nils-herrmann/clean_scirepeval huggingface dataset:
Automated Pattern Recognition and Defect Inspection System

Packaging appearance is extremely important in cigarette manufacturing. Typically, there are two types of cigarette packaging defects: (1) cigarette laying defects such as incorrect cigarette numbers and irregular layout; (2) tin paper handle defects such as folded paper handles. In this paper, an automated vision-based defect inspection system is designed for cigarettes packaged in tin containers. The first type of defects is inspected by counting the number of cigarettes in a tin container. First k-means clustering is performed to segment cigarette regions. After noise filtering, valid cigarette regions are identified by estimating individual cigarette area using linear regression. The k clustering centers and area estimation function are learned off-line on training images. The second kind of defect is detected by checking the segmented paper handle region. Experimental results on 500 test images demonstrate the effectiveness of the proposed inspection system. The proposed method also contributes to the general detection and classification system such as identifying mitosis in early diagnosis of cervical cancer.
Identifying optimal targets of network attack by belief propagation

For a network formed by nodes and undirected links between pairs of nodes, the network optimal attack problem aims at deleting a minimum number of target nodes to break the network down into many small components. This problem is intrinsically related to the feedback vertex set problem that was successfully tackled by spin-glass theory and an associated belief propagation-guided decimation (BPD) algorithm [Zhou,  (2013)EPJBFY1434-602810.1140/epjb/e2013-40690-1]. In the present work we apply the BPD algorithm (which has approximately linear time complexity) to the network optimal attack problem and demonstrate that it has much better performance than a recently proposed collective information algorithm [Morone and Makse,  (2015)NATUAS0028-083610.1038/nature14604] for different types of random networks and real-world network instances. The BPD-guided attack scheme often induces an abrupt collapse of the whole network, which may make it very difficult to defend.
Showing Off? Human Mobility and the Interplay of Traits, Self-Disclosure, and Facebook Check-Ins

Mobile and location-based media refer to technologies that can openly and dynamically portray the characteristics of the users and their mundane life. Facebook check-ins highlights physical and informational mobility of the users relating individual activities into spaces. This study explored how personality traits like extraversion and narcissism function to influence self-disclosure that, in turn, impacts the intensity of check-ins on Facebook. Using survey data collected through Facebook check-in users in Taiwan (N 1⁄4 523), the results demonstrated that although extraversion and narcissism might not directly impact check-in intensity on Facebook, the indirect effects of selfdisclosure and exhibitionism were particularly salient. Moreover, a complete path from extraversion to Facebook check-in through self-disclosure and exhibitionism was discovered. Theoretical implications on human mobility and selective self-presentation are also discussed.
Forest Fire Modeling and Early Detection using Wireless Sensor Networks

Forest fires cost millions of dollars in damages and claim many human lives every year. Apart from preventive measures, early detection and suppression of fires is the only way to minimize the damages and casualties. We present the design and evaluation of a wireless sensor network for early detection of forest fires. We first present the key aspects in modeling forest fires. We do this by analyzing the Fire Weather Index (FWI) System, and show how its different components can be used in designing efficient fire detection systems. The FWI System is one of the most comprehensive forest fire danger rating systems in North America, and it is backed by several decades of forestry research. The analysis of the FWI System could be of interest in its own right to researchers working in the sensor network area and to sensor manufacturers who can optimize the communication and sensing modules of their products to better fit forest fire detection systems. Then, we model the forest fire detection problem as a coverage problem in wireless sensor networks, and we present a distributed algorithm to solve it. In addition, we show how our algorithm can achieve various coverage degrees at different subareas of the forest, which can be used to provide unequal monitoring quality of forest zones. Unequal monitoring is important to protect residential and industrial neighborhoods close to forests. Finally, we present a simple data aggregation scheme based on the FWI System. This data aggregation scheme significantly prolongs the network lifetime, because it only delivers the data that is of interest to the application. We validate several aspects of our design using simulation.
Fake finger detection by skin distortion analysis

Attacking fingerprint-based biometric systems by presenting fake fingers at the sensor could be a serious threat for unattended applications. This work introduces a new approach for discriminating fake fingers from real ones, based on the analysis of skin distortion. The user is required to move the finger while pressing it against the scanner surface, thus deliberately exaggerating the skin distortion. Novel techniques for extracting, encoding and comparing skin distortion information are formally defined and systematically evaluated over a test set of real and fake fingers. The proposed approach is privacy friendly and does not require additional expensive hardware besides a fingerprint scanner capable of capturing and delivering frames at proper rate. The experimental results indicate the new approach to be a very promising technique for making fingerprint recognition systems more robust against fake-finger-based spoofing attempts
Density-based clustering of uncertain data

