nils-herrmann/clean_scirepeval huggingface dataset:
Bone Fraction Detection using Image Segmentation

The fracture can occur in any bone of our body like wrist, ankle, hip, rib, leg, chest etc. The Fracture cannot detect easily by the naked eye, so it is seen in the x-ray images. This Paper represents the fracture detection of the bone x-ray images by using the segmentation, Fuzzy c-means, and Multilevel wavelet algorithms. Bone Fracture detection has become a research area in the medical imaging system. An efficient algorithm is proposed for bone fracture based on thresholding and fuzzy cmean segmentation and morphological operators. In the base paper , the fractured portion is selected manually to overcome this drawback, the proposed method detect the bone fracture automatically. The result shows that the proposed method of fracture detection is better. The results show that algorithm is 89.6% accurate and efficient.
Image quality assessment and human visual system

This paper summaries the state-of-the-art of image quality assessment (IQA) and human visual system (HVS). IQA provides an objective index or real value to measure the quality of the specified image. Since human beings are the ultimate receivers of visual information in practical applications, the most reliable IQA is to build a computational model to mimic the HVS. According to the properties and cognitive mechanism of the HVS, the available HVS-based IQA methods can be divided into two categories, i.e., bionics methods and engineering methods. This paper briefly introduces the basic theories and development histories of the above two kinds of HVS-based IQA methods. Finally, some promising research issues are pointed out in the end of the paper.
CONDITIONAL RANDOM FIELDS FOR LIDAR POINT CLOUD CLASSIFICATION IN COMPLEX URBAN AREAS

In this paper, we investigate the potential of a Conditional Random Field (CRF) approach for the classification of an airborne LiDAR (Light Detection And Ranging) point cloud. This method enables the incorporation of contextual information and learning of specific relations of object classes within a training step. Thus, it is a powerful approach for obtaining reliable results even in complex urban scenes. Geometrical features as well as an intensity value are used to distinguish the five object classes building, low vegetation, tree, natural ground, and asphalt ground. The performance of our method is evaluated on the dataset of Vaihingen, Germany, in the context of the ’ISPRS Test Project on Urban Classification and 3D Building Reconstruction’. Therefore, the results of the 3D classification were submitted as a 2D binary label image for a subset of two classes, namely building and tree.
Forming impressions of people versus inanimate objects: Social-cognitive processing in the medial prefrontal cortex

Recent neuroimaging research has linked the task of forming a "person impression" to a distinct pattern of neural activation that includes dorsal regions of the medial prefrontal cortex (mPFC). Although this result suggests the distinctiveness of social cognition - the processes that support inferences about the psychological aspects of other people - it remains unclear whether mPFC contributions to this impression formation task were person specific or if they would extend to other stimulus targets. To address this unresolved issue, participants in the current study underwent fMRI scanning while performing impression formation or a control task for two types of target: other people and inanimate objects. Specifically, participants were asked to use experimentally-provided information either to form an impression of a person or an object or to intentionally encode the sequence in which the information was presented. Results demonstrated that activation in an extensive region of dorsal mPFC was greater for impression formation of other people than for all other trial types, suggesting that this region specifically indexes the social-cognitive aspects of impression formation (i.e., understanding the psychological characteristics of another mental agent). These findings underscore the extent to which social cognition relies on distinct neural mechanisms.
NTFS Directory Index Analysis for Computer Forensics

This work provides a forensic analysis method for a directory index in NTFS file system. NTFS employed B-tree indexing for providing efficient storage of many files and fast lookups, which changes in a structure of the directory index when files are operated. As a forensic view point, we observe behaviors of the B-tree to analyze files that once existed in the directory. However, it is difficult to analyze the allocated index entry when the file commands are executed. So, this work treats a forensic method for a directory index, especially when there are a large number of files in the directory. The index entry records are naturally expanded, then we examine how the index entry records are configured in the index tree. And we provide information that how the directory index nodes are changed and how the index entries remain traces in the index entry record with a computer forensic point of view when the files are deleted.
Fruit Recognition using Color and Texture Features

The computer vision strategies used to recognize a fruit rely on four basic features which characterize the object: intensity, color, shape and texture. This paper proposes an efficient fusion of color and texture features for fruit recognition. The recognition is done by the minimum distance classifier based upon the statistical and co-occurrence features derived from the Wavelet transformed subbands. Experimental results on a database of about 2635 fruits from 15 different classes confirm the effectiveness of the proposed approach.
Human agency in social cognitive theory.

