nils-herrmann/clean_scirepeval huggingface dataset:
Math modeling, neuropsychology, and category learning:: Response to B. Knowlton (1999)

raising the issue of the implications of findings in neuropsychological subjects for category learning [Knowlton, B. (1999) What can neuropsychology tell us about category learning? Trends Cognit. Sci. 3, 123–124]1. Knowlton and Squire2,3 reported various dissociations between categorization and recognition in normal and amnesic subjects to support their view that multiple memory systems mediate these tasks. The multiple-system view is more complex than a single-system approach is to modeling categorization and recognition. On grounds of parsimony, a question of scientific interest is whether a single-system model can suffice to account for these data. In support of this possibility, we demonstrated that an extremely simple version of a single-system exemplar model accounted in quantitative detail for the bulk of the dissociation findings reported by Knowlton and Squire, as long as plausible assumptions were made regarding parameter differences in memory ability between groups4. At the same time, we acknowledged some remaining challenges for the exemplar model, and concluded our article by stating, ‘Whether or not a full account of Knowlton and Squire’s complete set of dissociation findings is available within the exemplar approach remains a subject for future research, but we feel that we have made an important start’ (Ref. 4, p. 255). We also outlined various directions in which the exemplar model could be extended to meet these challenges. In the first part of her commentary1, Knowlton reiterates these acknowledged limitations of the simple exemplar model and argues against the possible lines of extension that we suggested. Her first point pertains to the dissociation exhibited by the severe amnesic E.P., who despite showing zero ability to discriminate between old and new exemplars in a recognition task, displayed normal performance in a classification task5. We had pointed out that stimulus conditions were dramatically different across the particular recognition and classification tasks in question. In the test phase of the recognition task, presentations of the target item were rare and widely spaced between distractor items. By contrast, in the classification test, members of the target category were frequently presented. We noted that if E.P. had even a small residual N o s o f s k y a n d Z a k i – R e s p o n s e Update Comment
Evidence for perceptual deficits in associative visual (prosop)agnosia: a single-case study

Associative visual agnosia is classically defined as normal visual perception stripped of its meaning [Archiv für Psychiatrie und Nervenkrankheiten 21 (1890) 22/English translation: Cognitive Neuropsychol. 5 (1988) 155]: these patients cannot access to their stored visual memories to categorize the objects nonetheless perceived correctly. However, according to an influential theory of visual agnosia [Farah, Visual Agnosia: Disorders of Object Recognition and What They Tell Us about Normal Vision, MIT Press, Cambridge, MA, 1990], visual associative agnosics necessarily present perceptual deficits that are the cause of their impairment at object recognition Here we report a detailed investigation of a patient with bilateral occipito-temporal lesions strongly impaired at object and face recognition. NS presents normal drawing copy, and normal performance at object and face matching tasks as used in classical neuropsychological tests. However, when tested with several computer tasks using carefully controlled visual stimuli and taking both his accuracy rate and response times into account, NS was found to have abnormal performances at high-level visual processing of objects and faces. Albeit presenting a different pattern of deficits than previously described in integrative agnosic patients such as HJA and LH, his deficits were characterized by an inability to integrate individual parts into a whole percept, as suggested by his failure at processing structurally impossible three-dimensional (3D) objects, an absence of face inversion effects and an advantage at detecting and matching single parts. Taken together, these observations question the idea of separate visual representations for object/face perception and object/face knowledge derived from investigations of visual associative (prosop)agnosia, and they raise some methodological issues in the analysis of single-case studies of (prosop)agnosic patients.
Balancing customer and network value in business models for mobile services

Designing business models for mobile services is complex. A business model can be seen as a blueprint of four interrelated components: service offering, technical architecture, and organisational and financial arrangements. In this paper the connections among these components are explored by analysing the critical design issues in business models for mobile services, e.g., targeting and branding in the service domain, security and quality of service in the technology domain, network governance in the organisation domain, and revenue sharing in the finance domain. A causal framework is developed linking these critical design issues to expected customer value and expected network value, and hence, to business model viability.
Crawler Mechanism with Circular Section to Realize a Sideling Motion

