nils-herrmann/clean_scirepeval huggingface dataset:
The effect of avatar realism in immersive social virtual realities

This paper investigates the effect of avatar realism on embodiment and social interactions in Virtual Reality (VR). We compared abstract avatar representations based on a wooden mannequin with high fidelity avatars generated from photogrammetry 3D scan methods. Both avatar representations were alternately applied to participating users and to the virtual counterpart in dyadic social encounters to examine the impact of avatar realism on self-embodiment and social interaction quality. Users were immersed in a virtual room via a head mounted display (HMD). Their full-body movements were tracked and mapped to respective movements of their avatars. Embodiment was induced by presenting the users' avatars to themselves in a virtual mirror. Afterwards they had to react to a non-verbal behavior of a virtual interaction partner they encountered in the virtual space. Several measures were taken to analyze the effect of the appearance of the users' avatars as well as the effect of the appearance of the others' avatars on the users. The realistic avatars were rated significantly more human-like when used as avatars for the others and evoked a stronger acceptance in terms of virtual body ownership (VBO). There also was some indication of a potential uncanny valley. Additionally, there was an indication that the appearance of the others' avatars impacts the self-perception of the users.
Word Meaning and Commonsense Metaphysics

In this paper, we outline a model of semantics that integrates aspects of discourse-sensitive logics with the compositional mechanisms available from lexically-driven semantic interpretation. Specifically, we concentrate on developing a composition logic required to properly model complex types within the Generative Lexicon (henceforth GL), for which we employ SDRT principles. As we are presently interested in the composition of information to construct logical forms, we will build on one standard way of arriving at such representations, the lambda calculus, in which functional types are exploited. We outline a new type calculus that captures one of the fundamental ideas of GL: providing a set of techniques governing type shifting possibilities for various lexical items so as to allow for the combination of lexical items in cases where there is an apparent type mismatch. These techniques themselves should follow from the structure of the lexicon and its underlying logic.
Office microlaparoscopic ovarian drilling (OMLOD) versus conventional laparoscopic ovarian drilling (LOD) for women with polycystic ovary syndrome

This was a prospective controlled study to compare the beneficial effects of office microlaparoscopic ovarian drilling (OMLOD) under augmented local anesthesia, as a new modality treatment option, compared to those following ovarian drilling with the conventional traditional 10-mm laparoscope (laparoscopic ovarian drilling, LOD) under general anesthesia. The study included 60 anovulatory women with polycystic ovary syndrome (PCOS) who underwent OMLOD (study group) and 60 anovulatory PCOS women, in whom conventional LOD using 10-mm laparoscope under general anesthesia was performed (comparison group). Transvaginal ultrasound scan and blood sampling to measure the serum concentrations of LH, FSH, testosterone and androstenedione were performed before and after the procedure. Intraoperative and postoperative pain scores in candidate women were evaluated during the office microlaparoscopic procedure, in addition to the number of candidates who needed extra analgesia. Women undergoing OMLOD showed good intraoperative and postoperative pain scores. The number of patients discharged within 2 h after the office procedure was significantly higher, without the need for postoperative analgesia in most patients. The LH:FSH ratio, mean serum concentrations of LH and testosterone and free androgen index decreased significantly after both OMLOD and LOD. The mean ovarian volume decreased significantly (P < 0.05) a year after both OMLOD and LOD. There were no significant differences in those results after both procedures. Intra- and postoperatively augmented local anesthesia allows outpatient bilateral ovarian drilling by microlaparoscopy without general anesthesia. The high pregnancy rate, the simplicity of the method and the faster discharge time offer a new option for patients with PCOS who are resistant to clomiphene citrate. Moreover, ovarian drilling could be performed simultaneously during the routine diagnostic microlaparoscopy and integrated into the fertility workup of these patients.
Cognitive management framework for Internet of Things: — A prototype implementation

In the domain of Internet of Things (IoT), applications are modeled to understand and react based on existing contextual and situational parameters. This work implements a management flow for the abstraction of real world objects and virtual composition of those objects to provide IoT services. We also present a real world knowledge model that aggregates constraints defining a situation, which is then used to detect and anticipate future potential situations. It is implemented based on reasoning and machine learning mechanisms. This work showcases a prototype implementation of the architectural framework in a smart home scenario, targeting two functionalities: actuation and automation based on the imposed constraints and thereby responding to situations and also adapting to the user preferences. It thus provides a productive integration of heterogeneous devices, IoT platforms, and cognitive technologies to improve the services provided to the user.
Machine Learning of Temporal Relations

This paper investigates a machine learning approach for temporally ordering and anchoring events in natural language texts. To address data sparseness, we used temporal reasoning as an oversampling method to dramatically expand the amount of training data, resulting in predictive accuracy on link labeling as high as 93% using a Maximum Entropy classifier on human annotated data. This method compared favorably against a series of increasingly sophisticated baselines involving expansion of rules derived from human intuitions.
Evidence of a Double Burden of Malnutrition in Urban Poor Settings in Nairobi, Kenya

Many low- and middle-income countries are undergoing a nutrition transition associated with rapid social and economic transitions. We explore the coexistence of over and under- nutrition at the neighborhood and household level, in an urban poor setting in Nairobi, Kenya.



