nils-herrmann/clean_scirepeval huggingface dataset:
Reading the mind in cartoons and stories: an fMRI study of ‘theory of mind’ in verbal and nonverbal tasks

Previous functional imaging studies have explored the brain regions activated by tasks requiring 'theory of mind'--the attribution of mental states. Tasks used have been primarily verbal, and it has been unclear to what extent different results have reflected different tasks, scanning techniques, or genuinely distinct regions of activation. Here we report results from a functional magnetic resonance imaging study (fMRI) involving two rather different tasks both designed to tap theory of mind. Brain activation during the theory of mind condition of a story task and a cartoon task showed considerable overlap, specifically in the medial prefrontal cortex (paracingulate cortex). These results are discussed in relation to the cognitive mechanisms underpinning our everyday ability to 'mind-read'.
Music Synthesis with Reconstructive Phrase Modeling

This article describes a new synthesis technology called reconstructive phrase modeling (RPM). A goal of RPM is to combine the realistic sound quality of sampling with the performance interaction of functional synthesis. Great importance is placed on capturing the dynamics of note transitions-slurs, legato, bow changes, etc. Expressive results are achieved with conventional keyboard controllers. Mastery of special performance techniques is not needed. RPM is an analysis-synthesis system that is related to two important trends in computer music research. The first is a form of additive synthesis in which sounds are represented as a sum of time-varying harmonics plus noise elements. RPM creates expressive performances by searching a database of idiomatic instrumental phrases and combining modified fragments of these phrases to form a new expressive performance. This approach is related to another research trend called concatenative synthesis
Speech recognition in adverse conditions : A review

Speech recognition in adverse conditions: A review Sven L. Mattys a , Matthew H. Davis b , Ann R. Bradlow c & Sophie K. Scott d a Department of Psychology, University of York, York, UK b Medical Research Council, Cognition and Brain Sciences Unit, Cambridge, UK c Department of Linguistics, Northwestern University, Evanston, IL, USA d Institute of Cognitive Neuroscience, University College London, London, UK
Hystor: making the best use of solid state drives in high performance storage systems

With the fast technical improvement, flash memory based Solid State Drives (SSDs) are becoming an important part of the computer storage hierarchy to significantly improve performance and energy efficiency. However, due to its relatively high price and low capacity, a major system research issue to address is on how to make SSDs play their most effective roles in a high-performance storage system in cost- and performance-effective ways.
 In this paper, we will answer several related questions with insights based on the design and implementation of a high performance hybrid storage system, called Hystor. We make the best use of SSDs in storage systems by achieving a set of optimization objectives from both system deployment and algorithm design perspectives. Hystor manages both SSDs and hard disk drives (HDDs) as one single block device with minimal changes to existing OS kernels. By monitoring I/O access patterns at runtime, Hystor can effectively identify blocks that (1) can result in long latencies or (2) are semantically critical (e.g. file system metadata), and stores them in SSDs for future accesses to achieve a significant performance improvement. In order to further leverage the exceptionally high performance of writes in the state-of-the-art SSDs, Hystor also serves as a write-back buffer to speed up write requests. Our measurements on Hystor implemented in the Linux kernel 2.6.25.8 show that it can take advantage of the performance merits of SSDs with only a few lines of changes to the stock Linux kernel. Our system study shows that in a highly effective hybrid storage system, SSDs should play a major role as an independent storage where the best suitable data are adaptively and timely migrated in and retained, and it can also be effective to serve as a write-back buffer.
A Technical Approach to the Energy Blockchain in Microgrids

The present paper considers some technical issues related to the “energy blockchain” paradigm applied to microgrids. In particular, what appears from the study is that the superposition of energy transactions in a microgrid creates a variation of the power losses in all the branches of the microgrid. Traditional power losses allocation in distribution systems takes into account only generators while, in this paper, a real-time attribution of power losses to each transaction involving one generator and one load node is done by defining some suitable indices. Besides, the presence of P–V nodes increases the level of reactive flows and provides a more complex technical perspective. For this reason, reactive power generation for voltage support at P–V nodes poses a further problem of reactive power flow exchange, which is worth of investigation in future works in order to define a possible way of remuneration. The experimental section of the paper considers a medium voltage microgrid and two different operational scenarios.
Hole-filling Algorithm for High-curvature Region in Triangular Mesh Models

As the problem of hole-filling in the high-curvature region of the triangular mesh isn’t satisfactory, a new algorithm is proposed. In the mesh subdivision process, judge the mean curvature of the triangles that have a common edge, if the edge in the high curvature area, inserting new vertices in different ways according to the different situations of the edge length to achieve the mesh subdivision. The algorithm can make holes with neighboring mesh transit more natural and the repairing effect is satisfactory by many examples.
Modelling memory functions with recurrent neural networks consisting of input compensation units: I. Static situations

