nils-herrmann/clean_scirepeval huggingface dataset:
Cooperative Holistic Scene Understanding: Unifying 3D Object, Layout, and Camera Pose Estimation

Holistic 3D indoor scene understanding refers to jointly recovering the i) object bounding boxes, ii) room layout, and iii) camera pose, all in 3D. The existing methods either are ineffective or only tackle the problem partially. In this paper, we propose an end-to-end model that simultaneously solves all three tasks in realtime given only a single RGB image. The essence of the proposed method is to improve the prediction by i) parametrizing the targets (e.g., 3D boxes) instead of directly estimating the targets, and ii) cooperative training across different modules in contrast to training these modules individually. Specifically, we parametrize the 3D object bounding boxes by the predictions from several modules, i.e., 3D camera pose and object attributes. The proposed method provides two major advantages: i) The parametrization helps maintain the consistency between the 2D image and the 3D world, thus largely reducing the prediction variances in 3D coordinates. ii) Constraints can be imposed on the parametrization to train different modules simultaneously. We call these constraints "cooperative losses" as they enable the joint training and inference. We employ three cooperative losses for 3D bounding boxes, 2D projections, and physical constraints to estimate a geometrically consistent and physically plausible 3D scene. Experiments on the SUN RGB-D dataset shows that the proposed method significantly outperforms prior approaches on 3D object detection, 3D layout estimation, 3D camera pose estimation, and holistic scene understanding.
Cluster-based D2D architecture for safety services in vehicular ad hoc networks

With the expected introduction of autonomous vehicles in the next decade, it will be imperative for road traffic authorities to develop strategies to manage the co-existence of autonomous and legacy vehicles. In this scenario, the exchange of safety messages among vehicles in a timely manner will be critical for future advanced road traffic management systems. Most of the work in the area of vehicular ad hoc network (VANET) has concentrated on the IEEE 802.11p and the DSRC standards. With the emergence of the advanced LTE-based cellular network standard, researchers are now concentrating on the LTE-based VANET architecture. However, due to the use of the centralized architecture, an LTE-based system may require significant network resources to support vehicle-to-vehicle (V2V) communications. In this paper, we propose a new vehicle discovery technique and a LTE cluster-based device-to-device (D2D) architecture to achieve high packet delivery ratio for the safety services in a vehicular network. The performance of the proposed network architecture is analyzed by an OMNET++ based simulation model. Simulations results shows that the proposed algorithm reduces the end-to-end delay and signalling overhead as well as improve the data packet delivery ratio (DPDR) compared to the existing 3GPP ProSe technique for vehicular safety applications.
Stochastic Block Model and Community Detection in the Sparse Graphs: A spectral algorithm with optimal rate of recovery

In this paper, we present and analyze a simple and robust spectral algorithm for the stochastic block model with k blocks, for any k fixed. Our algorithm works with graphs having constant edge density, under an optimal condition on the gap between the density inside a block and the density between the blocks. As a co-product, we settle an open question posed by Abbe et. al. concerning censor block models.
Semantic-Unit-Based Dilated Convolution for Multi-Label Text Classification

A novel model for multi-label text classification based on Seq2Seq; Generate semantic unit representations with dilated convolution; Hybrid attention to integrate semantic unit and word information; Improved results on benchmark datasets Comparable to hierarchical models but with fewer costs. Sequence-to-Sequence as Baseline  Encoder: Bidirectional LSTM  Decoder: LSTM for sequential decoding. Training is with teacher forcing.  Attention mechanism: global attention for the relevant sourceside information Motivation Labels for text have internal correlation; The relation between label and text is complex; Semantic unit demonstrating event is more informative; Significant key words can make a difference.  Ablation tests and Comparison with Hierarchical Models Experiments Datasets: Reuters Corpus Volume I (RCV1-v2) and Ren-CECps; Metric: Hamming Loss and MicroF1 score  A new model for multi-label text classification with the combination of Seq2Seq and dilated convolution;  Classification based on semantic units and key words;  Outperform the baselines and robust to labels of low frequency. Dilated Convolution for Semantic Unit  Multi-level dilated convolution over the outputs of the encoder tend to extract information of semantic units. Hybrid Attention for Integration  Hybrid Attention allows the integration of the attention to the semantic unit information and the word information. Pengcheng Yang2 Performance on labels of low frequency  Remove the top 10, 20, 30, 40, 50 and 60 most frequent labels subsequently  More robust to the classification of labels of low frequency
Management of diabetic foot by natural honey.