The present article examines the nature and function of human agency within the conceptual model of triadic reciprocal causation. In analyzing the operation of human agency in this interactional causal structure, social cognitive theory accords a central role to cognitive, vicarious, self-reflective, and self-regulatory processes. The issues addressed concern the psychological mechanisms through which personal agency is exercised, the hierarchical structure of self-regulatory systems, eschewal of the dichotomous construal of self as agent and self as object, and the properties of a nondualistic but nonreductional conception of human agency. The relation of agent causality to the fundamental issues of freedom and determinism is also analyzed.
When to Finish? Optimal Beam Search for Neural Text Generation (modulo beam size)

In neural text generation such as neural machine translation, summarization, and image captioning, beam search is widely used to improve the output text quality. However, in the neural generation setting, hypotheses can finish in different steps, which makes it difficult to decide when to end beam search to ensure optimality. We propose a provably optimal beam search algorithm that will always return the optimal-score complete hypothesis (modulo beam size), and finish as soon as the optimality is established (finishing no later than the baseline). To counter neural generation’s tendency for shorter hypotheses, we also introduce a bounded length reward mechanism which allows a modified version of our beam search algorithm to remain optimal. Experiments on neural machine translation demonstrate that our principled beam search algorithm leads to improvement in BLEU score over previously proposed alternatives.
Early Pest Identification in Greenhouse Crops using Image Processing Techniques

Early disease detection is a major challenge in agriculture field. Hence proper measures has to be taken to fight bioagressors of crops while minimizing the use of pesticides. The techniques of machine vision are extensively applied to agricultural science, and it has great perspective especially in the plant protection field,which ultimately leads to crops management. Our goal is early detection of bioagressors. The paper describes a software prototype system for pest detection on the infected images of different leaves. Images of the infected leaf are captured by digital camera and processed using image growing, image segmentation techniques to detect infected parts of the particular plants. Then the detected part is been processed for futher feature extraction which gives general idea about pests. This proposes automatic detection and calculating area of infection on leaves of a whitefly (Trialeurodes vaporariorum Westwood) at a mature stage.
Detecting rare visual relations using analogies

We seek to detect visual relations in images of the form of triplets t = (subject, predicate, object), such as ‘person riding dog’, where training examples of the individual entities are available but their combinations are rare or unseen at training. This is an important set-up due to the combinatorial nature of visual relations : collecting sufficient training data for all possible triplets would be very hard. The contributions of this work are three-fold. First, we learn a representation of visual relations that combines (i) individual embeddings for subject, object and predicate together with (ii) a visual phrase embedding that represents the relation triplet. Second, we learn how to transfer visual phrase embeddings from existing training triplets to unseen test triplets using analogies between relations that involve similar objects. Third, we demonstrate the benefits of our approach on two challenging datasets involving rare and unseen relations : on HICO-DET, our model achieves significant improvement over a strong baseline, and we confirm this improvement on retrieval of unseen triplets on the UnRel rare relation dataset.
The Suffocation of Marriage : Climbing Mount Maslow Without Enough Oxygen

This article distills insights from historical, sociological, and psychological perspectives on marriage to develop the suffocation model of marriage in America. According to this model, contemporary Americans are asking their marriage to help them fulfill different sets of goals than in the past. Whereas they ask their marriage to help them fulfill their physiological and safety needs much less than in the past, they ask it to help them fulfill their esteem and self-actualization needs much more than in the past. Asking the marriage to help them fulfill the latter, higher level needs typically requires sufficient investment of time and psychological resources to ensure that the two spouses develop a deep bond and profound insight into each other’s essential qualities. Although some spouses are investing sufficient resources—and reaping the marital and psychological benefits of doing so—most are not. Indeed, they are, on average, investing less than in the past. As a result, mean levels of marital quality and personal well-being are declining over time. According to the suffocation model, spouses who are struggling with an imbalance between what they are asking from their marriage and what they are investing in it have several promising options for corrective action: intervening to optimize their available resources, increasing their investment of resources in the marriage, and asking less of the marriage in terms of facilitating the fulfillment of spouses’ higher needs. Discussion explores the implications of the suffocation model for understanding dating and courtship, sociodemographic variation, and marriage beyond American’s borders.
Central fatigue. Critical issues, quantification and practical implications.