Humans are able to form internal representations of the information they process—a capability which enables them to perform many different memory tasks. Therefore, the neural system has to learn somehow to represent aspects of the environmental situation; this process is assumed to be based on synaptic changes. The situations to be represented are various as for example different types of static patterns but also dynamic scenes. How are neural networks consisting of mutually connected neurons capable of performing such tasks? Here we propose a new neuronal structure for artificial neurons. This structure allows one to disentangle the dynamics of the recurrent connectivity from the dynamics induced by synaptic changes due to the learning processes. The error signal is computed locally within the individual neuron. Thus, online learning is possible without any additional structures. Recurrent neural networks equipped with these computational units cope with different memory tasks. Examples illustrate how information is extracted from environmental situations comprising fixed patterns to produce sustained activity and to deal with simple algebraic relations.
Consistent inference of probabilities in layered networks: predictions and generalizations

The problem of learning a general input-output relation using a layered neural network is discussed in a statistical framework. By imposing the consistency condition that the error minimization be equivalent to a likelihood maximization for training the network, the authors arrive at a Gibbs distribution on a canonical ensemble of networks with the same architecture. This statistical description enables them to evaluate the probability of a correct prediction of an independent example, after training the network on a given training set. The prediction probability is highly correlated with the generalization ability of the network, as measured outside the training set. This suggests a general and practical criterion for training layered networks by minimizing prediction errors. The authors demonstrate the utility of this criterion for selecting the optimal architecture in the continuity problem. As a theoretical application of the statistical formalism, they discuss the question of learning curves and estimate the sufficient training size needed for correct generalization, in a simple example.<<ETX>>
X-Ku Wide-Bandwidth GaN HEMT MMIC Amplifier with Small Deviation of Output Power and PAE

A new design methodology was proposed to obtain wide-bandwidth and flat-group-delay reactive-matching GaN HEMT MMIC amplifiers. Frequency dependence of the optimal source and load impedance of a GaN HEMT are derived as polynomial equations and matching circuits are designed by small signal simulation without the use of large-signal transistor model and large-signal simulation. Fabricated GaN HEMT MMIC amplifiers, which show a small deviation of Pout and PAE in the range of 8-18 GHz, prove that our methodology is suitable for the design of a wide-bandwidth MMIC amplifier.
Ontology Based SMS Controller for Smart Phones

Text analysis includes lexical analysis of the text and has been widely studied and used in diverse applications. In the last decade, researchers have proposed many efficient solutions to analyze / classify large text dataset, however, analysis / classification of short text is still a challenge because 1) the data is very sparse 2) It contains noise words and 3) It is difficult to understand the syntactical structure of the text. Short Messaging Service (SMS) is a text messaging service for mobile/smart phone and this service is frequently used by all mobile users. Because of the popularity of SMS service, marketing companies nowadays are also using this service for direct marketing also known as SMS marketing.In this paper, we have proposed Ontology based SMS Controller which analyze the text message and classify it using ontology aslegitimate or spam. The proposed system has been tested on different scenarios and experimental results shows that the proposed solution is effective both in terms of efficiency and time. Short Text Classification; SMS Spam; Text Analysis; Ontology based SMS Spam; Text Analysis and Ontology
The Value of Machine Translation for the Professional Translator

More and more Translation Memory (TM) systems nowadays are fortified with machine translation (MT) techniques to enable them to propose a translation to the translator when no match is found in his TM resources. The system attempts this by assembling a combination of terms from its terminology database, translations from its memory, and even portions of them. This paper reviews the most popular commercial TM systems with integrated MT techniques and explores their usefulness based on the perceived practical benefits brought to their users. Feedback from translators reveals a variety of attitudes towards machine translation, with some supporting and others contradicting several points of conventional wisdom regarding the relationship between machine translation and human translators.
A cloud approach to unified lifecycle data management in architecture, engineering, construction and facilities management: Integrating BIMs and SNS

The problem of data integration throughout the lifecycle of a construction project among multiple collaborative enterprises remains unsolved due to the dynamics and fragmented nature of the construction industry. This study presents a novel cloud approach that, focusing on China’s special construction requirements, proposes a series of as-built BIM (building information modeling) tools and a self-organised application model that correlates project engineering data and project management data through a seamless BIM and BSNS (business social netwo rking services) federation. To achieve a logically centralised single-source data structure, a unified data model is constructed that integrates two categories of heterogeneous databas es through the adoption of handlers. Based on these models, key technical mechanisms that are critical to the successful management of large amounts of data are proposed and implemented, including permission, data manipulation and file version control. Specifically, a dynamic Generalised List series is proposed to address the sophisticated construction file versioning issue. The proposed cloud has been successfully used in real applications in China. This research work can enable data sharing not only by individuals and project teams but also by enterprises in a consistent and sustainable way throughout the life of a construction project. This system will reduce costs for construction firms by providing effective and efficient means and guides to comp lex project management, and by facilitating the conversion of project data into enterprise-owned properties.
International anthropometric study of facial morphology in various ethnic groups/races.

