nils-herrmann/clean_scirepeval huggingface dataset:
Effect of pencil grasp on the speed and legibility of handwriting in children.

Pencil grasps other than the dynamic tripod may be functional for handwriting. This study examined the impact of grasp on handwriting speed and legibility.



We videotaped 120 typically developing fourth-grade students while they performed a writing task. We categorized the grasps they used and evaluated their writing for speed and legibility using a handwriting assessment. Using linear regression analysis, we examined the relationship between grasp and handwriting.



We documented six categories of pencil grasp: four mature grasp patterns, one immature grasp pattern, and one alternating grasp pattern. Multiple linear regression results revealed no significant effect for mature grasp on either legibility or speed.



Pencil grasp patterns did not influence handwriting speed or legibility in this sample of typically developing children. This finding adds to the mounting body of evidence that alternative grasps may be acceptable for fast and legible handwriting.
A CPW-fed bow-tie slot antenna with tuning stub

In this paper, a modified slot bow-tie antenna fed by a coplanar waveguide (CPW) is investigated for wideband operation. It is designed to work on a thin substrate (h = 0.787 mm) with a low dielectric constant (epsivr = 2.2) operating at two frequencies (1.8 and 2.4 GHz). Two metal stubs are introduced in the middle of the bow-tie slot in order to achieve a wider impedance bandwidth over the conventional CPW-fed bow-tie slot antenna. The antenna is fabricated and tested in the antenna measurement facility of UCL and experimental far-field results are presented showing low cross-polarisation levels. The bow-tie slot antenna can obtain at least 55% bandwidth.
TECHNOLOGY ADOPTION IN THE BIM IMPLEMENTATION FOR LEAN ARCHITECTURAL PRACTICE

Justification for Research: the construction companies are facing barriers and challenges in BIM adoption as there is no clear guidance or best practice studies from which they can learn and build up their capacity for BIM use in order to increase productivity, efficiency, quality, and to attain competitive advantages in the global market and to achieve the targets in environmental sustainability.  this paper aims to explain a comprehensive and systemic evaluation and assessment of the relevant BIM technologies as part of the BIM adoption and implementation to demonstrate how efficiency gains have been achieved towards a lean architectural practice.  The research is undertaken through a KTP (Knowledge transfer Partnership) project between the University of Salford and the John McCall Architects based in Liverpool, which is an SME (Small Medium Enterprise). The overall aim of KTP is to develop Lean Design Practice through the BIM adoption and implementation. The overall BIM implementation approach uses a socio-technical view in which it does not only consider the implementation of technology but also considers the socio-cultural environment that provides the context for its implementation. The technology adoption methodology within the BIM implementation approach is the action research oriented qualitative and quantitative research for discovery, comparison, and experimentation as the KTP project with JMA provides an environment for “learning by doing”  research has proved that BIM technology adoption should be undertaken with a bottom-up approach rather than top-down approach for successful change management and dealing with the resistance to change. As a result of the BIM technology adoption, efficiency gains are achieved through the piloting projects and the design process is improved through the elimination of wastes and value generation.  successful BIM adoption needs an implementation strategy. However, at operational level, it is imperative that professional guidelines are required as part of the implementation strategy. This paper introduces a systematic approach for BIM technology adoption based on a case study implementation and it demonstrates a guideline at operational level for other SME companies of architectural practices.
The new era of the lymphatic system: no longer secondary to the blood vascular system.

The blood and lymphatic systems are the two major circulatory systems in our body. Although the blood system has been studied extensively, the lymphatic system has received much less scientific and medical attention because of its elusive morphology and mysterious pathophysiology. However, a series of landmark discoveries made in the past decade has begun to change the previous misconception of the lymphatic system to be secondary to the more essential blood vascular system. In this article, we review the current understanding of the development and pathology of the lymphatic system. We hope to convince readers that the lymphatic system is no less essential than the blood circulatory system for human health and well-being.
DeepDGA: Adversarially-Tuned Domain Generation and Detection