Data were collected in 2010 on a cohort of children aged under five years born between 2006 and 2010. Anthropometric measurements of the children and their mothers were taken. Additionally, dietary intake, physical activity, and anthropometric measurements were collected from a stratified random sample of adults aged 18 years and older through a separate cross-sectional study conducted between 2008 and 2009 in the same setting. Proportions of stunting, underweight, wasting and overweight/obesity were dettermined in children, while proportions of underweight and overweight/obesity were determined in adults.



Of the 3335 children included in the analyses with a total of 6750 visits, 46% (51% boys, 40% girls) were stunted, 11% (13% boys, 9% girls) were underweight, 2.5% (3% boys, 2% girls) were wasted, while 9% of boys and girls were overweight/obese respectively. Among their mothers, 7.5% were underweight while 32% were overweight/obese. A large proportion (43% and 37%%) of overweight and obese mothers respectively had stunted children. Among the 5190 adults included in the analyses, 9% (6% female, 11% male) were underweight, and 22% (35% female, 13% male) were overweight/obese.



The findings confirm an existing double burden of malnutrition in this setting, characterized by a high prevalence of undernutrition particularly stunting early in life, with high levels of overweight/obesity in adulthood, particularly among women. In the context of a rapid increase in urban population, particularly in urban poor settings, this calls for urgent action. Multisectoral action may work best given the complex nature of prevailing circumstances in urban poor settings. Further research is needed to understand the pathways to this coexistence, and to test feasibility and effectiveness of context-specific interventions to curb associated health risks.
Radio Link Planning made easy with a Telegram Bot

Traditional radio planning tools present a steep learning curve. We present BotRf, a Telegram Bot that facilitates the process by guiding non-experts in assessing the feasibility of radio links. Built on open source tools, BotRf can run on any smartphone or PC running Telegram. Using it on a smartphone has the added value that the Bot can leverage the internal GPS to enter coordinates. BotRf can be used in environments with low bandwidth as the generated data traffic is quite limited. We present examples of its use in Venezuela.
Big data emerging technologies: A CaseStudy with analyzing twitter data using apache hive

These are the days of Growth and Innovation for a better future. Now-a-days companies are bound to realize need of Big Data to make decision over complex problem. Big Data is a term that refers to collection of large datasets containing massive amount of data whose size is in the range of Petabytes, Zettabytes, or with high rate of growth, and complexity that make them difficult to process and analyze using conventional database technologies. Big Data is generated from various sources such as social networking sites like Facebook, Twitter etc, and the data that is generated can be in various formats like structured, semi-structured or unstructured format. For extracting valuable information from this huge amount of Data, new tools and techniques is a need of time for the organizations to derive business benefits and to gain competitive advantage over the market. In this paper a comprehensive study of major Big Data emerging technologies by highlighting their important features and how they work, with a comparative study between them is presented. This paper also represents performance analysis of Apache Hive query for executing Twitter tweets in order to calculate Map Reduce CPU time spent and total time taken to finish the job.
Winding Configurations and Optimal Stator and Rotor Pole Combination of Flux-Switching PM Brushless AC Machines

A simple analytical method is developed to compare the combinations of stator and rotor pole numbers in flux-switching permanent magnet (PM) machines in terms of back electromotive force (EMF) and electromagnetic torque. The winding connections and winding factors of machines having all poles and alternate poles wound, and different numbers of phases, from two to six, are determined by the coil-EMF vectors. Their differences from analyzing the conventional fractional-slot PM machines with concentrated nonoverlapping windings are highlighted. The general conditions are established for balanced symmetrical back-EMF waveform. It shows that the optimized rotor pole number should be close to the number of stator poles, whereas larger torque can be obtained by the machine with relatively higher rotor pole number. The analysis is validated by finite-element analyses and experiment.
LDA v. LSA: A Comparison of Two Computational Text Analysis Tools for the Functional Categorization of Patents