In this paper, a novel crawler mechanism for a sideling motion is presented. The crawler mechanism has a circular section and has active rolling axes at the center of the circles. Conventional crawler mechanism can convey heavy weigh, however they can not move to side. And previous crawler unit sinks into the soft ground when it whole body is inclined during moving. The proposed design solves this drawback by means of a circular section. An actual prototype model has been developed to illustrate the concept and to perform preliminary motion experiments, through which the basic performance of the Crawler Vehicle with this proposed Omni-Crawler mechanism was confirmed. An prototype has been developed to illustrate the concept. Motion experiments, with a test vehicle are also presented.
An overview of reservoir computing: theory, applications and implementations

Training recurrent neural networks is hard. Recently it has however been discovered that it is possible to just construct a random recurrent topology, and only train a single linear readout layer. State-ofthe-art performance can easily be achieved with this setup, called Reservoir Computing. The idea can even be broadened by stating that any high dimensional, driven dynamic system, operated in the correct dynamic regime can be used as a temporal ‘kernel’ which makes it possible to solve complex tasks using just linear post-processing techniques. This tutorial will give an overview of current research on theory, application and implementations of Reservoir Computing.
ARCHITECTURE-BASED DEPENDABILITY ANALYSIS APPROACHES

Software architecture plays an increasingly important role in the dependability of today’s large and complex software systems. Formal and informal architectural models embody critical design decisions about systems’ functional and nonfunctional properties. Traditionally, a wide range of software architecture modeling and analysis approaches focused on validating systems’ functionality. More recently, however, many approaches have been developed to predict and estimate dependability of complex software systems based on the system’s software architecture. In this paper, we present a novel classification of architecture-centric dependability modeling and analysis approaches along with a discussion of the specific underlying methodologies and their suitability to assess different dependability attributes. Our classification and discussion can be used to identify new areas of software architecture and dependability research and to identify compatible formalisms that can be used to perform tradeoff analysis among dependability attributes.
Improved Power Quality Bridgeless Converter-Based SMPS for Arc Welding

This paper proposes a power-factor-corrected bridgeless (BL) switched-mode power supply (SMPS) for welding applications that possesses output voltage control and current limiting feature even during extreme overloading conditions at the output terminals. Eliminating an input diode bridge rectifier minimizes conduction losses and improves thermal utilization of semiconductor devices. The front-end of the proposed SMPS consists of a BL boost converter operating in a continuous conduction mode to attain unity power factor, while at the rear end a pulse width modulation isolated full bridge dc-dc converter is used to regulate the output voltage. The design and implementation of this BL arc welding power supply (AWPS) is presented showing its fast dynamic response to supply voltage and load variations. The performance of the proposed AWPS is examined in terms of power factor, total harmonic distortion of the supply current, efficiency, and output current limit over a wide range of line/load variations. Test results confirm the effectiveness of the proposed AWPS in maintaining an impeccable power quality at utility interface apart from achieving an excellent output voltage regulation and current limiting capability.
Electricity Theft Detection Techniques for Distribution System in

Electricity consumer dishonesty is a problem faced by all power utilities. Finding efficient measurements for detecting fraudulent electricity consumption has been an active research area in recent years. This paper presents a new approach towards Distribution Power Loss analysis for electric utilities using a novel intelligencebased techniques like Extreme Learning Machine (ELM), OS-ELM & Support Vector Machine (SVM). The main motivation of this study is to assist Gujarat Urjha Vikas Nigam LTD (GUVNL) to reduce its Distribution Power Loss due to electricity theft. The proposed model preselects suspected customers to be inspected onsite for fraud based on irregularities and abnormal consumption behaviour. This approach provides a method of data mining and involves feature extraction from historical customer consumption data. The approach uses customer load profile information to expose abnormal behaviour that is known to be highly correlated with Power Loss activities. The result yields classification classes that are used to shortlist potential fraud suspects for onsite inspection, based on significant behaviour that emerges due to irregularities in consumption. Simulation results prove the proposed method is more effective compared to the current actions taken by GUVNL in order to reduce Power Loss activities.
Relationship Between Awareness, Knowledge and Attitudes Towards Environmental Education Among Secondary School Students in Malaysia