Many malware families utilize domain generation algorithms (DGAs) to establish command and control (C&C) connections. While there are many methods to pseudorandomly generate domains, we focus in this paper on detecting (and generating) domains on a per-domain basis which provides a simple and flexible means to detect known DGA families. Recent machine learning approaches to DGA detection have been successful on fairly simplistic DGAs, many of which produce names of fixed length. However, models trained on limited datasets are somewhat blind to new DGA variants. In this paper, we leverage the concept of generative adversarial networks to construct a deep learning based DGA that is designed to intentionally bypass a deep learning based detector. In a series of adversarial rounds, the generator learns to generate domain names that are increasingly more difficult to detect. In turn, a detector model updates its parameters to compensate for the adversarially generated domains. We test the hypothesis of whether adversarially generated domains may be used to augment training sets in order to harden other machine learning models against yet-to-be-observed DGAs. We detail solutions to several challenges in training this character-based generative adversarial network. In particular, our deep learning architecture begins as a domain name auto-encoder (encoder + decoder) trained on domains in the Alexa one million. Then the encoder and decoder are reassembled competitively in a generative adversarial network (detector + generator), with novel neural architectures and training strategies to improve convergence.
Comparing passwords, tokens, and biometrics for user authentication

Before the computer age, authenticating a user was a relatively simple process. One person could authenticate another by visual recognition, interpersonal communication, or, more formally, mutually agreed upon authentication methods. With the onset of the computer age, authentication has become more complicated. Face-to-face visual authentication has largely dissipated, with computers and networks intervening. Sensitive information is exchanged daily between humans and computers, and from computer to computer. This complexity demands more formal protection methods; in short, authentication processes to manage our routine interactions with such machines and networks. Authentication is the process of positively verifying identity, be it that of a user, device, or entity in a computer system. Often authentication is the prerequisite to accessing system resources. Positive verification is accomplished by means of matching some indicator of identity, such as a shared secret prearranged at the time a person was authorized to use the system. The most familiar user authenticator in use today is the password. The secure sockets layer (SSL) is an example of machine to machine authentication. Human–machine authentication is known as user authentication and it consists of verifying the identity of a user: is this person really who she claims to be? User authentication is much less secure than machine authentication and is known as the Achilles’ heel of secure systems. This paper introduces various human authenticators and compares them based on security, convenience, and cost. The discussion is set in the context of a larger analysis of security issues, namely, measuring a system’s vulnerability to attack. The focus is kept on remote computer authentication. Authenticators can be categorized into three main types: secrets (what you know), tokens (what you have), and IDs (who you are). A password is a secret word, phrase, or personal identification number. Although passwords are ubiquitously used, they pose vulnerabilities, the biggest being that a short mnemonic password can be guessed or searched by an ambitious attacker, while a longer, random password is difficult for a person to remember. A token is a physical device used to aid authentication. Examples include bank cards and smart cards. A token can be an active device that yields one-time passcodes (time-synchronous or
Efficient Learning of Harmonic Priors for Pitch Detection in Polyphonic Music

Automatic music transcription (AMT) aims to infer a latent symbolic representation of a piece of music (piano-roll), given a corresponding observed audio recording. Transcribing polyphonic music (when multiple notes are played simultaneously) is a challenging problem, due to highly structured overlapping between harmonics. We study whether the introduction of physically inspired Gaussian process (GP) priors into audio content analysis models improves the extraction of patterns required for AMT. Audio signals are described as a linear combination of sources. Each source is decomposed into the product of an amplitude-envelope, and a quasi-periodic component process. We introduce the Matérn spectral mixture (MSM) kernel for describing frequency content of singles notes. We consider two different regression approaches. In the sigmoid model every pitch-activation is independently non-linear transformed. In the softmax model several activation GPs are jointly non-linearly transformed. This introduce crosscorrelation between activations. We use variational Bayes for approximate inference. We empirically evaluate how these models work in practice transcribing polyphonic music. We demonstrate that rather than encourage dependency between activations, what is relevant for improving pitch detection is to learnt priors that fit the frequency content of the sound events to detect. Python code complementing this paper is available at https://github.com/PabloAlvarado/MSMK.
Secure in-VM monitoring using hardware virtualization

Kernel-level attacks or rootkits can compromise the security of an operating system by executing with the privilege of the kernel. Current approaches use virtualization to gain higher privilege over these attacks, and isolate security tools from the untrusted guest VM by moving them out and placing them in a separate trusted VM. Although out-of-VM isolation can help ensure security, the added overhead of world-switches between the guest VMs for each invocation of the monitor makes this approach unsuitable for many applications, especially fine-grained monitoring. In this paper, we present Secure In-VM Monitoring (SIM), a general-purpose framework that enables security monitoring applications to be placed back in the untrusted guest VM for efficiency without sacrificing the security guarantees provided by running them outside of the VM. We utilize contemporary hardware memory protection and hardware virtualization features available in recent processors to create a hypervisor protected address space where a monitor can execute and access data in native speeds and to which execution is transferred in a controlled manner that does not require hypervisor involvement. We have developed a prototype into KVM utilizing Intel VT hardware virtualization technology. We have also developed two representative applications for the Windows OS that monitor system calls and process creations. Our microbenchmarks show at least 10 times performance improvement in invocation of a monitor inside SIM over a monitor residing in another trusted VM. With a systematic security analysis of SIM against a number of possible threats, we show that SIM provides at least the same security guarantees as what can be achieved by out-of-VM monitors.
AnalyticalInk: An Interactive Learning Environment for Math Word Problem Solving

