nils-herrmann/clean_scirepeval huggingface dataset:
Prevalence of undernutrition and associated factors among children aged between six to fifty nine months in Bule Hora district, South Ethiopia

More than one-third of deaths during the first five years of life are attributed to undernutrition, which are mostly preventable through economic development and public health measures. To alleviate this problem, it is necessary to determine the nature, magnitude and determinants of undernutrition. However, there is lack of evidence in agro-pastoralist communities like Bule Hora district. Therefore, this study assessed magnitude and factors associated with undernutrition in children who are 6-59 months of age in agro-pastoral community of Bule Hora District, South Ethiopia.



A community based cross-sectional study design was used to assess the magnitude and factors associated with undernutrition in children between 6-59 months. A structured questionnaire was used to collect data from 796 children paired with their mothers. Anthropometric measurements and determinant factors were collected.  16.0 statistical software was used for analysis. Bivariate and multivariate logistic regression analyses were conducted to identify factors associated to nutritional status of the children Statistical association was declared significant if p-value was less than 0.05.



Among study participants, 47.6%, 29.2% and 13.4% of them were stunted, underweight, and wasted respectively. Presence of diarrhea in the past two weeks, male sex, uneducated fathers and > 4 children ever born to a mother were significantly associated with being underweight. Presence of diarrhea in the past two weeks, male sex and pre-lacteal feeding were significantly associated with stunting. Similarly, presence of diarrhea in the past two weeks, age at complementary feed was started and not using family planning methods were associated to wasting.



Undernutrition is very common in under-five children of Bule Hora district. Factors associated to nutritional status of children in agro-pastoralist are similar to the agrarian community. Diarrheal morbidity was associated with all forms of Protein energy malnutrition. Family planning utilization decreases the risk of stunting and underweight. Feeding practices (pre-lacteal feeding and complementary feeding practice) were also related to undernutrition. Thus, nutritional intervention program in Bule Hora district in Ethiopia should focus on these factors.
Text Summarization within the Latent Semantic Analysis Framework: Comparative Study

It is very difficult for human beings to manually summarize large documents of text. Text summarization solves this problem. Nowadays, Text summarization systems are among the most attractive research areas. Text summarization (TS) is used to provide a shorter version of the original text and keeping the overall meaning. There are various methods that aim to find out well-formed summaries. One of the most commonly used methods is the Latent Semantic Analysis (LSA). In this review, we present a comparative study among almost algorithms based on Latent Semantic Analysis (LSA) approach.
Failure Prediction for Autonomous Driving

The primary focus of autonomous driving research is to improve driving accuracy. While great progress has been made, state-of-the-art algorithms still fail at times. Such failures may have catastrophic consequences. It therefore is im- portant that automated cars foresee problems ahead as early as possible. This is also of paramount importance if the driver will be asked to take over. We conjecture that failures do not occur randomly. For instance, driving models may fail more likely at places with heavy traffic, at complex intersections, and/or under adverse weather/illumination conditions. This work presents a method to learn to predict the occurrence of these failures, i.e., to assess how difficult a scene is to a given driving model and to possibly give the human driver an early headsup. A camera- based driving model is developed and trained over real driving datasets. The discrepancies between the model's predictions and the human 'ground-truth' maneuvers were then recorded, to yield the 'failure' scores. Experimental results show that the failure score can indeed be learned and predicted. Thus, our prediction method is able to improve the overall safety of an automated driving model by alerting the human driver timely, leading to better human-vehicle collaborative driving.
An Easy-to-Use Airborne LiDAR Data Filtering Method Based on Cloth Simulation

Separating point clouds into ground and non-ground measurements is an essential step to generate digital terrain models (DTMs) from airborne LiDAR (light detection and ranging) data. However, most filtering algorithms need to carefully set up a number of complicated parameters to achieve high accuracy. In this paper, we present a new filtering method which only needs a few easy-to-set integer and Boolean parameters. Within the proposed approach, a LiDAR point cloud is inverted, and then a rigid cloth is used to cover the inverted surface. By analyzing the interactions between the cloth nodes and the corresponding LiDAR points, the locations of the cloth nodes can be determined to generate an approximation of the ground surface. Finally, the ground points can be extracted from the LiDAR point cloud by comparing the original LiDAR points and the generated surface. Benchmark datasets provided by ISPRS (International Society for Photogrammetry and Remote Sensing) working Group III/3 are used to validate the proposed filtering method, and the experimental results yield an average total error of 4.58%, which is comparable with most of the state-of-the-art filtering algorithms. The proposed easy-to-use filtering method may help the users without much experience to use LiDAR data and related technology in their own applications more easily.
Recent advances on multicue object tracking: a survey