When anthropometric methods were introduced into clinical practice to quantify changes in the craniofacial framework, features distinguishing various races/ethnic groups were discovered. To treat congenital or post-traumatic facial disfigurements in members of these groups successfully, surgeons require access to craniofacial databases based on accurate anthropometric measurements. Normative data of facial measurements are indispensable to precise determination of the degree of deviations from the normal. The set of anthropometric measurements of the face in the population studied was gathered by an international team of scientists. Investigators in the country of the given ethnic group, experienced and/or specially trained in anthropometric methods, carried out the measurements. The normal range in each resultant database was then established, providing valuable information about major facial characteristics. Comparison of the ethnic groups' databases with the established norms of the North America whites (NAW) offered the most suitable way to select a method for successful treatment. The study group consisted of 1470 healthy subjects (18 to 30 years), 750 males and 720 females. The largest group (780 subjects, 53.1%) came from Europe, all of them Caucasians. Three were drawn from the Middle-East (180 subjects, 12.2%), five from Asia (300 subjects, 20.4%) and four from peoples of African origin (210 subjects, 14.3%). Their morphological characteristics were determined by 14 anthropometric measurements, 10 of them used already by classic facial artists, Leonardo da Vinci and Albrecht Dürer, complemented by four measurements from the nasal, labio-oral and ear regions. In the regions with single measurements, identical values to NAW in forehead height, mouth width, and ear height were found in 99.7% in both sexes, while in those with multiple measurements, vertical measurements revealed a higher frequency of identical values than horizontal ones. The orbital regions exhibited the greatest variations in identical and contrasting measurements in comparison to NAW. Nose heights and widths contrasted sharply: in relation to NAW the nose was very or extremely significantly wide in both sexes of Asian and Black ethnic groups. Among Caucasians, nose height significantly differed from NAW in three ethnic groups, with one shorter and two greater. In the Middle Eastern groups nose width was identical to those of NAW but the height was significantly greater. The present study, conducted by investigators working separately across the world and with small samples of the population, is clearly preliminary in nature and extent. Yet it may fulfill its mission if medical and anthropological investigators continue the work of establishing normative data of the face. These data are urgently needed by medical professionals but have been lacking up till now in western and northern Europe, Asia, and Africa.
Nuclei segmentation of microscopic breast cancer image using Gram-Schmidt and cluster validation algorithm

A combination of Gram-Schmidt method and cluster validation algorithm based Bayesian is proposed for nuclei segmentation on microscopic breast cancer image. Gram-Schmidt is applied to identify the cell nuclei on a microscopic breast cancer image and the cluster validation algorithm based Bayesian method is used for separating the touching nuclei. The microscopic image of the breast cancer cells are used as dataset. The segmented cell nuclei results on microscopic breast cancer images using Gram-Schmidt method shows that the most of MSE values are below 0.1 and the average MSE of segmented cell nuclei results is 0.08. The average accuracy of separated cell nuclei counting using cluster validation algorithm is 73% compares with the manual counting.
Towards fabrication of Vertical Slit Field Effect Transistor (VeSFET) as new device for nano-scale CMOS technology

This paper proposes a CMOS based process for Vertical Slit Field Effect Transistors. The central part of the device, namely, the vertical slit, is defined by using electron beam lithography and silicon dry etching. In order to verify the validity and the reproducibility of the process, devices having the slit width ranging from 16 nm to 400 nm were fabricated, with slit conductance in the range 0.6 to 3 milliSiemens, in agreement with the expected values.
A Comparative Study of Texture Measures for Terrain Classification

Three standard approaches to automatic texture classification make use of features based on the Fourier power spectrum, on second-order gray level statistics, and on first-order statistics of gray level differences, respectively. Feature sets of these types, all designed analogously, were used to classify two sets of terrain samples. It was found that the Fourier features generally performed more poorly, while the other feature sets all performned comparably.
The EM Side-Channel(s)

