nils-herrmann/clean_scirepeval huggingface dataset:
Designing novel review ranking systems: predicting the usefulness and impact of reviews

With the rapid growth of the Internet, users' ability to publish content has created active electronic communities that provide a wealth of product information. Consumers naturally gravitate to reading reviews in order to decide whether to buy a product. However, the high volume of reviews that are typically published for a single product makes it harder for individuals to locate the best reviews and understand the true underlying quality of a product based on the reviews. Similarly, the manufacturer of a product needs to identify the reviews that influence the customer base, and examine the content of these reviews. In this paper, we propose two ranking mechanisms for ranking product reviews: a consumer-oriented ranking mechanism ranks the reviews according to their expected helpfulness, and a manufacturer-oriented ranking mechanism ranks the reviews according to their expected effect on sales. Our ranking mechanism combines econometric analysis with text mining techniques in general, and with subjectivity analysis in particular. We show that subjectivity analysis can give useful clues about the helpfulness of a review and about its impact on sales. Our results can have several implications for the market design of online opinion forums.
Oil-spills detection in SAR images by fractal dimension estimation

The paper describes a multi-resolution algorithm based on fractal geometry for texture analysis and detection of oil spills in SAR images. The multi-resolution approach reduces the problems of speckle and sea clutter and preserves subtle variations of oil slicks. The use of fractal dimension as a feature for classification improves the oil spill detection, since enhances texture discrimination. The proposed approach computes the fractal dimension from power spectra: the ratio between powers at different scales is straightforwardly related to fractal dimension. The proposed method to compute a fractal signature is based on the multi-scale image decomposition obtained by the Normalized Laplacian Pyramid which provides a reliable estimation of the fractal dimension even in the presence of speckle.
Grassland biodiversity bounces back from long-term nitrogen addition

The negative effect of increasing atmospheric nitrogen (N) pollution on grassland biodiversity is now incontrovertible. However, the recent introduction of cleaner technologies in the UK has led to reductions in the emissions of nitrogen oxides, with concomitant decreases in N deposition. The degree to which grassland biodiversity can be expected to ‘bounce back’ in response to these improvements in air quality is uncertain, with a suggestion that long-term chronic N addition may lead to an alternative low biodiversity state. Here we present evidence from the 160-year-old Park Grass Experiment at Rothamsted Research, UK, that shows a positive response of biodiversity to reducing N addition from either atmospheric pollution or fertilizers. The proportion of legumes, species richness and diversity increased across the experiment between 1991 and 2012 as both wet and dry N deposition declined. Plots that stopped receiving inorganic N fertilizer in 1989 recovered much of the diversity that had been lost, especially if limed. There was no evidence that chronic N addition has resulted in an alternative low biodiversity state on the Park Grass plots, except where there has been extreme acidification, although it is likely that the recovery of plant communities has been facilitated by the twice-yearly mowing and removal of biomass. This may also explain why a comparable response of plant communities to reduced N inputs has yet to be observed in the wider landscape.
The Poetics of Socio-Technical Space: Evaluating the Internet of Things Through Craft

Drawing on semi-structured interviews and cognitive mapping with 14 craftspeople, this paper analyzes the socio-technical arrangements of people and tools in the context of workspaces and productivity. Using actor-network theory and the concept of companionability, both of which emphasize the role of human and non-human actants in the socio-technical fabrics of everyday life, I analyze the relationships between people, productivity and technology through the following themes: embodiment, provenance, insecurity, flow and companionability. The discussion section develops these themes further through comparison with rhetoric surrounding the Internet of Things (IoT). By putting the experiences of craftspeople in conversation with IoT rhetoric, I suggest several policy interventions for understanding connectivity and inter-device operability as material, flexible and respectful of human agency.
Extracellular vesicles: their role in cancer biology and epithelial-mesenchymal transition.

Cell-cell communication is critical across an assortment of physiological and pathological processes. Extracellular vesicles (EVs) represent an integral facet of intercellular communication largely through the transfer of functional cargo such as proteins, messenger RNAs (mRNAs), microRNA (miRNAs), DNAs and lipids. EVs, especially exosomes and shed microvesicles, represent an important delivery medium in the tumour micro-environment through the reciprocal dissemination of signals between cancer and resident stromal cells to facilitate tumorigenesis and metastasis. An important step of the metastatic cascade is the reprogramming of cancer cells from an epithelial to mesenchymal phenotype (epithelial-mesenchymal transition, EMT), which is associated with increased aggressiveness, invasiveness and metastatic potential. There is now increasing evidence demonstrating that EVs released by cells undergoing EMT are reprogrammed (protein and RNA content) during this process. This review summarises current knowledge of EV-mediated functional transfer of proteins and RNA species (mRNA, miRNA, long non-coding RNA) between cells in cancer biology and the EMT process. An in-depth understanding of EVs associated with EMT, with emphasis on molecular composition (proteins and RNA species), will provide fundamental insights into cancer biology.
Preferences for symmetry in human faces in two cultures: data from the UK and the Hadza, an isolated group of hunter-gatherers.