The importance of environmental education (EE) is well known globally among societies. Environmental education is gradually promoted as a sustainable tool in protection of the environment Environmental education is found across school curriculums in Malaysia. The objectives of the curriculum are environmental attitude, knowledge and awareness (AKA) where has been investigated in the current study. The study was conducted to identify the relationship between environmental awareness, knowledge and attitude among secondary school students. The survey was conducted on 470 respondents who were in Form Four (16 years old) in Kajang city, Selangor, Malaysia. An instrument which included (48 questions) was employed to investigate the relationship between awareness, knowledge and attitude. The results of Person Correlation showed a significant but weak relationship between awareness and knowledge on environmental issues while there was high relationship observed between awareness and attitudes among respondents. Moreover, the statistical test showed a negligible relationship between knowledge and attitude among students about environment. The study concluded that a high level of awareness and knowledge plus positive attitude of students may come have been achieved from the families of respondents, teachers, media, private reading and school curriculums regarding the environment that increases the environmental view among students as well as overall in the society. The study recommended that environmental education subject necessarily might be considered as an independent syllabus in Malaysian education system.
Bot Classification for Real-Life Highly Class-Imbalanced Dataset

Botnets are networks formed with a number of machines infected by malware called bots. Detection of these malicious networks is becoming a major concern as they pose a serious threat to the network security. Most of the research on bot detection is based on particular botnet characteristics which fail to detect other types of botnets and bots. Furthermore, there are very few bot detection methods that considered real-life class-imbalanced dataset. A dataset is class-imbalanced if there are significantly more instances in one class than the other classes. In this paper, we develop three generic features to detect different types of bots regardless of their botnet characteristics. We develop five classification models based on those features to classify bots from a large, real-life, class-imbalanced network dataset. Results show that our methodology can detect bots more accurately than the existing methods. Experimental results also demonstrate that the developed methodology can successfully detect bots when the proportion of bots to normal activity is very small. We also provide a performance comparison of our methodology with a recent study on bot detection in a real-life, large, imbalanced dataset.
Management and orchestration challenges in network functions virtualization

NFV continues to draw immense attention from researchers in both industry and academia. By decoupling NFs from the physical equipment on which they run, NFV promises to reduce CAPEX and OPEX, make networks more scalable and flexible, and lead to increased service agility. However, despite the unprecedented interest it has gained, there are still obstacles that must be overcome before NFV can advance to reality in industrial deployments, let alone delivering on the anticipated gains. While doing so, important challenges associated with network and function MANO need to be addressed. In this article, we introduce NFV and give an overview of the MANO framework that has been proposed by ETSI. We then present representative projects and vendor products that focus on MANO, and discuss their features and relationship with the framework. Finally, we identify open MANO challenges as well as opportunities for future research.
A secure triple level encryption method using cryptography and steganography

Cryptography is the practice for secure communication in the presence of third parties. Steganography is technique of writing hidden messages such that except the sender and receiver, no one even suspects the existence of the message. In today's hi-tech age, threats from intruders are very great such that usage of either of the above techniques separately may not be able to provide the intended protection. In order to increase the level of protection, both the techniques may be used in a combined manner. Multimedia techniques can also be used to hide the data. In this paper, we propose an encrypting system which combines techniques of cryptography and steganography with data hiding. Instead of using a single level of data encryption, the message is encrypted twice. Traditional techniques have been used for this purpose. Then the cipher is hidden inside the image in encrypted format for further use. It uses a reference matrix for selection of passwords depending on the properties of the image. The image with the hidden data is used for further purposes.
Monte Carlo Strength Evaluation: Fast and Reliable Password Checking

Modern password guessing attacks adopt sophisticated probabilistic techniques that allow for orders of magnitude less guesses to succeed compared to brute force. Unfortunately, best practices and password strength evaluators failed to keep up: they are generally based on heuristic rules designed to defend against obsolete brute force attacks. Many passwords can only be guessed with significant effort, and motivated attackers may be willing to invest resources to obtain valuable passwords. However, it is eminently impractical for the defender to simulate expensive attacks against each user to accurately characterize their password strength. This paper proposes a novel method to estimate the number of guesses needed to find a password using modern attacks. The proposed method requires little resources, applies to a wide set of probabilistic models, and is characterised by highly desirable convergence properties.
 The experiments demonstrate the scalability and generality of the proposal. In particular, the experimental analysis reports evaluations on a wide range of password strengths, and of state-of-the-art attacks on very large datasets, including attacks that would have been prohibitively expensive to handle with existing simulation-based approaches.
Current review of prepubertal vaginal bleeding.

Prepubertal vaginal bleeding raises many concerns and evaluation and diagnosis may prove difficult for many providers. We aim to provide a comprehensive review and recent updates for those practitioners who care for these patients.



