nils-herrmann/clean_scirepeval huggingface dataset:
Advanced Quaternion Forward Kinematics Algorithm Including Overview of Different Methods for Robot Kinematics

Formulation of proper and efficient algorithms for robot kinematics is essential for the analysis and design of serial manipulators. Kinematic modeling of manipulators is most often performed in Cartesian space. However, due to disadvantages of most widely used mathematical constructs for description of orientation such as Euler angles and rotational matrices, a need for unambiguous, compact, singularity free, computationally efficient method for representing rotational information is imposed. As a solution, unit quaternions are proposed and kinematic modeling in dual quaternion space arose. In this paper, an overview of spatial descriptions and transformations that can be applied together within these spaces in order to solve kinematic problems is presented. Special emphasis is on a different mathematical formalisms used to represent attitude of a rigid body such as rotation matrix, Euler angles, axis-angle representation, unit quaternions, and their mutual relation. Benefits of kinematic modeling in quaternion space are presented. New direct kinematics algorithm in dual quaternion space pertaining to a particular manipulator is given. These constructs and algorithms are demonstrated on the human centrifuge as 3 DoF robot manipulator.
Cross-layer modeler: a tool for flexible multilevel modeling with consistency checking

Model-driven engineering has become a popular methodology in software engineering. Most available modeling tools support the creation of models based on a fixed metamodel. Typically, tool users cannot change the metamodel to reflect domain changes or newly emerged requirements. As a consequence, an updated version of the tool with an evolved metamodel must be developed and models as well as constraints that ensure model consistency have to be co-evolved, often manually, to conform to the new metamodel. Both, tool evolution and the necessary co-evolutions, are time consuming and error prone tasks. Furthermore, common tools often restrict the number of metalevels that can be modeled and force modelers to use workarounds to express certain facts. To overcome these issues we present the Cross-Layer Modeler (XLM), a modeling tool that supports multilevel modeling and allows co-evolution of metamodels and models. The XLM automatically performs co-evolution of constraints and gives instant feedback about model consistency. We illustrate the novel modeling approach of our tool and discuss its main capabilities.
RANDy-A True-Random Generator Based On Radioactive Decay

This paper presents a physical random number generator for mainly cryptographical applications based on alpha decay of Americium 241. A simple and low-cost implementation is shown to detect the decay events of a radioactive source often found in common household smoke detectors. Three different algorithms for the extraction of random bits from the exponentially distributed impulses are discussed. In the concrete application a speed optimized method was chosen to gain a reasonable high data rate from a moderate radiation source (0,1 μCi). To the author’s best knowledge this technique has not been applied so far in the context of radiation-based random generators. A tentative application of statistical suits of tests indicates a high quality of the data delivered by the device.
Analysis of a radial flux hysteresis IPM motor

Design of self-start permanent magnet motors over wide operating range has been a challenging topic of interest among the researchers over the last two decades. This paper presents the design and finite element analysis of a novel 1HP 208V 3-phase 4-pole line start radial flux hysteresis interior permanent magnet (IPM) motor. The proposed motor is a solid-rotor self-starting hybrid synchronous motor combining both hysteresis phenomena and permanent magnet excitation inside the rotor. The transient performance analysis results of the hysteresis IPM motor are presented and explained in this paper. Simulation results exhibit that the proposed hysteresis IPM motor has smooth starting and good synchronization capabilities.
An intraindividual process approach to the relationship between extraversion and positive affect: is acting extraverted as "good" as being extraverted?

This article investigates whether rapid variation within a person in extraversion is associated with positive affect variation in that person. In Study 1, participants reported their extraversion and positive affect every 3 hr for 2 weeks. Each participant was happier when acting extraverted than when acting introverted. Study 2's diary methodology replicated the relationship for weekly variations in positive affect. Study 3's experimental methodology replicated the relationship when extraversion was manipulated within a fixed situation. Thus, the relationship between extraversion and positive affect, previously demonstrated between persons, also characterizes the internal, ongoing psychological functioning of individuals and is likely to be explained by something capable of rapid intraindividual variation. Furthermore, traits and states are at least somewhat isomorphic, and acting extraverted may increase well-being.
Comparison of brazilian spiritist mediumship and dissociative identity disorder.