One means to support for design-by-analogy (DbA) in practice involves giving designers efficient access to source analogies as inspiration to solve problems. The patent database has been used for many DbA support efforts, as it is a preexisting repository of catalogued technology. Latent Semantic Analysis (LSA) has been shown to be an effective computational text processing method for extracting meaningful similarities between patents for useful functional exploration during DbA. However, this has only been shown to be useful at a small-scale (100 patents). Considering the vastness of the patent database and realistic exploration at a largescale, it is important to consider how these computational analyses change with orders of magnitude more data. We present analysis of 1,000 random mechanical patents, comparing the ability of LSA to Latent Dirichlet Allocation (LDA) to categorize patents into meaningful groups. Resulting implications for large(r) scale data mining of patents for DbA support are detailed.
Footprint-based personal recognition

Proposes a new method of personal recognition based on footprints. In this method, an input pair of raw footprints is normalized, both in direction and in position for robustness image-matching between the input pair of footprints and the pair of registered footprints. In addition to the Euclidean distance between them, the geometric information of the input footprint is used prior to the normalization, i.e., directional and positional information. In the experiment, the pressure distribution of the footprint was measured with a pressure-sensing mat. Ten volunteers contributed footprints for testing the proposed method. The recognition rate was 30.45% without any normalization (i.e., raw image), and 85.00% with the authors' method.
Attention-Aware Compositional Network for Person Re-identification

Person re-identification (ReID) is to identify pedestrians observed from different camera views based on visual appearance. It is a challenging task due to large pose variations, complex background clutters and severe occlusions. Recently, human pose estimation by predicting joint locations was largely improved in accuracy. It is reasonable to use pose estimation results for handling pose variations and background clutters, and such attempts have obtained great improvement in ReID performance. However, we argue that the pose information was not well utilized and hasn't yet been fully exploited for person ReID. In this work, we introduce a novel framework called Attention-Aware Compositional Network (AACN) for person ReID. AACN consists of two main components: Pose-guided Part Attention (PPA) and Attention-aware Feature Composition (AFC). PPA is learned and applied to mask out undesirable background features in pedestrian feature maps. Furthermore, pose-guided visibility scores are estimated for body parts to deal with part occlusion in the proposed AFC module. Extensive experiments with ablation analysis show the effectiveness of our method, and state-of-the-art results are achieved on several public datasets, including Market-1501, CUHK03, CUHK01, SenseReID, CUHK03-NP and DukeMTMC-reID.
Cancer treatment and survivorship statistics, 2012.

Although there has been considerable progress in reducing cancer incidence in the United States, the number of cancer survivors continues to increase due to the aging and growth of the population and improvements in survival rates. As a result, it is increasingly important to understand the unique medical and psychosocial needs of survivors and be aware of resources that can assist patients, caregivers, and health care providers in navigating the various phases of cancer survivorship. To highlight the challenges and opportunities to serve these survivors, the American Cancer Society and the National Cancer Institute estimated the prevalence of cancer survivors on January 1, 2012 and January 1, 2022, by cancer site. Data from Surveillance, Epidemiology, and End Results (SEER) registries were used to describe median age and stage at diagnosis and survival; data from the National Cancer Data Base and the SEER-Medicare Database were used to describe patterns of cancer treatment. An estimated 13.7 million Americans with a history of cancer were alive on January 1, 2012, and by January 1, 2022, that number will increase to nearly 18 million. The 3 most prevalent cancers among males are prostate (43%), colorectal (9%), and melanoma of the skin (7%), and those among females are breast (41%), uterine corpus (8%), and colorectal (8%). This article summarizes common cancer treatments, survival rates, and posttreatment concerns and introduces the new National Cancer Survivorship Resource Center, which has engaged more than 100 volunteer survivorship experts nationwide to develop tools for cancer survivors, caregivers, health care professionals, advocates, and policy makers.
Application of quantitative real-time PCR for rapid identification of Bacteroides fragilis group and related organisms in human wound samples.

Our goal was to establish a quantitative real-time PCR (QRT-PCR) method to detect Bacteroides fragilis group and related organisms from clinical specimens. Compared to conventional anaerobic culture, QRT-PCR can provide accurate and more rapid detection and identification of B. fragilis group and similar species. B. fragilis group and related organisms are the most frequently isolated anaerobic pathogens from clinical samples. However, culture and phenotypic identification is quite time-consuming. We designed specific primers and probes based on the 16S rRNA gene sequences of Bacteroides caccae, Bacteroides eggerthii, B. fragilis, Bacteroides ovatus, Bacteroides stercoris, Bacteroides thetaiotaomicron, Bacteroides uniformis, Bacteroides vulgatus, Odoribacter splanchnicus (Bacteroides splanchnicus), Parabacteroides distasonis (Bacteroides distasonis) and Parabacteroides merdae (Bacteroides merdae), and detected these species by means of QRT-PCR in 400 human surgical wound infection samples or closed abscesses. The target bacteria were detected from 31 samples (8%) by culture, but from 132 samples (33%) by QRT-PCR (p-value < 0.001). B. uniformis was the most common species (44 positive samples) according to QRT-PCR while culture showed it to be B. fragilis (16 positive samples). Additionally, for each species QRT-PCR detected higher counts than culture did; this may reflect detecting DNA of dead organisms by QRT-PCR. QRT-PCR is a rapid and sensitive method which has great potential for detection of B. fragilis group and related organisms in wound samples.
Relating schizotypy and personality to the phenomenology of creativity.