Prompt management in the case of prepubertal vaginal bleeding is indicated, especially to rule out malignancy or abuse. If a child is reluctant to undergo examination, or if the extent of injury or source of bleeding cannot be determined, examination under anesthesia and vaginoscopy is recommended. Use of vaginoscopy allows for clear visualization of the vagina and cervix without distorting hymenal anatomy, as well as diagnosis and removal of a foreign body and evaluation of mucosal damage caused. In the case of sexual abuse, providers specifically trained in pediatrics need to be present, and safety of the patient should always be ensured.



Careful history taking and targeted examination may lead to diagnosis in the case of prepubertal vaginal bleeding. However, in more difficult cases, practitioners should not hesitate to examine a patient in the operating room using general anesthesia to elicit the cause. Although sexual abuse and malignancy are always on the differential, most causes of bleeding are benign and easily treated.
Time series forecasting of cyber attack intensity

Cyber attacks occur on a near daily basis and are becoming exponentially more common. While some research aims to detect the characteristics of an attack, little focus has been given to patterns of attacks in general. This paper aims to exploit temporal correlations between the number of attacks per day in order to predict future intensity of cyber incidents. Through analysis of attack data collected from Hackmageddon, correlation was found among reported attack volume in consecutive days. This paper presents a forecasting system that aims to predict the number of cyber attacks on a given day based only on a set of historical attack count data. Our system conducts ARIMA time series forecasting on all previously collected incidents to predict the expected number of attacks on a future date. Our tool is able to use only a subset of data relevant to a specific attack method. Prediction models are dynamically updated over time as new data is collected to improve accuracy. Our system outperforms naive forecasting methods by 14.1% when predicting attacks of any type, and up to 21.2% when forecasting attacks of a specific type. Our system also produces a model which more accurately predicts future cyber attack intensity behavior.
Security Governance: Its Impact on Security Culture

While there is an overwhelming amount of literature that recognises the need for organisations to create a security culture in order to effectively manage security, little is known about how to create a good security culture or even what constitutes a good security culture. In this paper, we report on one of two case studies performed to examine how security governance influences security culture and in particular, the sense of responsibility and ownership of security. The results indicate that although the structural and functional mechanisms in security governance are influencing factors, it is the extent of social participation that may be the major contributing component in security governance that influences the levels of responsibility and sense of ownership that IT security personnel have over the management of security within an organisation.
Adaptive Decentralized Droop Controller to Preserve Power Sharing Stability of Paralleled Inverters in Distributed Generation Microgrids

This paper addresses the low-frequency relative stability problem in paralleled inverter-based distributed generation (DG) units in microgrids. In the sense of the small-signal dynamics of a microgrid, it can be shown that as the demanded power of each inverter changes, the low-frequency modes of the power sharing dynamics drift to new locations and the relative stability is remarkably affected, and eventually, instability can be yielded. To preserve the power sharing stability, an adaptive decentralized droop controller of paralleled inverter-based DG units is presented in this paper. The proposed power sharing strategy is based on the static droop characteristics combined with an adaptive transient droop function. Unlike conventional droop controllers, which yield 1-DOF tunable controller, the proposed droop controller yields 2-DOF tunable controller. Subsequently, the dynamic performance of the power sharing mechanism can be adjusted, without affecting the static droop gain, to damp the oscillatory modes of the power sharing controller. To account for the power modes immigration at different loading conditions, the transient droop gains are adaptively scheduled via small-signal analysis of the power sharing mechanism along the loading trajectory of each DG unit to yield the desired transient and steady-state response. The gain adaptation scheme utilizes the filtered active and reactive powers as indices; therefore, a stable and smooth power injection performance can be obtained at different loading conditions. The adaptive nature of the proposed controller ensures active damping of power oscillations at different operating conditions, and yields a stable and robust performance of the paralleled inverter system.
Gender Identity and Autism Spectrum Disorders


In this review, we briefly summarize much of the existing literature on gender-related concerns and autism spectrum disorders (ASD), drawing attention to critical shortcomings in our current understanding and potential clinical implications. Some authors have concluded that gender identity disorder (GID), or gender dysphoria (GD), is more common in individuals with ASD, providing a range of potential explanations. However, existing literature is quantitatively limited, and our capacity to draw conclusions is further complicated by conceptual challenges regarding how gender identity is best understood. Discourses that emphasize gender as a component of identity formation are gaining prominence and seem particularly salient when applied to ASD. Individuals with ASD should enjoy equal rights with regard to treatment for gender dysphoria. Clinicians may be able to assist individuals in understanding this aspect of their identity by broadening the social frame and facilitating an exploration of gender roles.