We present AnalyticalInk, a novel math learning environment prototype that uses a semantic graph as the knowledge representation of algebraic and geometric word problems. The system solves math problems by reasoning upon the semantic graph and automatically generates conceptual and procedural scaffoldings in sequence. We further introduces a step-wise tutoring framework, which can check students' input steps and provide the adaptive scaffolding feedback. Based on the knowledge representation, AnalyticalInk highlights keywords that allow users to further drag them onto the workspace to gather insight into the problem's initial conditions. The system simulates a pen-and-paper environment to let users input both in algebraic and geometric workspaces. We conducted an usability evaluation to measure the effectiveness of AnalyticalInk. We found that keyword highlighting and dragging is useful and effective toward math problem solving. Answer checking in the tutoring component is useful. In general, our prototype shows the promise in helping users to understand geometrical concepts and master algebraic procedures under the problem solving.
Convergent Learning: Do different neural networks learn the same representations?

Recent successes in training large, deep neural networks have prompted active investigation into the representations learned on their intermediate layers. Such research is difficult because it requires making sense of non-linear computations performed by millions of learned parameters, but valuable because it increases our ability to understand current models and training algorithms and thus create improved versions of them. In this paper we investigate the extent to which neural networks exhibit what we call convergent learning, which is when the representations learned by multiple nets converge to a set of features which are either individually similar between networks or where subsets of features span similar lowdimensional spaces. We propose a specific method of probing representations: training multiple networks and then comparing and contrasting their individual, learned representations at the level of neurons or groups of neurons. We begin research into this question by introducing three techniques to approximately align different neural networks on a feature or subspace level: a bipartite matching approach that makes one-to-one assignments between neurons, a sparse prediction and clustering approach that finds one-to-many mappings, and a spectral clustering approach that finds many-to-many mappings. This initial investigation reveals a few interesting, previously unknown properties of neural networks, and we argue that future research into the question of convergent learning will yield many more. The insights described here include (1) that some features are learned reliably in multiple networks, yet other features are not consistently learned; (2) that units learn to span low-dimensional subspaces and, while these subspaces are common to multiple networks, the specific basis vectors learned are not; (3) that the representation codes are a mix between a local (single unit) code and slightly, but not fully, distributed codes across multiple units; (4) that the average activation values of neurons vary considerably within a network, yet the mean activation values across different networks converge to an almost identical distribution.
A feasible method to combat against DDoS attack in SDN network

In Software Defined Network, the controller is so vulnerable to flooding attack. By injecting spoofed request packets continuously, attackers make a burdensome process to the controller, cause bandwidth occupation in the controller-switch channel, and overload the flow table in switch. The final target of attackers is to downgrade or even shutdown the stability and quality of service of the network. In this paper, we introduce a feasible method to protect the network against Distributed Denial of Service attacks more effectively.
Liquid-claim production , risk management , and bank capital structure : Why high leverage is optimal for banks

Liquidity production is a central function of banks. High leverage is optimal for banks in a model that has just enough frictions for banks to have a meaningful role in liquid-claim production. The model has a market premium for (socially valuable) safe/liquid debt, but no taxes or other traditional motives to lever up. Because only safe debt commands a liquidity premium, banks with risky assets use risk management to maximize their capacity to include such debt in the capital structure. The model can explain why banks have higher leverage than most operating firms, why risk management is central to banks' operating policies, why bank leverage increased over the last 150 years or so, and why leverage limits for regulated banks impede their ability to compete with unregulated shadow banks. & 2014
Rapid assessment of disaster damage using social media activity

