nils-herrmann/clean_scirepeval huggingface dataset:
The CAD triad hypothesis: a mapping between three moral emotions (contempt, anger, disgust) and three moral codes (community, autonomy, divinity).

It is proposed that 3 emotions--contempt, anger, and disgust--are typically elicited, across cultures, by violations of 3 moral codes proposed by R. A. Shweder and his colleagues (R. A. Shweder, N. C. Much, M. Mahapatra, & L. Park, 1997). The proposed alignment links anger to autonomy (individual rights violations), contempt to community (violation of communal codes including hierarchy), and disgust to divinity (violations of purity-sanctity). This is the CAD triad hypothesis. Students in the United States and Japan were presented with descriptions of situations that involve 1 of the types of moral violations and asked to assign either an appropriate facial expression (from a set of 6) or an appropriate word (contempt, anger, disgust, or their translations). Results generally supported the CAD triad hypothesis. Results were further confirmed by analysis of facial expressions actually made by Americans to the descriptions of these situations.
Regression shrinkage and selection via the lasso : a retrospective

This is equivalent to minimizing the sum of squares with a constraint of the form Σ |βj| s. It is similar to ridge regression, which has constraint Σjβ j t. Because of the form of the l1-penalty, the lasso does variable selection and shrinkage, whereas ridge regression, in contrast, only shrinks. If we consider a more general penalty of the form .Σpj=1β q j / 1=q, then the lasso uses q = 1 and ridge regression has q = 2. Subset selection emerges as q → 0, and the lasso uses the smallest value of q (i.e. closest to subset selection) that yields a convex problem. Convexity is very attractive for computational purposes.
Fabric defect detection using linear filtering and morphological operations

An algorithm with linear filters and morphological operations has been proposed for automatic fabric defect detection. The algorithm is applied off-line and real-time to denim fabric samples for five types of defects. All defect types have been detected successfully and the defective regions are labeled. The defective fabric samples are then classified by using feed forward neural network method. Both defect detection and classification application performances are evaluated statistically. Defect detection performance of real time and off-line applications are obtained as 88% and 83% respectively. The defective images are classified with an average accuracy rate of 96.3%.
Recurrent Spatial Transformer Networks

We integrate the recently proposed spatial transformer network (SPN) ( Jaderberg & Simonyan , 2015) into a recurrent neural network (RNN) to form an RNN-SPN model. We use the RNNSPN to classify digits in cluttered MNIST sequences. The proposed model achieves a single digit error of 1.5% compared to 2.9% for a convolutional networks and 2.0% for convolutional networks with SPN layers. The SPN outputs a zoomed, rotated and skewed version of the input image. We investigate different down-sampling factors (ratio of pixel in input and output) for the SPN and show that the RNN-SPN model is able to down-sample the input images without deteriorating performance. The down-sampling in RNN-SPN can be thought of as adaptive downsampling that minimizes the information loss in the regions of interest. We attribute the superior performance of the RNN-SPN to the fact that it can attend to a sequence of regions of interest.
Automated Capturing and Systematic Usage of DevOps Knowledge for Cloud Applications

DevOps is an emerging paradigm to actively foster the collaboration between system developers and operations in order to enable efficient end-to-end automation of software deployment and management processes. DevOps is typically combined with Cloud computing, which enables rapid, on-demand provisioning of underlying resources such as virtual servers, storage, or database instances using APIs in a self-service manner. Today, an ever-growing amount of DevOps tools, reusable artifacts such as scripts, and Cloud services are available to implement DevOps automation. Thus, informed decision making on the appropriate approach (es) for the needs of an application is hard. In this work we present a collaborative and holistic approach to capture DevOps knowledge in a knowledgebase. Beside the ability to capture expert knowledge and utilize crowd sourcing approaches, we implemented a crawling framework to automatically discover and capture DevOps knowledge. Moreover, we show how this knowledge is utilized to deploy and operate Cloud applications.
A PageRank based detection technique for phishing web sites

Phishing is an attempt to acquire one's information without user's knowledge by tricking him by making similar kind of website or sending emails to user which looks like legitimate site or email. Phishing is a social cyber threat attack, which is causing severe loss of economy to the user, due to phishing attacks online transaction users are declining. This paper aims to design and implement a new technique to detect phishing web sites using Google's PageRank. Google gives a PageRank value to each site in the web. This work uses the PageRank value and other features to classify phishing sites from normal sites. We have collected a dataset of 100 phishing sites and 100 legitimate sites for our use. By using this Google PageRank technique 98% of the sites are correctly classified, showing only 0.02 false positive rate and 0.02 false negative rate.
Plastic waste inputs from land into the ocean