Many studies have demonstrated that honey has antibacterial activity in vitro, and a small number of clinical case studies have shown that application of honey to severely infected cutaneous wounds is capable of clearing infection from the wound and improving tissue healing. Research has also indicated that honey may possess anti-inflammatory activity and stimulate immune responses within a wound. The overall effect is to reduce infection and to enhance wound healing in burns, ulcers, and other cutaneous wounds. The objective of the study was to find out the results of topical wound dressings in diabetic wounds with natural honey.



The study was conducted at department of Orthopaedics, Unit-1, Liaquat University of Medical and Health Sciences, Jamshoro from July 2006 to June 2007. Study design was experimental. The inclusion criteria were patients of either gender with any age group having diabetic foot Wagner type I, II, III and II. The exclusion criteria were patients not willing for studies and who needed urgent amputation due to deteriorating illness. Initially all wounds were washed thoroughly and necrotic tissues removed and dressings with honey were applied and continued up to healing of wounds.



Total number of patients was 12 (14 feet). There were 8 males (66.67%) and 4 females (33.33%), 2 cases (16.67%) were presented with bilateral diabetic feet. The age range was 35 to 65 years (46 +/- 9.07 years). Amputations of big toe in 3 patients (25%), second and third toe ray in 2 patients (16.67%) and of fourth and fifth toes at the level of metatarsophalengeal joints were done in 3 patients (25%). One patient (8.33%) had below knee amputation.



In our study we observed excellent results in treating diabetic wounds with dressings soaked with natural honey. The disability of diabetic foot patients was minimized by decreasing the rate of leg or foot amputations and thus enhancing the quality and productivity of individual life.
From LMS to PLE: A Step Forward through OpenSocial Apps in Moodle

Bringing flexibility and extensibility into Learning Management Systems) is crucial because it gives teachers and students a free choice of technologies and educational materials they want to use for their courses. The paper presents a solution via enabling widgets (OpenSocial apps) within Moodle. Our first Moodle plug-in allows teachers to freely choose a set of tools they want to use in their courses though students can not change widgets proposed by teachers. This environment was evaluated with students within several courses. Even though the environment was perceived as useful by students, they still lacked their own personalization. We describe how the future plug-in tackles this problem.
Introducing Concept And Syntax Transition Networks for Image Captioning

The area of image captioning i.e. the automatic generation of short textual descriptions of images has experienced much progress recently. However, image captioning approaches often only focus on describing the content of the image without any emotional or sentimental dimension which is common in human captions. This paper presents an approach for image captioning designed specifically to incorporate emotions and feelings into the caption generation process. The presented approach consists of a Deep Convolutional Neural Network (CNN) for detecting Adjective Noun Pairs in the image and a novel graphical network architecture called "Concept And Syntax Transition (CAST)" network for generating sentences from these detected concepts.
Stable Bipedal Walking With Foot Rotation Through Direct Regulation of the Zero Moment Point

Consider a biped evolving in the sagittal plane. The unexpected rotation of the supporting foot can be avoided by controlling the zero moment point (ZMP). The objective of this study is to propose and analyze a control strategy for simultaneously regulating the position of the ZMP and the joints of the robot. If the tracking requirements were posed in the time domain, the problem would be underactuated in the sense that the number of inputs would be less than the number of outputs. To get around this issue, the proposed controller is based on a path-following control strategy, previously developed for dealing with the underactuation present in planar robots without actuated ankles. In particular, the control law is defined in such a way that only the kinematic evolution of the robot's state is regulated, but not its temporal evolution. The asymptotic temporal evolution of the robot is completely defined through a one degree-of-freedom subsystem of the closed-loop model. Since the ZMP is controlled, bipedal walking that includes a prescribed rotation of the foot about the toe can also be considered. Simple analytical conditions are deduced that guarantee the existence of a periodic motion and the convergence toward this motion.
Simultaneous MR/PET imaging of the human brain: feasibility study.

The purpose of this study was to apply a magnetic resonance (MR) imaging-compatible positron emission tomographic (PET) detector technology for simultaneous MR/PET imaging of the human brain and skull base. The PET detector ring consists of lutetium oxyorthosilicate (LSO) scintillation crystals in combination with avalanche photodiodes (APDs) mounted in a clinical 3-T MR imager with use of the birdcage transmit/receive head coil. Following phantom studies, two patients were simultaneously examined by using fluorine 18 fluorodeoxyglucose (FDG) PET and MR imaging and spectroscopy. MR/PET data enabled accurate coregistration of morphologic and multifunctional information. Simultaneous MR/PET imaging is feasible in humans, opening up new possibilities for the emerging field of molecular imaging.
Modeling Missing Data in Distant Supervision for Information Extraction