Could social media data aid in disaster response and damage assessment? Countries face both an increasing frequency and an increasing intensity of natural disasters resulting from climate change. During such events, citizens turn to social media platforms for disaster-related communication and information. Social media improves situational awareness, facilitates dissemination of emergency information, enables early warning systems, and helps coordinate relief efforts. In addition, the spatiotemporal distribution of disaster-related messages helps with the real-time monitoring and assessment of the disaster itself. We present a multiscale analysis of Twitter activity before, during, and after Hurricane Sandy. We examine the online response of 50 metropolitan areas of the United States and find a strong relationship between proximity to Sandy’s path and hurricane-related social media activity. We show that real and perceived threats, together with physical disaster effects, are directly observable through the intensity and composition of Twitter’s message stream. We demonstrate that per-capita Twitter activity strongly correlates with the per-capita economic damage inflicted by the hurricane. We verify our findings for a wide range of disasters and suggest that massive online social networks can be used for rapid assessment of damage caused by a large-scale disaster.
Simulation and Analysis of Perturb and Observe MPPT Algorithm for PV Array Using Ċ UK Converter

This paper presents the comparative analysis between constant duty cycle and Perturb & Observe (P&O) algorithm for extracting the power from Photovoltaic Array (PVA). Because of nonlinear characteristics of PV cell, the maximum power can be extract under particular voltage condition. Therefore, Maximum Power Point Tracking (MPPT) algorithms are used in PVA to maximize the output power. In this paper the MPPT algorithm is implemented using Ćuk converter. The dynamics of PVA is simulated at different solar irradiance and cell temperature. The P&O MPPT technique is a direct control method enables ease to implement and less complexity.
Radicalization into Violent Extremism II : A Review of Conceptual Models and Empirical Research

Over the past decade, analysts have proposed several frameworks to explain the process of radicalization into violent extremism (RVE). These frameworks are based primarily on rational, conceptual models which are neither guided by theory nor derived from systematic research. This article reviews recent (post-9/11) conceptual models of the radicalization process and recent (post-9/11) empirical studies of RVE. It emphasizes the importance of distinguishing between ideological radicalization and terrorism involvement, though both issues deserve further empirical inquiry.Finally, it summarizes some recent RVE-related research efforts, identifies seven things that social science researchers and operational personnel still need to know about violent radicalization, and offers a set of starting assumptions to move forward with a research agenda that might help to thwart tomorrow's terrorists.  Radicalization into Violent Extremism II: A Review of Conceptual Models and Empirical Research Randy Borum University of South Florida
The Mechanics of Earthquakes and Faulting 2 nd edition

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Supporting Comment Moderators in Identifying High Quality Online News Comments

Online comments submitted by readers of news articles can provide valuable feedback and critique, personal views and perspectives, and opportunities for discussion. The varying quality of these comments necessitates that publishers remove the low quality ones, but there is also a growing awareness that by identifying and highlighting high quality contributions this can promote the general quality of the community. In this paper we take a user-centered design approach towards developing a system, CommentIQ, which supports comment moderators in interactively identifying high quality comments using a combination of comment analytic scores as well as visualizations and flexible UI components. We evaluated this system with professional comment moderators working at local and national news outlets and provide insights into the utility and appropriateness of features for journalistic tasks, as well as how the system may enable or transform journalistic practices around online comments.
A mixed-scale dense convolutional neural network for image analysis.

Deep convolutional neural networks have been successfully applied to many image-processing problems in recent works. Popular network architectures often add additional operations and connections to the standard architecture to enable training deeper networks. To achieve accurate results in practice, a large number of trainable parameters are often required. Here, we introduce a network architecture based on using dilated convolutions to capture features at different image scales and densely connecting all feature maps with each other. The resulting architecture is able to achieve accurate results with relatively few parameters and consists of a single set of operations, making it easier to implement, train, and apply in practice, and automatically adapts to different problems. We compare results of the proposed network architecture with popular existing architectures for several segmentation problems, showing that the proposed architecture is able to achieve accurate results with fewer parameters, with a reduced risk of overfitting the training data.
Paraphrasing with Bilingual Parallel Corpora

Previous work has used monolingual parallel corpora to extract and generate paraphrases. We show that this task can be done using bilingual parallel corpora, a much more commonly available resource. Using alignment techniques from phrasebased statistical machine translation, we show how paraphrases in one language can be identified using a phrase in another language as a pivot. We define a paraphrase probability that allows paraphrases extracted from a bilingual parallel corpus to be ranked using translation probabilities, and show how it can be refined to take contextual information into account. We evaluate our paraphrase extraction and ranking methods using a set of manual word alignments, and contrast the quality with paraphrases extracted from automatic alignments.