We studied the similarities and differences between Brazilian Spiritistic mediums and North American dissociative identity disorder (DID) patients. Twenty-four mediums selected among different Spiritistic organizations in São Paulo, Brazil, were interviewed using the Dissociative Disorder Interview Schedule, and their responses were compared with those of DID patients described in the literature. The results from Spiritistic mediums were similar to published data on DID patients only with respect to female prevalence and high frequency of Schneiderian first-rank symptoms. As compared with individuals with DID, the mediums differed in having better social adjustment, lower prevalence of mental disorders, lower use of mental health services, no use of antipsychotics, and lower prevalence of histories of physical or sexual childhood abuse, sleepwalking, secondary features of DID, and symptoms of borderline personality. Thus, mediumship differed from DID in having better mental health and social adjustment, and a different clinical profile.
Deep Reinforcement Learning for Inquiry Dialog Policies with Logical Formula Embeddings

This paper is the first attempt to learn the policy of an inquiry dialog system (IDS) by using deep reinforcement learning (DRL). Most IDS frameworks represent dialog states and dialog acts with logical formulae. In order to make learning inquiry dialog policies more effective, we introduce a logical formula embedding framework based on a recursive neural network. The results of experiments to evaluate the effect of 1) the DRL and 2) the logical formula embedding framework show that the combination of the two are as effective or even better than existing rule-based methods for inquiry dialog policies.
Portability of an RF fingerprint of a wireless transmitter

In conventional wireless networks, security issues are primarily considered above the physical layer and are usually based on bit-level algorithms to establish the identity of a legitimate wireless device. Physical layer security is a new paradigm in which features extracted from an analog signal can be used to establish the unique identity of a transmitter. Our previous research work into RF fingerprinting has shown that every transmitter has a unique RF fingerprint owing to imperfections in the analog components present in the RF front end. Generally, it is believed that the RF fingerprint of a specific transmitter is same across all receivers. That is, a fingerprint created in one receiver can be transported to another receiver to establish the identity of a transmitter. However, to the best of the author's knowledge, no such example is available in the literature in which an RF fingerprint generated in one receiver is used for identification in other receivers. This paper presents the results of experiments, and analyzing the feasibility of using an universal RF fingerprint of a transmitter for identification across different receivers.
Survey of Graph Matching Algorithms

Graph matching problems of varying types are important in a wide array of application areas. A graph matching problem is a problem involving some form of comparison between graphs. Some of the many application areas of such problems include information retrieval, sub-circuit identification, chemical structure classification, and networks. Problems of efficient graph matching arise in any field that may be modeled with graphs. For example, any problem that can be modeled with binary relations between entities in the domain is such a problem. The individual entities in the problem domain become nodes in the graph. And each binary relation becomes an edge between the appropriate nodes. Although it is possible to formulate such a large array of problems as graph matching problems, it is not necessarily a good idea to do so. Graph matching is a very difficult problem. The graph isomorphism problem is to determine if there exists a one-to-one mapping from the nodes of one graph to the nodes of a second graph that preserves adjacency. Similarly, the subgraph isomorphism problem is to determine if there exists a one-to-one mapping from the
Quasar: a probabilistic publish-subscribe system for social networks

Existing peer-to-peer publish-subscribe systems rely on structured-overlays and rendezvous nodes to store and relay group membership information. While conceptually simple, this design incurs the significant cost of creating and maintaining rigid-structures and introduces hotspots in the system at nodes that are neither publishers nor subscribers. In this paper, we introduce Quasar, a rendezvous-less probabilistic publish-subscribe system that caters to the specific needs of social networks. It is designed to handle social networks of many groups; on the order of the number of users in the system. It creates a routing infrastructure based on the proactive dissemination of highly aggregated routing vectors to provide anycast-like directed walks in the overlay. This primitive, when coupled with a novel mechanism for dynamically negating routes, enables scalable and efficient group-multicast that obviates the need for structure and rendezvous nodes. We examine the feasibility of this approach and show in a large-scale simulation that the system is scalable and efficient.
Opportunistic message routing using multi-layer social networks