Plastic debris in the marine environment is widely documented, but the quantity of plastic entering the ocean from waste generated on land is unknown. By linking worldwide data on solid waste, population density, and economic status, we estimated the mass of land-based plastic waste entering the ocean. We calculate that 275 million metric tons (MT) of plastic waste was generated in 192 coastal countries in 2010, with 4.8 to 12.7 million MT entering the ocean. Population size and the quality of waste management systems largely determine which countries contribute the greatest mass of uncaptured waste available to become plastic marine debris. Without waste management infrastructure improvements, the cumulative quantity of plastic waste available to enter the ocean from land is predicted to increase by an order of magnitude by 2025.
Impact of Smart Grid on distribution system design

There has been much recent discussion on what distribution systems can and should look like in the future. Terms related to this discussion include smart grid, distribution system of the future, and others. Functionally, a smart grid should be able to provide new abilities such as self-healing, high reliability, energy management, and real-time pricing. From a design perspective, a smart grid will likely incorporate new technologies such as advanced metering, automation, communication, distributed generation, and distributed storage. This paper discussed the potential impact that issues related to smart grid will have on distribution system design.
Infrared face recognition: a comprehensive review of methodologies and databases

Automatic face recognition is an area with immense practical potential which includes a wide range of commercial and law enforcement applications. Hence it is unsurprising that it continues to be one of the most active research areas of computer vision. Even after over three decades of intense research, the state-of-the-art in face recognition continues to improve, benefitting from advances in a range of different research fields such as image processing, pattern recognition, computer graphics, and physiology. Systems based on visible spectrum images, the most researched face recognition modality, have reached a significant level of maturity with some practical success. However, they continue to face challenges in the presence of illumination, pose and expression changes, as well as facial disguises, all of which can significantly decrease recognition accuracy. Amongst various approaches which have been proposed in an attempt to overcome these limitations, the use of infrared (IR) imaging has emerged as a particularly promising research direction. This paper presents a comprehensive and timely review of the literature on this subject. Our key contributions are (i) a summary of the inherent properties of infrared imaging which makes this modality promising in the context of face recognition; (ii) a systematic review of the most influential approaches, with a focus on emerging common trends as well as key differences between alternative methodologies; (iii) a description of the main databases of infrared facial images available to the researcher; and lastly (iv) a discussion of the most promising avenues for future research.
Iago attacks: why the system call API is a bad untrusted RPC interface

In recent years, researchers have proposed systems for running trusted code on an untrusted operating system. Protection mechanisms deployed by such systems keep a malicious kernel from directly manipulating a trusted application's state. Under such systems, the application and kernel are, conceptually, peers, and the system call API defines an RPC interface between them.
 We introduce Iago attacks, attacks that a malicious kernel can mount in this model. We show how a carefully chosen sequence of integer return values to Linux system calls can lead a supposedly protected process to act against its interests, and even to undertake arbitrary computation at the malicious kernel's behest.
 Iago attacks are evidence that protecting applications from malicious kernels is more difficult than previously realized.
The NimStim set of facial expressions: Judgments from untrained research participants

A set of face stimuli called the NimStim Set of Facial Expressions is described. The goal in creating this set was to provide facial expressions that untrained individuals, characteristic of research participants, would recognize. This set is large in number, multiracial, and available to the scientific community online. The results of psychometric evaluations of these stimuli are presented. The results lend empirical support for the validity and reliability of this set of facial expressions as determined by accurate identification of expressions and high intra-participant agreement across two testing sessions, respectively.
Argumentation in artificial intelligence

Over the last ten years, argumentation has come to be increasingly central as a core study within Artificial Intelligence (AI). The articles forming this volume reflect a variety of important trends, developments, and applications covering a range of current topics relating to the theory and applications of argumentation. Our aims in this introduction are, firstly, to place these contributions in the context of the historical foundations of argumentation in AI and, subsequently, to discuss a number of themes that have emerged in recent years resulting in a significant broadening of the areas in which argumentation based methods are used. We begin by presenting a brief overview of the issues of interest within the classical study of argumentation: in particular, its relationship— in terms of both similarities and important differences—to traditional concepts of logical reasoning and mathematical proof. We continue by outlining how a number of foundational contributions provided the basis for the formulation of argumentation models and their promotion in AI related settings and then consider a number of new themes that have emerged in recent years, many of which provide the principal topics of the research presented in this volume.
Aerial display of vibrotactile sensation with high spatial-temporal resolution using large-aperture airborne ultrasound phased array