The performance of single cue object tracking algorithms may degrade due to complex nature of visual world and environment challenges. In recent past, multicue object tracking methods using single or multiple sensors such as vision, thermal, infrared, laser, radar, audio, and RFID are explored to a great extent. It was acknowledged that combining multiple orthogonal cues enhance tracking performance over single cue methods. The aim of this paper is to categorize multicue tracking methods into single-modal and multi-modal and to list out new trends in this field via investigation of representative work. The categorized works are also tabulated in order to give detailed overview of latest advancement. The person tracking datasets are analyzed and their statistical parameters are tabulated. The tracking performance measures are also categorized depending upon availability of ground truth data. Our review gauges the gap between reported work and future demands for object tracking.
Autism: A “Critical Period” Disorder?

Cortical circuits in the brain are refined by experience during critical periods early in postnatal life. Critical periods are regulated by the balance of excitatory and inhibitory (E/I) neurotransmission in the brain during development. There is now increasing evidence of E/I imbalance in autism, a complex genetic neurodevelopmental disorder diagnosed by abnormal socialization, impaired communication, and repetitive behaviors or restricted interests. The underlying cause is still largely unknown and there is no fully effective treatment or cure. We propose that alteration of the expression and/or timing of critical period circuit refinement in primary sensory brain areas may significantly contribute to autistic phenotypes, including cognitive and behavioral impairments. Dissection of the cellular and molecular mechanisms governing well-established critical periods represents a powerful tool to identify new potential therapeutic targets to restore normal plasticity and function in affected neuronal circuits.
Hydrogel scaffolds for tissue engineering: Progress and challenges

Designing of biologically active scaffolds with optimal characteristics is one of the key factors for successful tissue engineering. Recently, hydrogels have received a considerable interest as leading candidates for engineered tissue scaffolds due to their unique compositional and structural similarities to the natural extracellular matrix, in addition to their desirable framework for cellular proliferation and survival. More recently, the ability to control the shape, porosity, surface morphology, and size of hydrogel scaffolds has created new opportunities to overcome various challenges in tissue engineering such as vascularization, tissue architecture and simultaneous seeding of multiple cells. This review provides an overview of the different types of hydrogels, the approaches that can be used to fabricate hydrogel matrices with specific features and the recent applications of hydrogels in tissue engineering. Special attention was given to the various design considerations for an efficient hydrogel scaffold in tissue engineering. Also, the challenges associated with the use of hydrogel scaffolds were described.
SCREENING OF PLANT ESSENTIAL OILS FOR ANTIFUNGAL ACTIVITY AGAINST MALASSEZIA FURFUR

Dandruff is a common scalp disorder affecting almost half of the population at the prepubertal age and of any gender and ethnicity. The fungus Malassezia furfur, a lipophilic yeast is widely accepted as the causative agent of dandruff, which due to its lipase activity releases proinflammatory free fatty acids causing dermal inflammation and tissue damage. Currently available treatment options of chemical origin have various limitations, either due to poor clinical efficiency or compliance issues. Also, these drugs are unable to prevent recurrence, which is the most common problem. Due to this, attention is shifting towards herbal remedies with aromatic plants and their essential oils being widely popular for their empirically antifungal properties. The present study was aimed to evaluate the potential inhibitory effects of essential oils of indigenous plants on M. furfur. The antifungal activity of four aromatic oils of kapur tulsi, cinnamon, eucalyptus, cajeput along with one fixed oil of karanj was screened alone or in combinations using tea tree oil and ketoconazole as standards. Out of five selected oils, three oils showed activity in the order cinnamon oil >kapur tulsi oil >cajeput while karanj oil and eucalyptus oil were inactive against the fungus. The minimum inhibitory concentration (MICs) of the active oils was evaluated using broth dilution method. The cinnamon oil, kapur tulsi oil and cajeput oil on evaluating in different combinations showed synergistic effect with a mixture of cinnamon oil and kapur tulsi oil exhibiting the best activity. The study reports effectiveness of kapur tulsi oil against M. furfur for the first time and further the synergistic combinations of oils is also being reported for the first time. The findings provide promising information on the potential use of essential oils for the treatment of dandruff.
"who is Key...?" - Characterizing Value Adding Users in Enterprise Social Networks

Whereas the use of Enterprise Social Networks (ESN) is a pervasive topic in research and practice, both parties are still struggling to come to a better understanding of the role and impact of ESN in and on knowledge-intensive corporate work. As a part of this phenomenon, employees who communicate their knowledge in ESN helping other users to do their daily work play a decisive role. We need to come to a better understanding of the role and behaviour of such value adding users. This is a prerequisite, for example, for understanding knowledge support hubs or for enabling more effective internal information and knowledge sharing. Against this background, we investigate the structural characteristics of value adding users in ESN using qualitative text analysis and Social Network Analysis. Based on a large scale dataset of a global consulting company using the ESN Yammer.com we analyse the social relationships of value adding users. We confirm their significant position and draw conclusions for research and practice.
A Context-aware Attention Network for Interactive Question Answering