In opportunistic networks, the nodes usually exploit a contact opportunity to perform hop-by-hop routing, since an end-to-end path between the source node and destination node may not exist. Most social-based routing protocols use social information extracted from real-world encounter networks to select an appropriate message relay. A protocol based on encounter history, however, takes time to build up a knowledge database from which to take routing decisions. An opportunistic routing protocol which extracts social information from multiple social networks, can be an alternative approach to avoid suboptimal paths due to partial information on encounters. While contact information changes constantly and it takes time to identify strong social ties, online social network ties remain rather stable and can be used to augment available partial contact information. In this paper, we propose a novel opportunistic routing approach, called ML-SOR (Multi-layer Social Network based Routing), which extracts social network information from multiple social contexts. To select an effective forwarding node, ML-SOR measures the forwarding capability of a node when compared to an encountered node in terms of node centrality, tie strength and link prediction. These metrics are computed by ML-SOR on different social network layers. Trace driven simulations show that ML-SOR, when compared to other schemes, is able to deliver messages with high probability while keeping overhead ratio very small.
Do Early Educators ’ Implicit Biases Regarding Sex and Race Relate to Behavior Expectations and Recommendations of Preschool Expulsions and Suspensions ? A RESEARCH STUDY

1 This is a research brief providing basic information regarding the methods, findings and implications from the described study, for presentation at the U. Acknowledgements: This work was completed through a generous grant from the W.K. Kellogg Foundation. All photos of children are presented with permission of their parents. The authors also wish to acknowledge Amalia Londono Tobon, MD and Sarah Grossman-Kahn for their assistance in collecting the data and preparing the videos for analysis; Dan Huber for filming the videos used in this study; and useful comments from Abstract Preschool expulsions and the disproportionate expulsion of Black boys have gained attention in recent years, but little has been done to understand the underlying causes behind this issue. This study examined the potential role of preschool educators' implicit biases as a viable partial explanation behind disparities in preschool expulsions. Participants were recruited at a large conference of early educators and completed two tasks. In Task 1, participants were primed to expect challenging behaviors (although none were present) while watching a video of preschoolers, balanced by sex and race, engaging in typical activities, as the participants' eye gazes were tracked. In Task 2, participants read a standardized vignette of a preschooler with challenging behavior and were randomized to receive the vignette with the child's name implying either a Black boy, Black girl, White boy, or White girl, as well as randomized to receive the vignette with or without background information on the child's family environment. Findings revealed that when expecting challenging behaviors teachers gazed longer at Black children, especially Black boys. Findings also suggested that implicit biases may differ depending on teacher race. Providing family background information resulted in lowered severity ratings when teacher and child race matched, but resulted in increased severity ratings when their race did not match. No differences were found based on recommendations regarding suspension or expulsion, except that Black teachers in general recommended longer periods of disciplinary exclusion regardless of child gender/race. Recommendations for future research and policy regarding teacher training are offered. Preschool expulsions and suspensions cause young children to lose their early educational placement or time in care, directly undermining their access to educational opportunities. This " push out " phenomenon has become increasingly concerning to the field especially given the disproportionate rates of early childhood expulsions for boys, Blacks, and particularly Black boys. Black preschoolers are 3.6 times as likely to receive one …
Semantic analysis of song lyrics

We explore the use of song lyrics for automatic indexing of music. Using lyrics mined from the Web, we apply a standard text processing technique to characterize their semantic content. We then determine artist similarity in this space. We found lyrics can be used to discover natural genre clusters. Experiments on a publicly available set of 399 artists showed that determining artist similarity using lyrics is better than random, but inferior to a state-of-the-art acoustic similarity technique. However the approaches made different errors, suggesting they could be profitably combined
Data mining approach to analyse the road accidents in India

Despite all that has been done to promote Road Safety in India so far, there are always regions that fall prey to the vulnerabilities that linger on in every corner. The heterogeneity of these vulnerability-inducing causes leads to the need for an effective analysis so as to subdue the alarming figures by a significant amount. The objective of this paper is to have data mining to come to aid to create a model that not only smooths out the heterogeneity of the data by grouping similar objects together to find the accident prone areas in the country with respect to different accident-factors but also helps determine the association between these factors and casualties.
Challenges to Internet e-banking