Distant supervision algorithms learn information extraction models given only large readily available databases and text collections. Most previous work has used heuristics for generating labeled data, for example assuming that facts not contained in the database are not mentioned in the text, and facts in the database must be mentioned at least once. In this paper, we propose a new latent-variable approach that models missing data. This provides a natural way to incorporate side information, for instance modeling the intuition that text will often mention rare entities which are likely to be missing in the database. Despite the added complexity introduced by reasoning about missing data, we demonstrate that a carefully designed local search approach to inference is very accurate and scales to large datasets. Experiments demonstrate improved performance for binary and unary relation extraction when compared to learning with heuristic labels, including on average a 27% increase in area under the precision recall curve in the binary case.
DETECTING DECEIT VIA ANALYSIS OF VERBAL AND NONVERBAL BEHAVIOR

We examined the hypotheses that (1) a systematic analysis of nonverbal behavior could be useful in the detection of deceit and (2) that lie detection would be most accurate if both verbal and nonverbal indicators of deception are taken into account. Seventy-three nursing students participated in a study about “telling lies” and either told the truth or lied about a film they had just seen. The interviews were videotaped and audiotaped, and the nonverbal behavior (NVB) and speech content of the liars and truth tellers were analyzed, the latter with the Criteria-Based Content Analysis technique (CBCA) and the Reality Monitoring technique (RM). Results revealed several nonverbal and verbal indicators of deception. On the basis of nonverbal behavior alone, 78% of the lies and truths could be correctly classified. An even higher percentage could be correctly classified when all three detection techniques (i.e., NVB, CBCA, RM) were taken into account.
The Internet of Things vision: Key features, applications and open issues

The Internet of Things (IoT) is a new paradigm that combines aspects and technologies coming from different approaches. Ubiquitous computing, pervasive computing, Internet Protocol, sensing technologies, communication technologies, and embedded devices are merged together in order to form a system where the real and digital worlds meet and are continuously in symbiotic interaction. The smart object is the building block of the IoT vision. By putting intelligence into everyday objects, they are turned into smart objects able not only to collect information from the environment and interact/control the physical world, but also to be interconnected, to each other, through Internet to exchange data and information. The expected huge number of interconnected devices and the significant amount of available data open new opportunities to create services that will bring tangible benefits to the society, environment, economy and individual citizens. In this paper we present the key features and the driver technologies of IoT. In addition to identifying the application scenarios and the correspondent potential applications, we focus on research challenges and open issues to be faced for the IoT realization in the real world.
Reasoning about Object Affordances in a Knowledge Base Representation

Reasoning about objects and their affordances is a fundamental problem for visual intelligence. Most of the previous work casts this problem as a classification task where separate classifiers are trained to label objects, recognize attributes, or assign affordances. In this work, we consider the problem of object affordance reasoning using a knowledge base representation. Diverse information of objects are first harvested from images and other meta-data sources. We then learn a knowledge base (KB) using a Markov Logic Network (MLN). Given the learned KB, we show that a diverse set of visual inference tasks can be done in this unified framework without training separate classifiers, including zeroshot affordance prediction and object recognition given human poses.
Generating Videos with Scene Dynamics

We capitalize on large amounts of unlabeled video in order to learn a model of scene dynamics for both video recognition tasks (e.g. action classification) and video generation tasks (e.g. future prediction). We propose a generative adversarial network for video with a spatio-temporal convolutional architecture that untangles the scene’s foreground from the background. Experiments suggest this model can generate tiny videos up to a second at full frame rate better than simple baselines, and we show its utility at predicting plausible futures of static images. Moreover, experiments and visualizations show the model internally learns useful features for recognizing actions with minimal supervision, suggesting scene dynamics are a promising signal for representation learning. We believe generative video models can impact many applications in video understanding and simulation.
Demographic differences in how students navigate through MOOCs

The current generation of Massive Open Online Courses (MOOCs) attract a diverse student audience from all age groups and over 196 countries around the world. Researchers, educators, and the general public have recently become interested in how the learning experience in MOOCs differs from that in traditional courses. A major component of the learning experience is how students navigate through course content.
 This paper presents an empirical study of how students navigate through MOOCs, and is, to our knowledge, the first to investigate how navigation strategies differ by demographics such as age and country of origin. We performed data analysis on the activities of 140,546 students in four edX MOOCs and found that certificate earners skip on average 22% of the course content, that they frequently employ non-linear navigation by jumping backward to earlier lecture sequences, and that older students and those from countries with lower student-teacher ratios are more comprehensive and non-linear when navigating through the course.
 From these findings, we suggest design recommendations such as for MOOC platforms to develop more detailed forms of certification that incentivize students to deeply engage with the content rather than just doing the minimum necessary to earn a passing grade. Finally, to enable other researchers to reproduce and build upon our findings, we have made our data set and analysis scripts publicly available.
Procedural content generation for games: A survey