We fabricated a tactile display which can generate vibrotactile sensation on human skin on which no equipment is mounted. It utilizes focused airborne ultrasound radiation pressure for stimulation. The workspace of our new tactile display is widened to a cube of 1 m × 1 m × 1 m, which allows users free motions in it. In order to widen the workspace, our new prototype integrates multiple ultrasound transducer units and achieves a large aperture airborne ultrasound phased array. As the workspace is widened, it has become possible to stimulate arbitrary regions all over a human body. The amplitude of imposed radiation pressure can be time-variant. The profiles of generated vibrotactile stimuli can be designed with a temporal resolution of 0.5 ms and 320-level quantization of radiation pressure amplitude. It is easy to choose a recorded waveform and reproduce it as vibrotactile stimuli at an arbitrary spatial point. This paper introduces how our new tactile display works and reports its performance evaluation.
Bit-serial Reed - Solomon encoders

This paper presents new concepts and techniques for implementing encoders for Reed-Solomon codes, with or without interleaving. Reed-Solomon encoders based on these concepts and techniques often require substantially less hardware than even linear cyclic binary codes of comparable redundancy. A CODEWORD of a cyclic code is a sequence of characters which can be viewed as the coefficients of a polynomial n-1 c(x) = 2 c,x’. i=o The characters C,,, C,,-2, Cn-s,. . . , C,, Co are elements in a finite field. In this paper, we consider only fields of order 2”, where m m ight be any integer. A sequence of n characters is a codeword if and only if its corresponding polynomial, C(x), is a mu ltiple of the code’s generator polynomial, g(x). Let deg g(x) = n k. The common method of encoding a cyclic code is to regard q-,9 cn-2,* * * 9 C,-, as message characters, and to divide the polynomial
Super-Linear Convergence of Dual Augmented-Lagrangian Algorithm for Sparsity Regularized Estimation

We analyze the convergence behaviour of a recently proposed algorithm for regularized estimation called Dual Augmented Lagrangian (DAL). Our analysis i based on a new interpretation of DAL as a proximal minimization algorithm. We theoretically show under some conditions that DAL converges super-linearly in a non-asymptotic and globa l sense. Due to a special modelling of sparse estimation problems in the context of machine lear ning, the assumptions we make are milder and more natural than those made in conventional anal ysis of augmented Lagrangian algorithms. In addition, the new interpretation enables us to generalize DAL to wide varieties of sparse estimation problems. We experimentally confirm our a nalysis in a large scale l1-regularized logistic regression problem and extensively compare the ef ficiency of DAL algorithm to previously proposed algorithms on both synthetic and benchmark data se ts.
Views: A Way for Pattern Matching to Cohabit with Data Abstraction

Pattern matching and data abstraction are important concepts in designing programs, but they do not fit well together. Pattern matching depends on making public a free data type representation, while data abstraction depends on hiding the representation. This paper proposes the views mechanism as a means of reconciling this conflict. A view allows any type to be viewed as a free data type, thus combining the clarity of pattern matching with the efficiency of data abstraction.
Dual-polarized patch array antenna package for 5G communication systems

For applications of 5G (5th generation mobile networks) communication systems, dual-polarized patch array antenna operating at 28.5 GHz is designed on the package substrate. To verify the radiation performance of designed antenna itself, a test package including two patch antennas is also design and its scattering parameters were measured. Using a large height of dielectric materials, 1.5 ∼ 2.0 GHz of antenna bandwidth is achieved which is wide enough. Besides, the dielectric constants are reduced to reflect variances of material properties in the higher frequency region. Measured results of the test package show a good performance at the operating frequency, indicating that the fabricated antenna package will perform well, either. In the future work, manufacturing variances will be investigated further.
Automatic Interpretation and Coding of Face Images Using Flexible Models

Face images are difficult to interpret because they are highly variable. Sources of variability include individual appearance, 3D pose, facial expression , and lighting. We describe a compact parametrized model of facial appearance which takes into account all these sources of variability. The model represents both shape and gray-level appearance , and is created by performing a statistical analysis over a training set of face images. A robust multiresolution search algorithm is used to fit the model to faces in new images. This allows the main facial features to be located , and a set of shape , and gray-level appearance parameters to be recovered. A good approximation to a given face can be reconstructed using less than 100 of these parameters. This representation can be used for tasks such as image coding, person identification, 3D pose recovery, gender recognition , and expression recognition. Experimental results are presented for a database of 690 face images obtained under widely varying conditions of 3D pose, lighting , and facial expression. The system performs well on all the tasks listed above. Index Terms —Face recognition, expression recognition, pose recovery, coding-reconstruction, facial feature location, deformable
Modeling Data Mining Applications for Prediction of Prepaid Churn in Telecommunication Services