The extension of money and banking to the cyberspace is an inevitable development in the information age. Over the past few years, many financial institutions have launched e-retail banking over the Internet. Given the requirements of matching marginal gains against marginal costs, evaluating the profitability of market development along specific dimensions and segments, and determining whether the new technology would be accepted, it is imperative that this decision is continually re-evaluated. Commercial banks face significant challenges on both the supply side and demand side, associated in particular with competition, product-service quality and differentiation, transaction security, cost efficiency, and demographic change. As is generally the case in financial innovation, competition is the driving force behind the introduction of Internet e-retail banking. Primarily, commercial banks viewed it as a competitive strategy to retain existing customers, attract additional business, increase market share, and support business re-engineering. Since many of the products and services are newly emerged from R&D departments, supply must be adapted to demand and consumer acceptance. In oligopolistic markets, it is necessary for the e-bank to compete in the first place through the sub-strategy of product-service differentiation. The financial products and services supplied over the Internet must not only be tailored to fulfill wants, preferences, and quality expectations at the present time, but are also required to induce consumers to demand their technological progeny in the future. Expertise in marketing and the ability to anticipate changes in market conditions are therefore crucial to the success of Internet e-retail banking. In particular, strategic priority must be assigned to the establishment of brand name and brand identification. The Internet is exploited as a channel to build, maintain, and develop long-term client relationships through ready access to a broad and increasing array of products, services and low-cost financial shopping, rapid response to customer inquiries, and personalized product-service innovation. Success in this endeavor would enhance product-service differentiation, which would in turn strengthen customer loyalty, promote cross-selling, increase repeated purchases, and attract new business.
Interactive Facial Feature Localization

We address the problem of interactive facial feature localization from a single image. Our goal is to obtain an accurate segmentation of facial features on high-resolution images under a variety of pose, expression, and lighting conditions. Although there has been significant work in facial feature localization, we are addressing a new application area, namely to facilitate intelligent high-quality editing of portraits, that brings requirements not met by existing methods. We propose an improvement to the Active Shape Model that allows for greater independence among the facial components and improves on the appearance fitting step by introducing a Viterbi optimization process that operates along the facial contours. Despite the improvements, we do not expect perfect results in all cases. We therefore introduce an interaction model whereby a user can efficiently guide the algorithm towards a precise solution. We introduce the Helen Facial Feature Dataset consisting of annotated portrait images gathered from Flickr that are more diverse and challenging than currently existing datasets. We present experiments that compare our automatic method to published results, and also a quantitative evaluation of the effectiveness of our interactive method.
Community Mining from Signed Social Networks

Many complex systems in the real world can be modeled as signed social networks that contain both positive and negative relations. Algorithms for mining social networks have been developed in the past; however, most of them were designed primarily for networks containing only positive relations and, thus, are not suitable for signed networks. In this work, we propose a new algorithm, called FEC, to mine signed social networks where both positive within-group relations and negative between-group relations are dense. FEC considers both the sign and the density of relations as the clustering attributes, making it effective for not only signed networks but also conventional social networks including only positive relations. Also, FEC adopts an agent-based heuristic that makes the algorithm efficient (in linear time with respect to the size of a network) and capable of giving nearly optimal solutions. FEC depends on only one parameter whose value can easily be set and requires no prior knowledge on hidden community structures. The effectiveness and efficacy of FEC have been demonstrated through a set of rigorous experiments involving both benchmark and randomly generated signed networks.
Multipurpose Reservoir Operation Using Particle Swarm Optimization

This paper presents an efficient and reliable swarm intelligence-based approach, namely elitist-mutated particle swarm optimization EMPSO technique, to derive reservoir operation policies for multipurpose reservoir systems. Particle swarm optimizers are inherently distributed algorithms, in which the solution for a problem emerges from the interactions between many simple individuals called particles. In this study the standard particle swarm optimization PSO algorithm is further improved by incorporating a new strategic mechanism called elitist-mutation to improve its performance. The proposed approach is first tested on a hypothetical multireservoir system, used by earlier researchers. EMPSO showed promising results, when compared with other techniques. To show practical utility, EMPSO is then applied to a realistic case study, the Bhadra reservoir system in India, which serves multiple purposes, namely irrigation and hydropower generation. To handle multiple objectives of the problem, a weighted approach is adopted. The results obtained demonstrate that EMPSO is consistently performing better than the standard PSO and genetic algorithm techniques. It is seen that EMPSO is yielding better quality solutions with less number of function evaluations.  2007 133:3 192 CE Database subject headings: Reservoir operation; Optimization; Irrigation; Hydroelectric power generation.
Tweeting Your Mental Health: an Exploration of Different Classifiers and Features with Emotional Signals in Identifying Mental Health Conditions

Applying simple natural language processing methods on social media data have shown to be able to reveal insights of specific mental disorders. However, few studies have employed fine-grained sentiment or emotion related analysis approaches in the detection of mental health conditions from social media messages. This work, for the first time, employed fine-grained emotions as features and examined five popular machine learning classifiers in the task of identifying users with selfreported mental health conditions (i.e. Bipolar, Depression, PTSD, and SAD) from the general public. We demonstrated that the support vector machines and the random forests classifiers with emotion-based features and combined features showed promising improvements to the performance on this task.