nils-herrmann/clean_scirepeval huggingface dataset:
Simplified intrinsic camera calibration and hand-eye calibration for robot vision

In this paper we investigate how intrinsic camera calibration and hand-eye calibration can be performed on a robot vision system using the simplest possible motions and a planar calibration object. The standard methods on plane-based camera calibration are extended with theory on how to use pure translational motions for the intrinsic calibration and we see how hand-eye calibration can be performed within the same framework. The calibration of two cameras in a stereo head configuration is shown to be an interesting application of the developed theory. Results of experiments on a real robot vision system are presented.
Fuzzy Cognitive Map to model project management problems

Project management is a complex process impacted by numerous factors either from the external environment and/or internal factors completely or partially under the project manager's control. Managing projects successfully involves a complex amalgamation of comprehensive, informed planning, dynamic assessment and analysis of changes in external and internal factors, and the development and communication of updated strategies over the life of the project. Project management involves the interaction and analysis of many systems and requires the continuous integration and evaluation of large amounts of information. Fuzzy Cognitive Maps (FCM) allow us to encode project management knowledge and experiential results to create a useful model of the interacting systems. This paper covers the representation and development of a construction project management FCM that provides an integrated view of the most important concepts affecting construction project management and risk management. This paper then presents the soft computing approach of FCM to project management (PM) modeling and analysis. The resulting PM-FCM models the interaction of internal and external factors and offers an abstract conceptual model of interacting concepts for construction project management application.
The Medial Axis of a Multi-Layered Environment and Its Application as a Navigation Mesh

Path planning for walking characters in complicated virtual environments is a fundamental task in simulations and games. A navigation mesh is a data structure that allows efficient path planning. The Explicit Corridor Map (ECM) is a navigation mesh based on the medial axis. It enables path planning for disk-shaped characters of any radius.
 In this article, we formally extend the medial axis (and therefore the ECM) to 3D environments in which characters are constrained to walkable surfaces. Typical examples of such environments are multi-storey buildings, train stations, and sports stadiums. We give improved definitions of a walkable environment (WE: a description of walkable surfaces in 3D) and a multi-layered environment (MLE: a subdivision of a WE into connected layers). We define the medial axis of such environments based on projected distances on the ground plane. For an MLE with n boundary vertices and k connections, we show that the medial axis has size O(n), and we present an improved algorithm that constructs the medial axis in O(n log n log k) time.
 The medial axis can be annotated with nearest-obstacle information to obtain the ECM navigation mesh. Our implementations show that the ECM can be computed efficiently for large 2D and multi-layered environments and that it can be used to compute paths within milliseconds. This enables simulations of large virtual crowds of heterogeneous characters in real-time.
Gratitude in intermediate affective terrain: links of grateful moods to individual differences and daily emotional experience.

Two studies were conducted to explore gratitude in daily mood and the relationships among various affective manifestations of gratitude. In Study 1, spiritual transcendence and a variety of positive affective traits were related to higher mean levels of gratitude across 21 days. Study 2 replicated these findings and revealed that on days when people had more grateful moods than was typical for them, they also reported more frequent daily episodes of grateful emotions, more intense gratitude per episode, and more people to whom they were grateful than was typical for them. In addition, gratitude as an affective trait appeared to render participants' grateful moods somewhat resistant to the effects of discrete emotional episodes of gratitude.
Classification of Acceleration Data for Biometric Gait Recognition on Mobile Devices

Ubiquitous mobile devices like smartphones and tablets are often not secured against unauthorized access as the users tend to not use passwords because of convenience reasons. Therefore, this study proposes an alternative user authentication method for mobile devices based on gait biometrics. The gait characteristics are captured using the built-in accelerometer of a smartphone. Various features are extracted from the measured accelerations and utilized to train a support vector machine (SVM). Among the extracted features are the Meland Bark-frequency cepstral coefficients (MFCC, BFCC) which are commonly used in speech and speaker recognition and have not been used for gait recognition previously. The proposed approach showed competitive recognition performance, yielding 5.9% FMR at 6.3% FNMR in a mixedday scenario.
MCMLSD: A Dynamic Programming Approach to Line Segment Detection

