without the aid of artificial intelligence, machine learning and advanced data mining techniques to collect, analyse, and interpret cyber attack evidences. In this introductory chapter we first discuss the notion of cyber threat intelligence and its main challenges and opportunities, and then briefly introduce the chapters of the book which either address the identified challenges or present opportunistic solutions to provide threat intelligence.
Sequence-Aware Recommender Systems

Recommender systems are one of the most successful applications of data mining and machine-learning technology in practice. Academic research in the field is historically often based on the matrix completion problem formulation, where for each user-item-pair only one interaction (e.g., a rating) is considered. In many application domains, however, multiple user-item interactions of different types can be recorded over time. And, a number of recent works have shown that this information can be used to build richer individual user models and to discover additional behavioral patterns that can be leveraged in the recommendation process.
 In this work, we review existing works that consider information from such sequentially ordered user-item interaction logs in the recommendation process. Based on this review, we propose a categorization of the corresponding recommendation tasks and goals, summarize existing algorithmic solutions, discuss methodological approaches when benchmarking what we call sequence-aware recommender systems, and outline open challenges in the area.
Review: Existing Image Segmentation Techniques

Image segmentation is the most important part in digital image processing. Segmentation is nothing but a portion of any image and object. In image segmentation, digital image is divided into multiple set of pixels. Image segmentation is generally required to cut out region of interest (ROI) from an image. Currently there are many different algorithms available for image segmentation. Each have their own advantages and purpose. In this paper, different image segmentation algorithms with their prospects are reviewed.
Adaptive Semi-supervised Learning for Cross-domain Sentiment Classification

We consider the cross-domain sentiment classification problem, where a sentiment classifier is to be learned from a source domain and to be generalized to a target domain. Our approach explicitly minimizes the distance between the source and the target instances in an embedded feature space. With the difference between source and target minimized, we then exploit additional information from the target domain by consolidating the idea of semi-supervised learning, for which, we jointly employ two regularizations – entropy minimization and self-ensemble bootstrapping – to incorporate the unlabeled target data for classifier refinement. Our experimental results demonstrate that the proposed approach can better leverage unlabeled data from the target domain and achieve substantial improvements over baseline methods in various experimental settings.
Requirements Engineering und Management

Requirements Engineering und Management gewinnen in allen Bereichen der Systementwicklung stetig an Bedeutung. Zusammenhänge zwischen der Qualität der Anforderungserhebung und des Projekterfolges, wie von der Standish Group im jährlich erscheinenden Chaos Report [Standish 2004] untersucht, sind den meisten ein Begriff. Bei der Erhebung von Anforderungen treten immer wieder ähnliche Probleme auf. Dabei spielen unterschiedliche Faktoren und Gegebenheiten eine Rolle, die beachtet werden müssen. Es gibt mehrere Möglichkeiten, die Tücken der Analysephase zu meistern; eine Hilfe bietet der Einsatz der in diesem Artikel vorgestellten Methoden zur Anforderungserhebung. Auch wenn die Anforderungen korrekt und vollständig erhoben sind, ist es eine Kunst, diese zu verwalten. In der heutigen Zeit der verteilten Projekte ist es eine Herausforderung, die Dokumentation für jeden Beteiligten ständig verfügbar, nachvollziehbar und eindeutig zu erhalten. Requirements Management rüstet den Analytiker mit Methoden aus, um sich dieser Herausforderung zu stellen. Änderungen von Stakeholder-Wünschen an bestehenden Anforderungen stellen besondere Ansprüche an das Requirements Management, doch mithilfe eines Change-Management-Prozesses können auch diese bewältigt werden. Metriken und Traceability unterstützen bei der Aufwandsabschätzung für Änderungsanträge.
Active cyber defense with denial and deception: A cyber-wargame experiment

In January 2012, MITRE performed a real-time, red team/blue team cyber-wargame experiment. This presented the opportunity to blend cyber-warfare with traditional mission planning and execution, including denial and deception tradecraft. The cyberwargame was designed to test a dynamic network defense cyber-security platform being researched in The MITRE Corporation’s Innovation Program called Blackjack, and to investigate the utility of using denial and deception to enhance the defense of information in command and control systems. The Blackjack tool failed to deny the adversary access to real information on the command and control mission system. The adversary had compromised a number of credentials without the computer network defenders’ knowledge, and thereby observed both the real command and control mission system and the fake command and control mission system. However, traditional denial and deception techniques were effective in denying the adversary access to real information on the real command and control mission system, and instead provided the adversary with access to false information on a fake command and control mission system. a 2013 Elsevier Ltd. All rights reserved.
A Strategy for an Uncompromising Incremental Learner