Central fatigue during exercise is the decrease in muscle force attributable to a decline in motoneuronal output. Several methods have been used to assess central fatigue; however, some are limited or not sensitive enough to detect failure in central drive. Central fatigue develops during many forms of exercise. A number of mechanisms may contribute to its development including an increased inhibition mediated by group III and IV muscle afferents along with a decrease in muscle spindle facilitation. In some situations, motor cortical output is shown to be suboptimal. A specific terminology for central fatigue is included.
Learning good policies from suboptimal demonstrations

Imitating an expert policy is one way to boost reinforcement learning algorithms which, in most cases, rely on random exploration and a huge amount of data. While some major drawbacks of imitation learning, such as compound errors, have been addressed, most results make an implicit hypothesis of having a good (or desired) expert which is usually hard to get in practice. To overcome this obstacle, this paper focuses on learning good policies from suboptimal demonstration data. We systematically investigate the performance of a recent approach under varying assumptions on demonstration quality and show that it performs poorly with suboptimal demonstration data. We then demonstrate the potential to overcome this issue through a performance comparison between learner and demonstrator.
Construction automatique d'ontologies à partir de spécifications de bases de données

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Construction automatique d’ontologies à partir de spécifications de bases de données Mouna Kamel, Nathalie Aussenac-Gilles
Tail Attacks on Web Applications

As the extension of Distributed Denial-of-Service (DDoS) attacks to application layer in recent years, researchers pay much interest in these new variants due to a low-volume and intermittent pattern with a higher level of stealthiness, invaliding the state-of-the-art DDoS detection/defense mechanisms. We describe a new type of low-volume application layer DDoS attack--Tail Attacks on Web Applications. Such attack exploits a newly identified system vulnerability of n-tier web applications (millibottlenecks with sub-second duration and resource contention with strong dependencies among distributed nodes) with the goal of causing the long-tail latency problem of the target web application (e.g., 95th percentile response time > 1 second) and damaging the long-term business of the service provider, while all the system resources are far from saturation, making it difficult to trace the cause of performance degradation.
 We present a modified queueing network model to analyze the impact of our attacks in n-tier architecture systems, and numerically solve the optimal attack parameters. We adopt a feedback control-theoretic (e.g., Kalman filter) framework that allows attackers to fit the dynamics of background requests or system state by dynamically adjusting attack parameters. To evaluate the practicality of such attacks, we conduct extensive validation through not only analytical, numerical, and simulation results but also real cloud production setting experiments via a representative benchmark website equipped with state-of-the-art DDoS defense tools. We further proposed a solution to detect and defense the proposed attacks, involving three stages: fine-grained monitoring, identifying bursts, and blocking bots.
No-redundant Metrics for UML Class Diagram Structural Complexity

In software engineering it is widely acknowledged that the usage of metrics at the initial phases of the object oriented software life cycle can help designers to make better decisions and to predict external quality attributes, such as maintainability. Following this idea we have carried out three controlled experiments to ascertain if any correlation exists between the structural complexity and the size of UML class diagrams and their maintainability. We used 8 metrics for measuring the structural complexity of class diagrams due to the usage of UML relationships, and 3 metrics to measure their size. With the aim of determining which of these metrics are really relevant to be used as class diagrams maintainability indicators, we present in this work a study based on Principal Component Analysis. The obtained results show that the metrics related to associations, aggregations, generalizations and dependencies, are the most relevant whilst those related to size seem to be redundant.
Fingerprint Minutiae Detection Based on Multi-scale Convolution Neural Networks