Prior approaches to line segment detection typically involve perceptual grouping in the image domain or global accumulation in the Hough domain. Here we propose a probabilistic algorithm that merges the advantages of both approaches. In a first stage lines are detected using a global probabilistic Hough approach. In the second stage each detected line is analyzed in the image domain to localize the line segments that generated the peak in the Hough map. By limiting search to a line, the distribution of segments over the sequence of points on the line can be modeled as a Markov chain, and a probabilistically optimal labelling can be computed exactly using a standard dynamic programming algorithm, in linear time. The Markov assumption also leads to an intuitive ranking method that uses the local marginal posterior probabilities to estimate the expected number of correctly labelled points on a segment. To assess the resulting Markov Chain Marginal Line Segment Detector (MCMLSD) we develop and apply a novel quantitative evaluation methodology that controls for under-and over-segmentation. Evaluation on the YorkUrbanDB dataset shows that the proposed MCMLSD method outperforms the state-of-the-art by a substantial margin.
RTIC-C: A Big Data System for Massive Traffic Information Mining

Traffic information system may produce massive and complex traffic data with the process of collecting real-time original GPS (Global Positioning System) data, matching positions to a map and generating traffic flow information, which brings great markets for even-worse traffic condition in China. Howerver, several issues would occur when we reuse these massive traffic data for history data mining applying on-hand database management tools or traditional data processing apporaches, such as massive storage, high performance processing, open interface. "Big Data" system usually includes data sets with sizes beyond the ability of commonly-used software tools to capture, manage, and process the data within a tolerable elapsed time. With this difficulty and the advantage of "Big Data", we schemed RTIC-C system to handle sensemaking over large quantities of traffic data based on cloud computing technique. C designs a distributed data management service to support large scale of data storage, a parallel distributed computing framework for diverse kinds of mining applications based on Map-Reduce mechanism, a restful Web services interface to support third-party mining applications. Experiments on a massive traffic data sets showed that RTIC-C achieves considerable performance comparing with traditional traffic data mining applications.
WALNUT: Waging Doubt on the Integrity of MEMS Accelerometers with Acoustic Injection Attacks

Cyber-physical systems depend on sensors to make automated decisions. Resonant acoustic injection attacks are already known to cause malfunctions by disabling MEMS-based gyroscopes. However, an open question remains on how to move beyond denial of service attacks to achieve full adversarial control of sensor outputs. Our work investigates how analog acoustic injection attacks can damage the digital integrity of a popular type of sensor: the capacitive MEMS accelerometer. Spoofing such sensors with intentional acoustic interference enables an out-of-spec pathway for attackers to deliver chosen digital values to microprocessors and embedded systems that blindly trust the unvalidated integrity of sensor outputs. Our contributions include (1) modeling the physics of malicious acoustic interference on MEMS accelerometers, (2) discovering the circuit-level security flaws that cause the vulnerabilities by measuring acoustic injection attacks on MEMS accelerometers as well as systems that employ on these sensors, and (3) two software-only defenses that mitigate many of the risks to the integrity of MEMS accelerometer outputs. We characterize two classes of acoustic injection attacks with increasing levels of adversarial control: output biasing and output control. We test these attacks against 20 models of capacitive MEMS accelerometers from 5 different manufacturers. Our experiments find that 75% are vulnerable to output biasing, and 65% are vulnerable to output control. To illustrate end-to-end implications, we show how to inject fake steps into a Fitbit with a $5 speaker. In our self-stimulating attack, we play a malicious music file from a smartphone's speaker to control the on-board MEMS accelerometer trusted by a local app to pilot a toy RC car. In addition to offering hardware design suggestions to eliminate the root causes of insecure amplification and filtering, we introduce two low-cost software defenses that mitigate output biasing attacks: randomized sampling and 180 degree out-of-phase sampling. These software-only approaches mitigate attacks by exploiting the periodic and predictable nature of the malicious acoustic interference signal. Our results call into question the wisdom of allowing microprocessors and embedded systems to blindly trust that hardware abstractions alone will ensure the integrity of sensor outputs.
Emotion recognition using mobile phones

The availability of built-in sensors in mobile phones has enabled a host of innovative applications. One class of applications deals with detecting a user's emotions. Previous applications have primarily relied on recording and displaying self-reported emotions. This paper presents an intelligent emotion detection system for mobile phones implemented as a smart keyboard. The smart keyboard independently infers a user's emotional state using machine learning techniques. The system uses accelerometer readings and various aspect of typing behavior like speed, number of backspaces, and time delay between letters to train a classifier to predict emotions. Naïve Bayes, J48, IBK, Multi-response linear regression and SVM were evaluated and J48 was found to be the best classifier with over 90% accuracy and precision. In addition to providing emotive feedback to individual users, the system also uses geo-tagged data to collect and display emotional states of regions or countries through a website.
Survey of Authentication and Authorization for the Internet of Things