In many different application areas, e.g. sensor databases, location based services or face recognition systems, distances between odjects have to be computed based on vague and uncertain data. Commonly, the distances between these uncertain object descriptions are expressed by one numerical distance value. Based on such single-valued distance functions standard data mining algorithms can work without any changes. In this paper, we propose to express the similarity between two fuzzy objects by distance probability functions. These fuzzy distance functions assign a probability value to each possible distance value. By integrating these fuzzy distance functions directly into data mining algorithms, the full information provided by these functions is exploited. In order to demonstrate the benefits of this general approach, we enhance the density-based clustering algorithm DBSCAN so that it can work directly on these fuzzy distance functions. In a detailed experimental evaluation based on artificial and real-world data sets, we show the characteristics and benefits of our new approach.
Validation of web service compositions

Web services support software architectures that can evolve dynamically. In particular, here we focus on architectures where services are composed (orchestrated) through a workflow described in the BPEL language. We assume that the resulting composite service refers to external services through assertions that specify their expected functional and non-functional properties. Based on these assertions, the composite service may be verified at design time by checking that it ensures certain relevant properties. Because of the dynamic nature of Web services and the multiple stakeholders involved in their provision, however, the external services may evolve dynamically, and even unexpectedly. They may become inconsistent with respect to the assertions against which the workflow was verified during development. As a consequence, validation of the composition must extend to run time. We introduce an assertion language, called ALBERT, which can be used to specify both functional and non-functional properties. We also describe an environment which supports design-time verification of ALBERT assertions for BPEL workflows via model checking. At run time, the assertions can be turned into checks that a software monitor performs on the composite system to verify that it continues to guarantee its required properties. A TeleAssistance application is provided as a running example to illustrate our validation framework.
When choice is demotivating: can one desire too much of a good thing?

Current psychological theory and research affirm the positive affective and motivational consequences of having personal choice. These findings have led to the popular notion that the more choice, the better-that the human ability to manage, and the human desire for, choice is unlimited. Findings from 3 experimental studies starkly challenge this implicit assumption that having more choices is necessarily more intrinsically motivating than having fewer. These experiments, which were conducted in both field and laboratory settings, show that people are more likely to purchase gourmet jams or chocolates or to undertake optional class essay assignments when offered a limited array of 6 choices rather than a more extensive array of 24 or 30 choices. Moreover, participants actually reported greater subsequent satisfaction with their selections and wrote better essays when their original set of options had been limited. Implications for future research are discussed.
A theoretician's guide to the experimental analysis of algorithms

of the 5th and 6th DIMACS Implementation Challenges, Goldwasser Johnson, and McGeoch (eds),  2002. A Theoretician's Guide to the Experimental Analysis of Algorithms David S. Johnson AT&T Labs { Research http://www.research.att.com/ dsj/ November 25, 2001  This paper presents an informal discussion of issues that arise when one attempts to analyze algorithms experimentally. It is based on lessons learned by the author over the course of more than a decade of experimentation, survey paper writing, refereeing, and lively discussions with other experimentalists. Although written from the perspective of a theoretical computer scientist, it is intended to be of use to researchers from all elds who want to study algorithms experimentally. It has two goals: rst, to provide a useful guide to new experimentalists about how such work can best be performed and written up, and second, to challenge current researchers to think about whether their own work might be improved from a scienti c point of view. With the latter purpose in mind, the author hopes that at least a few of his recommendations will be considered controversial.
Adversarial stylometry: Circumventing authorship recognition to preserve privacy and anonymity

The use of stylometry, authorship recognition through purely linguistic means, has contributed to literary, historical, and criminal investigation breakthroughs. Existing stylometry research assumes that authors have not attempted to disguise their linguistic writing style. We challenge this basic assumption of existing stylometry methodologies and present a new area of research: adversarial stylometry. Adversaries have a devastating effect on the robustness of existing classification methods. Our work presents a framework for creating adversarial passages including obfuscation, where a subject attempts to hide her identity, and imitation, where a subject attempts to frame another subject by imitating his writing style, and translation where original passages are obfuscated with machine translation services. This research demonstrates that manual circumvention methods work very well while automated translation methods are not effective. The obfuscation method reduces the techniques' effectiveness to the level of random guessing and the imitation attempts succeed up to 67% of the time depending on the stylometry technique used. These results are more significant given the fact that experimental subjects were unfamiliar with stylometry, were not professional writers, and spent little time on the attacks. This article also contributes to the field by using human subjects to empirically validate the claim of high accuracy for four current techniques (without adversaries). We have also compiled and released two corpora of adversarial stylometry texts to promote research in this field with a total of 57 unique authors. We argue that this field is important to a multidisciplinary approach to privacy, security, and anonymity.
Mental Health Discourse on reddit: Self-Disclosure, Social Support, and Anonymity