Minutiae points are defined as the minute discontinuities of local ridge flows, which are widely used as the fine level features for fingerprint recognition. Accurate minutiae detection is important and traditional methods are often based on the hand-crafted processes such as image enhancement, binarization, thinning and tracing of the ridge flows etc. These methods require strong prior knowledge to define the patterns of minutiae points and are easily sensitive to noises. In this paper, we propose a machine learning based algorithm to detect the minutiae points with the gray fingerprint image based on Convolution Neural Networks (CNN). The proposed approach is divided into the training and testing stages. In the training stage, a number of local image patches are extracted and labeled and CNN models are trained to classify the image patches. The test fingerprint is scanned with the CNN model to locate the minutiae position in the testing stage. To improve the detection accuracy, two CNN models are trained to classify the local patch into minutiae v.s. non-minutiae and into ridge ending v.s. bifurcation, respectively. In addition, multi-scale CNNs are constructed with the image patches of varying sizes and are combined to achieve more accurate detection. Finally, the proposed algorithm is tested the fingerprints of FVC2002 DB1 database. Experimental results and comparisons have been presented to show the effectiveness of the proposed method.
Localizing by Describing: Attribute-Guided Attention Localization for Fine-Grained Recognition

A key challenge in fine-grained recognition is how to find and represent discriminative local regions. Recent attention models are capable of learning discriminative region localizers only from category labels with reinforcement learning. However, not utilizing any explicit part information, they are not able to accurately find multiple distinctive regions. In this work, we introduce an attribute-guided attention localization scheme where the local region localizers are learned under the guidance of part attribute descriptions. By designing a novel reward strategy, we are able to learn to locate regions that are spatially and semantically distinctive with reinforcement learning algorithm. The attribute labeling requirement of the scheme is more amenable than the accurate part location annotation required by traditional part-based fine-grained recognition methods. Experimental results on the CUB-200-2011 dataset  demonstrate the superiority of the proposed scheme on both fine-grained recognition and attribute recognition.
Pokémon GO and Physical Activity in Asia: Multilevel Study

Physical activity has long been considered as an important component of a healthy lifestyle. Although many efforts have been made to promote physical activity, there is no effective global intervention for physical activity promotion. Some researchers have suggested that Pokémon GO, a location-based augmented reality game, was associated with a short-term increase in players' physical activity on a global scale, but the details are far from clear.



The objective of our study was to study the relationship between Pokémon GO use and players' physical activity and how the relationship varies across players with different physical activity levels.



We conducted a field study in Hong Kong to investigate if Pokémon GO use was associated with physical activity. Pokémon GO players were asked to report their demographics through a survey; data on their Pokémon GO behaviors and daily walking and running distances were collected from their mobile phones. Participants (n=210) were Hong Kong residents, aged 13 to 65 years, who played Pokémon GO using iPhone 5 or 6 series in 5 selected types of built environment. We measured the participants' average daily walking and running distances over a period of 35 days, from 14 days before to 21 days after game installation. Multilevel modeling was used to identify and examine the predictors (including Pokémon GO behaviors, weather, demographics, and built environment) of the relationship between Pokémon GO use and daily walking and running distances.



The average daily walking and running distances increased by 18.1% (0.96 km, approximately 1200 steps) in the 21 days after the participants installed Pokémon GO compared with the average distances over the 14 days before installation (P<.001). However, this association attenuated over time and was estimated to disappear 24 days after game installation. Multilevel models indicated that Pokémon GO had a stronger and more lasting association among the less physically active players compared with the physically active ones (P<.001). Playing Pokémon GO in green space had a significant positive relationship with daily walking and running distances (P=.03). Moreover, our results showed that whether Pokémon GO was played, the number of days played, weather (total rainfall, bright sunshine, mean air temperature, and mean wind speed), and demographics (age, gender, income, education, and body mass index) were associated with daily walking and running distances.



Pokémon GO was associated with a short-term increase in the players' daily walking and running distances; this association was especially strong among less physically active participants. Pokémon GO can build new links between humans and green space and encourage people to engage in physical activity. Our results show that location-based augmented reality games, such as Pokémon GO, have the potential to be a global public health intervention tool.
How to write a research journal article in engineering and science 1

Writing a research article can be a daunting task, and often, writers are not certain what should be included and how the information should be conveyed. Fortunately, scientific and engineering journal articles follow an accepted format. They contain an introduction which includes a statement of the problem, a literature review, and a general outline of the paper, a methods section detailing the methods used, separate or combined results, discussion and application sections, and a final summary and conclusions section. Here, each of these elements is described in detail using examples from the published literature as illustration. Guidance is also provided with respect to style, getting started, and the revision/review process.