The Internet of Things is currently getting significant interest from the scientific community. Academia and industry are both focused on moving ahead in attempts to enhance usability, maintainability, and security through standardization and development of best practices.We focus on security because of its impact as one of themost limiting factors to wider Internet ofThings adoption. Numerous research areas exist in the security domain, ranging from cryptography to network security to identitymanagement.This paper provides a survey of existing research applicable to the Internet of Things environment at the application layer in the areas of identity management, authentication, and authorization. We survey and analyze more than 200 articles, categorize them, and present current trends in the Internet of Things security domain.
TweetCred: A Real-time Web-based System for Assessing Credibility of Content on Twitter

During large scale events, a large volume of content is posted on Twitter, but not all of this content is trustworthy. The presence of spam, advertisements, rumors and fake images reduces the value of information collected from Twitter, especially during sudden-onset crisis events where information from other sources is scarce. In this research work, we describe various facets of assessing the credibility of usergenerated content on Twitter during large scale events, and develop a novel real-time system to assess the credibility of tweets. Firstly, we develop a semi-supervised ranking model using SVM-rank for assessing credibility, based on training data obtained from six high-impact crisis events of 2013. An extensive set of forty-five features is used to determine the credibility score for each of the tweets. Secondly, we develop and deploy a system–TweetCred–in the form of a browser extension, a web application and an API at the link: http://twitdigest.iiitd.edu.in/TweetCred/. To the best of our knowledge, this is the first research work to develop a practical system for credibility on Twitter and evaluate it with real users. TweetCred was installed and used by 717 Twitter users within a span of three weeks. During this period, a credibility score was computed for more than 1.1 million unique tweets. Thirdly, we evaluated the real-time performance of TweetCred , observing that 84% of the credibility scores were displayed within 6 seconds. We report on the positive feedback that we received from the system’s users and the insights we gained into improving the system for future iterations.
Author ' s personal copy Romancing leadership : Past , present , and future ☆

Available online 2 October 2011 This paper presents a review of the romance of leadership and the social construction of leadership theory 25 years after it was originally introduced. We trace the development of this theoretical approach from the original formulation of the romance of leadership (RoL) theory as attributional bias through its emergence as a radical, unconventional approach that views leadership as a sensemaking activity that is primarily ‘in the eye of the beholder.’ We subsequently review research published in management and organizational psychology journals, book chapters and special issues of journals from 1985 to 2010. Three overall themes emerged from this review: 1) biases in (mis)attributions of leadership, including attributions for organizational success and failure; 2) follower-centered approaches, including the role of follower characteristics, perceptions, and motivations in interpreting leadership ratings; and 3) the social construction of leadership, including interfollower and social contagion processes, the role of crisis and uncertainty, and constructions and deconstructions of leadership and CEO celebrity in the media. Within each of these themes, we examine developments and summarize key findings. Our review concludes with recommendations for future theoretical and empirical
Probabilistic Student Models: Bayesian Belief Networks and Knowledge Space Theory

The applicability of Knowledge Space Theory (Falmagne and Doignon) and Bayesian Belief Networks (Pearl) as probabilistic student models imbedded in an Intelligent Tutoring System is examined. Student modeling issues such as knowledge representation, adaptive assessment, curriculum advancement, and student feedback are addressed. Several factors contribute to uncertainty in student modeling such as careless errors and lucky guesses, learning and forgetting, and unanticipated student response patterns. However, a probabilistic student model can represent uncertainty regarding the estimate of the student's knowledge and can be tested using empirical student data and established statistical techniques.
IETF Standardization in the Field of the Internet of Things (IoT): A Survey

Smart embedded objects will become an important part of what is called the Internet of Things. However, the integration of embedded devices into the Internet introduces several challenges, since many of the existing Internet technologies and protocols were not designed for this class of devices. In the past few years, there have been many efforts to enable the extension of Internet technologies to constrained devices. Initially, this resulted in proprietary protocols and architectures. Later, the integration of constrained devices into the Internet was embraced by IETF, moving towards standardized IP-based protocols. In this paper, we will briefly review the history of integrating constrained devices into the Internet, followed by an extensive overview of IETF standardization work in the 6LoWPAN, ROLL and CoRE working groups. This is complemented with a broad overview of related research results that illustrate how this work can be extended or used to tackle other problems and with a discussion on open issues and challenges. As such the aim of this paper is twofold: apart from giving readers solid insights in IETF standardization work on the Internet of Things, it also aims to encourage readers to further explore the world of Internet-connected objects, pointing to future research opportunities.  2013, 2 236
Studies of fingerprint matching using the NIST Verification Test Bed (VTB)