Social media is continually emerging as a platform of information exchange around health challenges. We study mental health discourse on the popular social media: reddit. Building on findings about health information seeking and sharing practices in online forums, and social media like Twitter, we address three research challenges. First, we present a characterization of self-disclosure in mental illness communities on reddit. We observe individuals discussing a variety of concerns ranging from the daily grind to specific queries about diagnosis and treatment. Second, we build a statistical model to examine the factors that drive social support on mental health reddit communities. We also develop language models to characterize mental health social support, which are observed to bear emotional, informational, instrumental, and prescriptive information. Finally, we study disinhibition in the light of the dissociative anonymity that reddit’s throwaway accounts provide. Apart from promoting open conversations, such anonymity surprisingly is found to gather feedback that is more involving and emotionally engaging. Our findings reveal, for the first time, the kind of unique information needs that a social media like reddit might be fulfilling when it comes to a stigmatic illness. They also expand our understanding of the role of the social web in behavioral therapy.
Parameterised sigmoid and reLU hidden activation functions for DNN acoustic modelling

The form of hidden activation functions has been always an important issue in deep neural network (DNN) design. The most common choices for acoustic modelling are the standard Sigmoid and rectified linear unit (ReLU), which are normally used with fixed function shapes and no adaptive parameters. Recently, there have been several papers that have studied the use of parameterised activation functions for both computer vision and speaker adaptation tasks. In this paper, we investigate generalised forms of both Sigmoid and ReLU with learnable parameters, as well as their integration with the standard DNN acoustic model training process. Experiments using conversational telephone speech (CTS) Mandarin data, result in an average of 3.4% and 2.0% relative word error rate (WER) reduction with Sigmoid and ReLU parameterisations.
Ranking with ordered weighted pairwise classification

In ranking with the pairwise classification approach, the loss associated to a predicted ranked list is the mean of the pairwise classification losses. This loss is inadequate for tasks like information retrieval where we prefer ranked lists with high precision on the top of the list. We propose to optimize a larger class of loss functions for ranking, based on an ordered weighted average (OWA) (Yager, 1988) of the classification losses. Convex OWA aggregation operators range from the max to the mean depending on their weights, and can be used to focus on the top ranked elements as they give more weight to the largest losses. When aggregating hinge losses, the optimization problem is similar to the SVM for interdependent output spaces. Moreover, we show that OWA aggregates of margin-based classification losses have good generalization properties. Experiments on the Letor 3.0 benchmark dataset for information retrieval validate our approach.
Key Technologies for Big Data Stream Computing

As a new trend for data-intensive computing, real-time stream computing is gaining significant attention in the big data era. In theory, stream computing is an effective way to support big data by providing extremely low-latency processing tools and massively parallel processing architectures in real-time data analysis. However, in most existing stream computing environments, how to efficiently deal with big data stream computing, and how to build efficient big data stream computing systems are posing great challenges to big data computing research. First, the data stream graphs and the system architecture for big data stream computing, and some related key technologies, such as system structure, data transmission, application interfaces, and high availability, are systemically researched. Then, we give a classification of the latest research and depict the development status of some popular big data stream computing systems, including Twitter Storm, Yahoo! S4, Microsoft TimeStream, and Microsoft Naiad. Finally, the potential challenges and future directions of big data stream computing are discussed. 11.
Principled Evaluation of Differentially Private Algorithms using DPBench