Multi-class supervised learning systems require the knowledge of the entire range of labels they predict. Often when learnt incrementally, they suffer from catastrophic forgetting. To avoid this, generous leeways have to be made to the philosophy of incremental learning that either forces a part of the machine to not learn, or to retrain the machine again with a selection of the historic data. While these hacks work to various degrees, they do not adhere to the spirit of incremental learning. In this article, we redefine incremental learning with stringent conditions that do not allow for any undesirable relaxations and assumptions. We design a strategy involving generative models and the distillation of dark knowledge as a means of hallucinating data along with appropriate targets from past distributions. We call this technique, phantom sampling.We show that phantom sampling helps avoid catastrophic forgetting during incremental learning. Using an implementation based on deep neural networks, we demonstrate that phantom sampling dramatically avoids catastrophic forgetting. We apply these strategies to competitive multi-class incremental learning of deep neural networks. Using various benchmark datasets and through our strategy, we demonstrate that strict incremental learning could be achieved. We further put our strategy to test on challenging cases, including cross-domain increments and incrementing on a novel label space. We also propose a trivial extension to unbounded-continual learning and identify potential for future development.
Neural Architecture Search: A Survey

Deep Learning has enabled remarkable progress over the last years on a variety of tasks, such as image recognition, speech recognition, and machine translation. One crucial aspect for this progress are novel neural architectures. Currently employed architectures have mostly been developed manually by human experts, which is a time-consuming and error-prone process. Because of this, there is growing interest in automated neural architecture search methods. We provide an overview of existing work in this field of research and categorize them according to three dimensions: search space, search strategy, and performance estima-
Identifying and Tracking Sentiments and Topics from Social Media Texts during Natural Disasters

We study the problem of identifying the topics and sentiments and tracking their shifts from social media texts in different geographical regions during emergencies and disasters. We propose a location-based dynamic sentiment-topic model (LDST) which can jointly model topic, sentiment, time and Geolocation information. The experimental results demonstrate that LDST performs very well at discovering topics and sentiments from social media and tracking their shifts in different geographical regions during emergencies and disasters1.
Accuracy and Security Evaluation of Multi-Factor Biometric Authentication

This paper aims to evaluate the security and accuracy of Multi-Factor Biometric Authentication (MFBA) schemes that are based on applying UserBased Transformations (UBTs) on biometric features. Typically, UBTs employ transformation keys generated from passwords/PINs or retrieved from tokens. In this paper, we not only highlight the importance of simulating the scenario of compromised transformation keys rigorously, but also show that there has been misevaluation of this scenario as the results can be easily misinterpreted. In particular, we expose the falsehood of the widely reported claim in the literature that in the case of stolen keys, authentication accuracy drops but remains close to the authentication accuracy of biometric only system. We show that MFBA systems setup to operate at zero (%) Equal Error Rates (EER) can be undermined in the event of keys being compromised where the False Acceptance Rate reaches unacceptable levels. We demonstrate that for commonly used recognition schemes the FAR could be as high as 21%, 56%, and 66% for iris, fingerprint, and face biometrics respectively when using stolen transformation keys compared to near zero (%) EER when keys are assumed secure. We also discuss the trade off between improving accuracy of biometric systems using additional authentication factor(s) and compromising the security when the additional factor(s) are compromised. Finally, we propose mechanisms to enhance the security as well as the accuracy of MFBA schemes.
Improving data transparency in clinical trials using blockchain smart contracts

The scientific credibility of findings from clinical trials can be undermined by a range of problems including missing data, endpoint switching, data dredging, and selective publication. Together, these issues have contributed to systematically distorted perceptions regarding the benefits and risks of treatments. While these issues have been well documented and widely discussed within the profession, legislative intervention has seen limited success. Recently, a method was described for using a blockchain to prove the existence of documents describing pre-specified endpoints in clinical trials. Here, we extend the idea by using smart contracts - code, and data, that resides at a specific address in a blockchain, and whose execution is cryptographically validated by the network - to demonstrate how trust in clinical trials can be enforced and data manipulation eliminated. We show that blockchain smart contracts provide a novel technological solution to the data manipulation problem, by acting as trusted administrators and providing an immutable record of trial history.
The necessities and luxuries of mate preferences: testing the tradeoffs.

Social exchange and evolutionary models of mate selection incorporate economic assumptions but have not considered a key distinction between necessities and luxuries. This distinction can clarify an apparent paradox: Status and attractiveness, though emphasized by many researchers, are not typically rated highly by research participants. Three studies supported the hypothesis that women and men first ensure sufficient levels of necessities in potential mates before considering many other characteristics rated as more important in prior surveys. In Studies 1 and 2, participants designed ideal long-term mates, purchasing various characteristics with 3 different budgets. Study 3 used a mate-screening paradigm and showed that people inquire 1st about hypothesized necessities. Physical attractiveness was a necessity to men, status and resources were necessities to women, and kindness and intelligence were necessities to both.