A series of fingerprint matching studies have been conducted on an experimental laboratory system called the Verification Test Bed (VTB). The VTB is a collection of commercial off the shelf (COTS) computer hardware, an open-source operating system, and a suite of public domain application software. Results are presented that compare various sources of fingerprints, assess the image quality of fingerprints by analyzing the matcher scores for inked and live-scan impressions, and study the trade-offs of matching rolled fingerprints with plain impressions. Database size in these studies range from 216 to ~600,000 people. Performance statistics are primarily reported for single-finger matching; however, results from two different approaches to two-finger fusion matching are also presented. At a false accept rate (FAR) of 1%, the best twofinger true accept rate (TAR) is 99% while the worst single-finger TAR is 71%. This report illustrates the wide range of image types and quality that exist in government fingerprint databases and the effect this variability has on the accuracy of matching using a single algorithm. An appendix compares the VTB matcher to two commercial fingerprint systems and concludes that the performance of the VTB is very similar to commercial verification systems currently on the market. This further confirms that data quality uniformity is of paramount importance in the evaluation of fingerprint biometrics.
Familiarity, Confidence, Trust: Problems and Alternatives

Trust has never been a topic of mainstream sociology. Neither classical authors nor modern sociologists use the term in a theoretical context. For this reason the elaboration of theoretical frameworks, one of the main sources of conceptual clarification, has been relatively neglected. Furthermore, empirical research for example, research about trust and distrust in politics has relied on rather general and unspecified ideas, confusing problems of trust with positive or negative attitudes toward political leadership or political institutions, with alienation (itself a multidimensional concept), with hopes and worries, or with confidence. In their monograph on patrons, clients, and friends, Shmuel Eisenstadt and Luis Roniger (1984) use the concept of trust as roughly equivalent to solidarity, meaning, and participation. This makes it possible to show that unconditional trust is generated in families and small-scale societies and cannot be automatically transferred to complex societies based on the division of labour. Trust, then, needs for its reconstruction special social institutions; friendship networks and patron-client relations are examples for this adaptation. But this is merely to reiterate well-known statements about the division of labour and the need to reconstruct solidarity, about Gesellschaft and Gemeinschaft. It does not give any new insight into the particularities of trusting relations. To gain such insights we need further conceptual clarification.
Coupled segmentation and similarity detection for architectural models

Recent shape retrieval and interactive modeling algorithms enable the re-use of existing models in many applications. However, most of those techniques require a pre-labeled model with some semantic information. We introduce a fully automatic approach to simultaneously segment and detect similarities within an existing 3D architectural model. Our framework approaches the segmentation problem as a weighted minimum set cover over an input triangle soup, and maximizes the repetition of similar segments to find a best set of unique component types and instances. The solution for this set-cover formulation starts with a search space reduction to eliminate unlikely combinations of triangles, and continues with a combinatorial optimization within each disjoint subspace that outputs the components and their types. We show the discovered components of a variety of architectural models obtained from public databases. We demonstrate experiments testing the robustness of our algorithm, in terms of threshold sensitivity, vertex displacement, and triangulation variations of the original model. In addition, we compare our components with those of competing approaches and evaluate our results against user-based segmentations. We have processed a database of 50 buildings, with various structures and over 200K polygons per building, with a segmentation time averaging up to 4 minutes.
A Meta-Model for the Analysis and Design of Organizations in Multi-Agent Systems

This paper presents a generic meta-model of multi-agent systems based on organizational concepts such as groups, roles and structures. This model, called AALAADIN, defines a very simple description of coordination and negotiation schemes through multi-agent systems. Aalaadin is a meta-model of artificial organization by which one can build multi-agent systems with different forms of organizations such as market-like and hierarchical organizations. We show that this meta-model allows for agent heterogeneity in languages, applications and architectures. We also introduce the concept of organizational reflection which uses the same conceptual model to describe systemlevel tasks such as remote communication and migration of agents. Finally, we briefly describe a platform, called MADKIT, based on this model. It relies on a minimal agent kernel with platform-level services implemented as agents, groups and roles.
Design for license plate RFID tag antenna with dual resonance to improve identification ratio

The resonance frequency and bandwidth of the proposed active and passive antenna are about 434 MHz (260 MHz ~ 600 MHz) and 900 MHz (850 MHz ~ 960 MHz), respectively. The measured return loss and radiation pattern matched the result of calculation well. The measured readable range of tag antenna with car bumper which contained the poly urethane was approximately 8.5 m.