This paper defines an advanced methodology for modeling applications based on Data Mining methods that represents a logical framework for development of Data Mining applications. Methodology suggested here for Data Mining modeling process has been applied and tested through Data Mining applications for predicting Prepaid users churn in the telecom industry. The main emphasis of this paper is defining of a successful model for prediction of potential Prepaid churners, in which the most important part is to identify the very set of input variables that are high enough to make the prediction model precise and reliable. Several models have been created and compared on the basis of different Data Mining methods and algorithms (neural networks, decision trees, logistic regression). For the modeling examples we used WEKA analysis tool.
Protecting Users by Confining JavaScript with COWL

Modern web applications are conglomerations of JavaScript written by multiple authors: application developers routinely incorporate code from third-party libraries, and mashup applications synthesize data and code hosted at different sites. In current browsers, a web application’s developer and user must trust third-party code in libraries not to leak the user’s sensitive information from within applications. Even worse, in the status quo, the only way to implement some mashups is for the user to give her login credentials for one site to the operator of another site. Fundamentally, today’s browser security model trades privacy for flexibility because it lacks a sufficient mechanism for confining untrusted code. We present COWL, a robust JavaScript confinement system for modern web browsers. COWL introduces label-based mandatory access control to browsing contexts in a way that is fully backwardcompatible with legacy web content. We use a series of case-study applications to motivate COWL’s design and demonstrate how COWL allows both the inclusion of untrusted scripts in applications and the building of mashups that combine sensitive information from multiple mutually distrusting origins, all while protecting users’ privacy. Measurements of two COWL implementations, one in Firefox and one in Chromium, demonstrate a virtually imperceptible increase in page-load latency.
IDF diabetes atlas: global estimates of the prevalence of diabetes for 2011 and 2030.

Diabetes is an increasingly important condition globally and robust estimates of its prevalence are required for allocating resources.



Data sources from 1980 to April 2011 were sought and characterised. The Analytic Hierarchy Process (AHP) was used to select the most appropriate study or studies for each country, and estimates for countries without data were modelled. A logistic regression model was used to generate smoothed age-specific estimates which were applied to UN population estimates for 2011.



A total of 565 data sources were reviewed, of which 170 sources from 110 countries were selected. In 2011 there are 366 million people with diabetes, and this is expected to rise to 552 million by 2030. Most people with diabetes live in low- and middle-income countries, and these countries will also see the greatest increase over the next 19 years.



This paper builds on previous IDF estimates and shows that the global diabetes epidemic continues to grow. Recent studies show that previous estimates have been very conservative. The new IDF estimates use a simple and transparent approach and are consistent with recent estimates from the Global Burden of Disease study. IDF estimates will be updated annually.
When perception is more than reality: the effects of perceived versus actual resource depletion on self-regulatory behavior.

Considerable research demonstrates that the depletion of self-regulatory resources impairs performance on subsequent tasks that demand these resources. The current research sought to assess the impact of perceived resource depletion on subsequent task performance at both high and low levels of actual depletion. The authors manipulated perceived resource depletion by having participants 1st complete a depleting or nondepleting task before being presented with feedback that did or did not provide a situational attribution for their internal state. Participants then persisted at a problem-solving task (Experiments 1-2), completed an attention-regulation task (Experiment 3), or responded to a persuasive message (Experiment 4). The findings consistently demonstrated that individuals who perceived themselves as less (vs. more) depleted, whether high or low in actual depletion, were more successful at subsequent self-regulation. Thus, perceived regulatory depletion can impact subsequent task performance-and this impact can be independent of one's actual state of depletion.
A novel and robust method for automatic license plate recognition system based on pattern recognition

In this paper, for finding the place of plate, a real time and fast method is expressed. In our suggested method, the image is taken to HSV color space; then, it is broken into blocks in a stable size. In frequent process, each block, in special pattern is probed. With the appearance of pattern, its neighboring blocks according to geometry of plate as a candidate are considered and increase blocks, are omitted. This operation is done for all of the uncontrolled blocks of images. First, all of the probable candidates are exploited; then, the place of plate is obtained among exploited candidates as density and geometry rate. In probing every block, only its lip pixel is studied which consists 23.44% of block area. From the features of suggestive method, we can mention the lack of use of expensive operation in image process and its low dynamic that it increases image process speed. This method is examined on the group of picture in background, distance and point of view. The rate of exploited plate reached at 99.33% and character recognition rate achieved 97%.