Differential privacy has become the dominant standard in the research community for strong privacy protection. There has been a flood of research into query answering algorithms that meet this standard. Algorithms are becoming increasingly complex, and in particular, the performance of many emerging algorithms is data dependent, meaning the distribution of the noise added to query answers may change depending on the input data. Theoretical analysis typically only considers the worst case, making empirical study of average case performance increasingly important. In this paper we propose a set of evaluation principles which we argue are essential for sound evaluation. Based on these principles we propose DPBench, a novel evaluation framework for standardized evaluation of privacy algorithms. We then apply our benchmark to evaluate algorithms for answering 1- and 2-dimensional range queries. The result is a thorough empirical study of 15 published algorithms on a total of 27 datasets that offers new insights into algorithm behavior---in particular the influence of dataset scale and shape---and a more complete characterization of the state of the art. Our methodology is able to resolve inconsistencies in prior empirical studies and place algorithm performance in context through comparison to simple baselines. Finally, we pose open research questions which we hope will guide future algorithm design.
A hox gene inspired generative approach to evolving robot morphology

This paper proposes an approach to representing robot morphology and control, using a two-level description linked to two different physical axes of development. The bioinspired encoding produces robots with animal-like bilateral limbed morphology with co-evolved control parameters using a central pattern generator-based modular artificial neural network. Experiments are performed on optimizing a simple simulated locomotion problem, using multi-objective evolution with two secondary objectives. The results show that the representation is capable of producing a variety of viable designs even with a relatively restricted set of parameters and a very simple control system. Furthermore, the utility of a cumulative encoding over a non-cumulative approach is demonstrated. We also show that the representation is viable for real-life reproduction by automatically generating CAD files, 3D printing the limbs, and attaching off-the-shelf servomotors.
What regularized auto-encoders learn from the data-generating distribution

What do auto-encoders learn about the underlying data-generating distribution? Recent work suggests that some auto-encoder variants do a good job of capturing the local manifold structure of data. This paper clarifies some of these previous observations by showing that minimizing a particular form of regularized reconstruction error yields a reconstruction function that locally characterizes the shape of the data-generating density. We show that the auto-encoder captures the score (derivative of the log-density with respect to the input). It contradicts previous interpretations of reconstruction error as an energy function. Unlike previous results, the theorems provided here are completely generic and do not depend on the parameterization of the auto-encoder: they show what the auto-encoder would tend to if given enough capacity and examples. These results are for a contractive training criterion we show to be similar to the denoising auto-encoder training criterion with small corruption noise, but with contraction applied on the whole reconstruction function rather than just encoder. Similarly to score matching, one can consider the proposed training criterion as a convenient alternative to maximum likelihood because it does not involve a partition function. Finally, we show how an approximate Metropolis-Hastings MCMC can be setup to recover samples from the estimated distribution, and this is confirmed in sampling experiments.
The media's representation of the ideal male body: a cause for muscle dysmorphia?

This study sought to examine the effects of media images on men's attitudes toward their body appearance.



A group of college men viewed advertisements showing muscular men, whereas a control group viewed neutral advertisements. Immediately thereafter, participants performed a computerized test of body image perception while unaware of the hypotheses being tested in the study.



The students exposed to the muscular images showed a significantly greater discrepancy between their own perceived muscularity and the level of muscularity that they ideally wanted to have.



These findings suggest that media images, even in a brief presentation, can affect men's views of their bodies.
Small-Signal Discrete-Time Modeling of Digitally Controlled PWM Converters

The letter presents an exact small-signal discrete-time model for digitally controlled pulsewidth modulated (PWM) dc-dc converters operating in constant frequency continuous conduction mode (CCM) with a single effective A/D sampling instant per switching period. The model, which is based on well-known approaches to discrete-time modeling and the standard Z-transform, takes into account sampling, modulator effects and delays in the control loop, and is well suited for direct digital design of digital compensators. The letter presents general results valid for any CCM converter with leading or trailing edge PWM. Specific examples, including approximate closed-form expressions for control-to-output transfer functions are given for buck and boost converters. The model is verified in simulation using an independent system identification approach.
Inferring social ties across heterogenous networks

It is well known that different types of social ties have essentially different influence on people. However, users in online social networks rarely categorize their contacts into "family", "colleagues", or "classmates". While a bulk of research has focused on inferring particular types of relationships in a specific social network, few publications systematically study the generalization of the problem of inferring social ties over multiple heterogeneous networks. In this work, we develop a framework for classifying the type of social relationships by learning across heterogeneous networks. The framework incorporates social theories into a factor graph model, which effectively improves the accuracy of inferring the type of social relationships in a target network by borrowing knowledge from a different source network. Our empirical study on five different genres of networks validates the effectiveness of the proposed framework. For example, by leveraging information from a coauthor network with labeled advisor-advisee relationships, the proposed framework is able to obtain an F1-score of 90% (8-28% improvements over alternative methods) for inferring manager-subordinate relationships in an enterprise email network.