Hundreds of millions of people play computer games every day. For them, game content—from 3D objects to abstract puzzles—plays a major entertainment role. Manual labor has so far ensured that the quality and quantity of game content matched the demands of the playing community, but is facing new scalability challenges due to the exponential growth over the last decade of both the gamer population and the production costs. Procedural Content Generation for Games (PCG-G) may address these challenges by automating, or aiding in, game content generation. PCG-G is difficult, since the generator has to create the content, satisfy constraints imposed by the artist, and return interesting instances for gamers. Despite a large body of research focusing on PCG-G, particularly over the past decade, ours is the first comprehensive survey of the field of PCG-G. We first introduce a comprehensive, six-layered taxonomy of game content: bits, space, systems, scenarios, design, and derived. Second, we survey the methods used across the whole field of PCG-G from a large research body. Third, we map PCG-G methods to game content layers; it turns out that many of the methods used to generate game content from one layer can be used to generate content from another. We also survey the use of methods in practice, that is, in commercial or prototype games. Fourth and last, we discuss several directions for future research in PCG-G, which we believe deserve close attention in the near future.
Neural Text Embeddings for Information Retrieval

In the last few years, neural representation learning approaches have achieved very good performance on many natural language processing tasks, such as language modelling and machine translation. This suggests that neural models will also achieve good performance on information retrieval (IR) tasks, such as relevance ranking, addressing the query-document vocabulary mismatch problem by using a semantic rather than lexical matching. Although initial iterations of neural models do not outperform traditional lexical-matching baselines, the level of interest and effort in this area is increasing, potentially leading to a breakthrough. The popularity of the recent SIGIR 2016 workshop on Neural Information Retrieval provides evidence to the growing interest in neural models for IR. While recent tutorials have covered some aspects of deep learning for retrieval tasks, there is a significant scope for organizing a tutorial that focuses on the fundamentals of representation learning for text retrieval. The goal of this tutorial will be to introduce state-of-the-art neural embedding models and bridge the gap between these neural models with early representation learning approaches in IR (e.g., LSA). We will discuss some of the key challenges and insights in making these models work in practice, and demonstrate one of the toolsets available to researchers interested in this area.
Histological study on the effects of microablative fractional CO2 laser on atrophic vaginal tissue: an ex vivo study.

Microablative fractional CO2 laser has been proven to determine tissue remodeling with neoformation of collagen and elastic fibers on atrophic skin. The aim of our study is to evaluate the effects of microablative fractional CO2 laser on postmenopausal women with vulvovaginal atrophy using an ex vivo model.



This is a prospective ex vivo cohort trial. Consecutive postmenopausal women with vulvovaginal atrophy managed with pelvic organ prolapse surgical operation were enrolled. After fascial plication, the redundant vaginal edge on one side was treated with CO2 laser (SmartXide2; DEKA Laser, Florence, Italy). Five different CO2 laser setup protocols were tested. The contralateral part of the vaginal wall was always used as control. Excessive vagina was trimmed and sent for histological evaluation to compare treated and nontreated tissues. Microscopic and ultrastructural aspects of the collagenic and elastic components of the matrix were studied, and a specific image analysis with computerized morphometry was performed. We also considered the fine cytological aspects of connective tissue proper cells, particularly fibroblasts.



During the study period, five women were enrolled, and 10 vaginal specimens were finally retrieved. Four different settings of CO2 laser were compared. Protocols were tested twice each to confirm histological findings. Treatment protocols were compared according to histological findings, particularly in maximal depth and connective changes achieved. All procedures were uneventful for participants.



This study shows that microablative fractional CO2 laser can produce a remodeling of vaginal connective tissue without causing damage to surrounding tissue.
A Complete Framework for Verification , Validation , and Uncertainty Quantification in Scientific Computing ( Invited )

This paper gives a broad overview of a complete framework for assessing the predictive uncertainty of scientific computing applications. The framework is complete in the sense that it treats both types of uncertainty (aleatory and epistemic) and incorporates uncertainty due to the form of the model and any numerical approximations used. Aleatory (or random) uncertainties in model inputs are treated using cumulative distribution functions, while epistemic (lack of knowledge) uncertainties are treated as intervals. Approaches for propagating both types of uncertainties through the model to the system response quantities of interest are discussed. Numerical approximation errors (due to discretization, iteration, and round off) are estimated using verification techniques, and the conversion of these errors into epistemic uncertainties is discussed. Model form uncertainties are quantified using model validation procedures, which include a comparison of model predictions to experimental data and then extrapolation of this uncertainty structure to points in the application domain where experimental data do not exist. Finally, methods for conveying the total predictive uncertainty to decision makers are presented.