Neural network based sequence-to-sequence models in an encoder-decoder framework have been successfully applied to solve Question Answering (QA) problems, predicting answers from statements and questions. However, almost all previous models have failed to consider detailed context information and unknown states under which systems do not have enough information to answer given questions. These scenarios with incomplete or ambiguous information are very common in the setting of Interactive Question Answering (IQA). To address this challenge, we develop a novel model, employing context-dependent word-level attention for more accurate statement representations and question-guided sentence-level attention for better context modeling. We also generate unique IQA datasets to test our model, which will be made publicly available. Employing these attention mechanisms, our model accurately understands when it can output an answer or when it requires generating a supplementary question for additional input depending on different contexts. When available, user's feedback is encoded and directly applied to update sentence-level attention to infer an answer. Extensive experiments on QA and IQA datasets quantitatively demonstrate the effectiveness of our model with significant improvement over state-of-the-art conventional QA models.
The Power of Events: An Introduction to Complex Event Processing in Distributed Enterprise Systems

Complex Event Processing (CEP) is a defined set of tools and techniques for analyzing and controlling the complex series of interrelated events that drive modern distributed information systems. This emerging technology helps IS and IT professionals understand what is happening within the system, quickly identify and solve problems, and more effectively utilize events for enhanced operation, performance, and security. CEP can be applied to a broad spectrum of information system challenges, including business process automation, schedule and control processes, network monitoring and performance prediction, and intrusion detection. This talk is about the rise of CEP as we know it today, its historical roots and its current position in commercial markets. Some possible long-term future roles of CEP in the Information Society are discussed along with the need to develop rule-based event hierarchies on a commercial basis to make those applications possible. The talk gives empahsis to the point that "Rules are everywhere" and that mathematical formalisms cannot express all the forms that are in use in various event processing systems.
Adopting the Word-Pair-Dependency-Triplets with Individual Comparison for Natural Language Inference

This paper proposes to perform natural language inference with Word-Pair-DependencyTriplets. Most previous DNN-based approaches either ignore syntactic dependency among words, or directly use tree-LSTM to generate sentence representation with irrelevant information. To overcome the problems mentioned above, we adopt Word-Pair-DependencyTriplets to improve alignment and inference judgment. To be specific, instead of comparing each triplet from one passage with the merged information of another passage, we first propose to perform comparison directly between the triplets of the given passage-pair to make the judgment more interpretable. Experimental results show that the performance of our approach is better than most of the approaches that use tree structures, and is comparable to other stateof-the-art approaches.
InstanceCut: From Edges to Instances with MultiCut

This work addresses the task of instance-aware semantic segmentation. Our key motivation is to design a simple method with a new modelling-paradigm, which therefore has a different trade-off between advantages and disadvantages compared to known approaches. Our approach, we term InstanceCut, represents the problem by two output modalities: (i) an instance-agnostic semantic segmentation and (ii) all instance-boundaries. The former is computed from a standard convolutional neural network for semantic segmentation, and the latter is derived from a new instance-aware edge detection model. To reason globally about the optimal partitioning of an image into instances, we combine these two modalities into a novel MultiCut formulation. We evaluate our approach on the challenging CityScapes dataset. Despite the conceptual simplicity of our approach, we achieve the best result among all published methods, and perform particularly well for rare object classes.
Variational Reasoning for Question Answering With Knowledge Graph

Knowledge graph (KG) is known to be helpful for the task of question answering (QA), since it provides well-structured relational information between entities, and allows one to further infer indirect facts. However, it is challenging to build QA systems which can learn to reason over knowledge graphs based on question-answer pairs alone. First, when people ask questions, their expressions are noisy (for example, typos in texts, or variations in pronunciations), which is non-trivial for the QA system to match those mentioned entities to the knowledge graph. Second, many questions require multi-hop logic reasoning over the knowledge graph to retrieve the answers. To address these challenges, we propose a novel and unified deep learning architecture, and an end-to-end variational learning algorithm which can handle noise in questions, and learn multi-hop reasoning simultaneously. Our method achieves state-of-the-art performance on a recent benchmark dataset in the literature. We also derive a series of new benchmark datasets, including questions for multi-hop reasoning, questions paraphrased by neural translation model, and questions in human voice. Our method yields very promising results on all these challenging datasets.
Adaptive 360 VR Video Streaming: Divide and Conquer