We present results of a systematic investigation of leakage of compromising information via electromagnetic (EM) emanations from CMOS devices. These emanations are shown to consist of a multiplicity of signals, each leaking somewhat different information about the underlying computation. We show that not only can EM emanations be used to attack cryptographic devices where the power side–channel is unavailable, they can even be used to break power analysis countermeasures.
A 3D human motion refinement method based on sparse motion bases selection

Motion capture (MOCAP) is an important technique that is widely used in many areas such as computer animation, film industry, physical training and so on. Even with professional MOCAP system, the missing marker problems always occur. Motion refinement is an essential preprocessing step for MOCAP data based applications. Although many existing approaches for motion refinement have been developed, it is still a challenging task due to the complexity and diversity of human motion. A data driven based motion refinement method is proposed in this paper, which modifies the traditional sparse coding process for special task of motion recovery from missing parts. Meanwhile, the objective function is derived by taking both statistical and kinematical property of motion data into account. Poselet model and moving window grouping are applied in the proposed method to achieve a fine-grained feature representation, which preserves the embedded spatial-temporal kinematic information. 5 motion dictionaries are learnt for each kind of poselet from training data in parallel. The motion refine problem is finally solved as an ℓ1-minimization problem. Compared with several state-of-art motion refine methods, the experimental result shows that our approach outperforms the competitors.
The British Industrial Revolution in Global Perspective

THE INDUSTRIAL REVOLUTION was a turning point in the history of the world and inaugurated two centuries of economic growth that have resulted in the high incomes enjoyed in developed countries today.1 Technological progress is the motor of economic growth, and the Industrial Revolution is defi ned by famous technological breakthroughs: machinery to spin and weave cotton, the use of coal to smelt and refi ne iron, and the steam engine.2 In the words of the schoolboy made famous by T. S. Ashton: ‘About 1760 a wave of gadgets swept over England’ (Ashton 1955: 42). The questions for today’s lecture are: How can we explain the technological breakthroughs of the Industrial Revolution? And, why did the Industrial Revolution happen in Britain, rather than France, the Netherlands, or China? These questions will be answered by developing these themes: in comparison with other countries, Britain had an unusual structure of wages and prices in the eighteenth century, and this structure of wages and prices was a major factor in explaining why the revolution happened in Britain. In addition Britain had an effective ‘innovation system’ based on a high
Input and Weight Space Smoothing for Semi-supervised Learning

We propose regularizing the empirical loss for semi-supervised learning by acting on both the input (data) space, and the weight (parameter) space. We show that the two are not equivalent, and in fact are complementary, one affecting the minimality of the resulting representation, the other insensitivity to nuisance variability. We propose a method to perform such smoothing, which combines known input-space smoothing with a novel weight-space smoothing, based on a min-max (adversarial) optimization. The resulting Adversarial Block Coordinate Descent (ABCD) algorithm performs gradient ascent with a small learning rate for a random subset of the weights, and standard gradient descent on the remaining weights in the same mini-batch. It achieves comparable performance to the state-of-the-art without resorting to heavy data augmentation, using a relatively simple architecture.
How to do a grounded theory study: a worked example of a study of dental practices

Qualitative methodologies are increasingly popular in medical research. Grounded theory is the methodology most-often cited by authors of qualitative studies in medicine, but it has been suggested that many 'grounded theory' studies are not concordant with the methodology. In this paper we provide a worked example of a grounded theory project. Our aim is to provide a model for practice, to connect medical researchers with a useful methodology, and to increase the quality of 'grounded theory' research published in the medical literature.



We documented a worked example of using grounded theory methodology in practice.



We describe our sampling, data collection, data analysis and interpretation. We explain how these steps were consistent with grounded theory methodology, and show how they related to one another. Grounded theory methodology assisted us to develop a detailed model of the process of adapting preventive protocols into dental practice, and to analyse variation in this process in different dental practices.



By employing grounded theory methodology rigorously, medical researchers can better design and justify their methods, and produce high-quality findings that will be more useful to patients, professionals and the research community.
Implementing Customizable Online Food Ordering System Using Web Based Application

Typically in a restaurant food order process involves several steps for ordering the food where firstly customer starting from browsing the paper based menu and then inform to the waiter for ordering items. Usually the process require that the customer has to be seated before starting. An alternative method for the customers is “Food Pre-Order System using Web Based Application” in which customer can be able to create the order before they approach the restaurant. Customer using Smartphone. When the customer approach to the restaurant, the saved order can be confirmed by touching the Smartphone. The list of selected pre-ordered items shall be shown on the kitchen screen, and when confirmed, order slip shall be printed for further order processing. The solution provides easy and convenient way to select pre-order transaction form customers.