Although a considerable amount of research has addressed psychopathological and personality correlates of creativity, the relationship between these characteristics and the phenomenology of creativity has been neglected. Relating these characteristics to the phenomenology of creativity may assist in clarifying the precise nature of the relationship between psychopathology and creativity. The current article reports on an empirical study of the relationship between the phenomenology of the creative process and psychopathological and personality characteristics in a sample of artists.



A total of 100 artists (43 males, 57 females, mean age = 34.69 years) from a range of disciplines completed the Experience of Creativity Questionnaire and measures of "positive" schizotypy, affective disturbance, mental boundaries, and normal personality.



The sample of artists was found to be elevated on "positive" schizotypy, unipolar affective disturbance, thin boundaries, and the personality dimensions of Openness to Experience and Neuroticism, compared with norm data. Schizotypy was found to be the strongest predictor of a range of creative experience scales (Distinct Experience, Anxiety, Absorption, Power/Pleasure), suggesting a strong overlap of schizotypal and creative experience.



These findings indicate that "positive" schizotypy is associated with central features of "flow"-type experience, including distinct shift in phenomenological experience, deep absorption, focus on present experience, and sense of pleasure. The neurologically based construct of latent inhibition may be a mechanism that facilitates entry into flow-type states for schizotypal individuals. This may occur by reduced latent inhibition providing a "fresh" awareness and therefore a greater absorption in present experience, thus leading to flow-type states.
Detecting hidden errors in an ontology using contextual knowledge

Due to modeling errors in designing ontologies, an ontology may carry incorrect information. Ontology debugging can be helpful in detecting errors in ontologies that are increasing in size and expressiveness day by day. While current ontology debugging methods can detect logical errors (incoherences and inconsistencies), they are incapable of detecting hidden modeling errors in coherent and consistent ontologies. From the logical perspective, there are no errors in such ontologies, but this study shows some modeling errors may not break the coherency of the ontology by not participating in any contradiction. In this paper, contextual knowledge is exploited to detect such hidden errors. Our experiments show that adding general ontologies like DBpedia as contextual knowledge in the ontology debugging process results in detecting contradictions in ontologies that are coherent.
Morphometric properties of the tensor fascia lata muscle in human foetuses.

In neonatal and early childhood surgeries such as meningomyelocele repairs, closing deep wounds and oncological treatment, tensor fasciae lata (TFL) flaps are used. However, there are not enough data about structural properties of TFL in foetuses, which can be considered as the closest to neonates in terms of sampling. This study's main objective is to gather data about morphological structures of TFL in human foetuses to be used in newborn surgery.



Fifty formalin-fixed foetuses (24 male, 26 female) with gestational age ranging from 18 to 30 weeks (mean 22.94 ± 3.23 weeks) were included in the study. TFL samples were obtained by bilateral dissection and then surface area, width and length parameters were recorded. Digital callipers were used for length and width measurements whereas surface area was calculated using digital image analysis software.



No statistically significant differences were found in terms of numerical value of parameters between sides and sexes (p > 0.05). Linear functions for TFL surface area, width, anterior and posterior margin lengths were calculated as y = -225.652 + 14.417 × age (weeks), y = -5.571 + 0.595 × age (weeks), y = -4.276 + 0.909 × age (weeks), and y = -4.468 + 0.779 × age (weeks), respectively.



Linear functions for TFL surface area, width and lengths can be used in designing TFL flap dimensions in newborn surgery. In addition, using those described linear functions can also be beneficial in prediction of TFL flap dimensions in autopsy studies.
Attributed and Predictive Entity Embedding for Fine-Grained Entity Typing in Knowledge Bases

References  In the real world, some large KBs are lack of type information. Fine-grained entity typing aims at identifying semantic types of an entity in KB. Existing methods suffer from two main problems:  Ignoring rich structural and partial-labeled information in KB.  Requiring large scale corpus in which entity mentions are annotated.  We propose APE model, which can fully utilize various kinds of information comprehensively. Our work benefits many real applications, such as relation extraction, entity linking and QA system.