While traditional multimedia applications such as games and videos are still popular, there has been a significant interest in the recent years towards new 3D media such as 3D immersion and Virtual Reality (VR) applications, especially 360 VR videos. 360 VR video is an immersive spherical video where the user can look around during playback. Unfortunately, 360 VR videos are extremely bandwidth intensive, and therefore are difficult to stream at acceptable quality levels. In this paper, we propose an adaptive bandwidth-efficient 360 VR video streaming system using a divide and conquer approach. We propose a dynamic view-aware adaptation technique to tackle the huge bandwidth demands of 360 VR video streaming. We spatially divide the videos into multiple tiles while encoding and packaging, use MPEG-DASH SRD to describe the spatial relationship of tiles in the 360-degree space, and prioritize the tiles in the Field of View (FoV). In order to describe such tiled representations, we extend MPEG-DASH SRD to the 3D space of 360 VR videos. We spatially partition the underlying 3D mesh, and construct an efficient 3D geometry mesh called hexaface sphere to optimally represent a tiled 360 VR video in the 3D space. Our initial evaluation results report up to 72% bandwidth savings on 360 VR video streaming with minor negative quality impacts compared to the baseline scenario when no adaptations is applied.
Paraphrase Fragment Extraction from Monolingual Comparable Corpora

We present a novel paraphrase fragment pair extraction method that uses a monolingual comparable corpus containing different articles about the same topics or events. The procedure consists of document pair extraction, sentence pair extraction, and fragment pair extraction. At each stage, we evaluate the intermediate results manually, and tune the later stages accordingly. With this minimally supervised approach, we achieve 62% of accuracy on the paraphrase fragment pairs we collected and 67% extracted from the MSR corpus. The results look promising, given the minimal supervision of the approach, which can be further scaled up.
When Personalization Meets Conformity: Collective Similarity based Multi-Domain Recommendation

Existing recommender systems place emphasis on personalization to achieve promising accuracy. However, in the context of multiple domain, users are likely to seek the same behaviors as domain authorities. This conformity effect provides a wealth of prior knowledge when it comes to multi-domain recommendation, but has not been fully exploited. In particular, users whose behaviors are significant similar with the public tastes can be viewed as domain authorities. To detect these users meanwhile embed conformity into recommendation, a domain-specific similarity matrix is intuitively employed. Therefore, a collective similarity is obtained to leverage the conformity with personalization. In this paper, we establish a Collective Structure Sparse Representation(CSSR) method for multi-domain recommendation. Based on adaptive $k$-Nearest-Neighbor framework, we impose the lasso and group lasso penalties as well as least square loss to jointly optimize the collective similarity. Experimental results on real-world data confirm the effectiveness of the proposed method.
Dynamics of Blockchain Implementation - A Case Study from the Energy Sector

This case study analyzes the impact of theory-based factors on the implementation of different blockchain technologies in use cases from the energy sector. We construct an integrated research model based on the Diffusion of Innovations theory, institutional economics and the Technology-Organization-Environment framework. Using qualitative data from in-depth interviews, we link constructs to theory and assess their impact on each use case. Doing so we can depict the dynamic relations between different blockchain technologies and the energy sector. The study provides insights for decision makers in electric utilities, and government administrations.
Bootstrapping for Numerical Open

We design and release BONIE, the first open numerical relation extractor, for extracting Open IE tuples where one of the arguments is a number or a quantity-unit phrase. BONIE uses bootstrapping to learn the specific dependency patterns that express numerical relations in a sentence. BONIE’s novelty lies in task-specific customizations, such as inferring implicit relations, which are clear due to context such as units (for e.g., ‘square kilometers’ suggests area, even if the word ‘area’ is missing in the sentence). BONIE obtains 1.5x yield and 15 point precision gain on numerical facts over a state-of-the-art Open IE system.
When Corrections Fail : The persistence of political misperceptions

An extensive literature addresses citizen ignorance, but very little research focuses on misperceptions. Can these false or unsubstantiated beliefs about politics be corrected? Previous studies have not tested the efficacy of corrections in a realistic format. We conducted four experiments in which subjects read mock news articles that included either a misleading claim from a politician, or a misleading claim and a correction. Results indicate that corrections frequently fail to reduce misperceptions among the targeted ideological group. We also document several instances of a “backfire effect” in which corrections actually increase misperceptions among the group in question. A previous version of this paper was presented at the 2006 annual meeting of the American Political Science Association. We thank anonymous reviewers, the editors, and audiences at the University of North Carolina at Chapel Hill, Northwestern University, and the Duke Political Science Graduate Student Colloquium for valuable feedback.