Many studies show agreement within and between cultures for general judgements of facial attractiveness. Few studies, however, have examined the attractiveness of specific traits and few have examined preferences in hunter-gatherers. The current study examined preferences for symmetry in both the UK and the Hadza, a hunter-gatherer society of Tanzania. We found that symmetry was more attractive than asymmetry across both the cultures and was more strongly preferred by the Hadza than in the UK. The different ecological conditions may play a role in generating this difference. Such variation in preference may be adaptive if it reflects adaptation to local conditions. Symmetry is thought to indicate genetic quality, which may be more important among the Hadza with much higher mortality rates from birth onwards. Hadza men who were more often named as good hunters placed a greater value on symmetry in female faces. These results suggest that high quality Hadza men are more discriminating in their choice of faces. Hadza women had increased preferences for symmetry in men's faces when they were pregnant or nursing, perhaps due to their increased discrimination and sensitivity to foods and disease harmful to a foetus or nursing infant. These results imply that symmetry is an evolutionarily relevant trait and that variation in symmetry preference appears strategic both between cultures and within individuals of a single culture.
Challenges in the Automatic Analysis of Students' Diagnostic Reasoning

Diagnostic reasoning is a key component of many professions. To improve students’ diagnostic reasoning skills, educational psychologists analyse and give feedback on epistemic activities used by these students while diagnosing, in particular, hypothesis generation, evidence generation, evidence evaluation, and drawing conclusions. However, this manual analysis is highly time-consuming. We aim to enable the large-scale adoption of diagnostic reasoning analysis and feedback by automating the epistemic activity identification. We create the first corpus for this task, comprising diagnostic reasoning selfexplanations of students from two domains annotated with epistemic activities. Based on insights from the corpus creation and the task’s characteristics, we discuss three challenges for the automatic identification of epistemic activities using AI methods: the correct identification of epistemic activity spans, the reliable distinction of similar epistemic activities, and the detection of overlapping epistemic activities. We propose a separate performance metric for each challenge and thus provide an evaluation framework for future research. Indeed, our evaluation of various state-of-the-art recurrent neural network architectures reveals that current techniques fail to address some of these challenges.
Development of a 5 DOF prosthetic arm for above elbow amputees

Recent progress in biomechatronics technology brings a lot of benefit to increase the mobility of above elbow (AE) amputees in their daily life activities. A prosthetic arm is used to compensate for the lost functions of the AE amputees absent arm. A number of commercial prosthetic arm have been developed since last few decades. However, many amputees have not used them due to the discrepancy between their expectations and the reality. One of the main factors that cause the loss of interest in current commercial prosthetic arm is the lack of desired DOF (degree of freedom) that results unnatural arm motion. In this paper, a 5 DOF externally powered prosthetic arm for AE amputees is proposed to increase their mobility in daily life activities. The proposed prosthesis is designed to generate natural human like arm motion while performing a daily life task. This paper summarizes the design and controllability of the proposed prosthesis.
Vistradas: Visual Analytics for Urban Trajectory Data

In the past few years a growing number of cities have started monitoring the position of public transportation vehicles using GPS devices. Most of these trajectory data are released in raw format and usually have issues, such as measurement errors. Providing insights from these valuable (and noisy) data is a major challenge in larger cities. In this paper we present a system, called Vistradas, for visual analytics of urban trajectory data. Vistradas allows users to analyze use cases related to trajectories of public buses such as: analysis of bus uniformity, verification of bus route, and the impact of events in bus traffic. Our proposed Vistradas system helps user to get insights into various aspects of public transportation.
APPLYING MACHINE LEARNING ALGORITHMS IN SOFTWARE DEVELOPMENT

Machine learning deals with the issue of how to build programs that improve their performance at some task through experience. Machine learning algorithms have proven to be of great practical value in a variety of application domains. They are particularly useful for (a) poorly understood problem domains where littl e knowledge exists for the humans to develop effective algorithms; (b) domains where there are large databases containing valuable implicit regularities to be discovered; or (c) domains where programs must adapt to changing conditions. Not surprisingly, the field of software engineering turns out to be a fertile ground where many software development tasks could be formulated as learning problems and approached in terms of learning algorithms. In this paper, we first take a look at the characteristics and applicabilit y of some frequently utili zed machine learning algorithms. We then provide formulations of some software development tasks using learning algorithms. Finally, a brief summary is given of the existing work.
Management of aneurysmal subarachnoid hemorrhage: State of the art and future perspectives

Aneurysmal subarachnoid hemorrhage (SAH) accounts for 5% of strokes and carries a poor prognosis. It affects around 6 cases per 100,000 patient years occurring at a relatively young age.



Common risk factors are the same as for stroke, and only in a minority of the cases, genetic factors can be found. The overall mortality ranges from 32% to 67%, with 10-20% of patients with long-term dependence due to brain damage. An explosive headache is the most common reported symptom, although a wide spectrum of clinical disturbances can be the presenting symptoms. Brain computed tomography (CT) allow the diagnosis of SAH. The subsequent CT angiography (CTA) or digital subtraction angiography (DSA) can detect vascular malformations such as aneurysms. Non-aneurysmal SAH is observed in 10% of the cases. In patients surviving the initial aneurysmal bleeding, re-hemorrhage and acute hydrocephalus can affect the prognosis.



Although occlusion of an aneurysm by surgical clipping or endovascular procedure effectively prevents rebleeding, cerebral vasospasm and the resulting cerebral ischemia occurring after SAH are still responsible for the considerable morbidity and mortality related to such a pathology. A significant amount of experimental and clinical research has been conducted to find ways in preventing these complications without sound results.



Even though no single pharmacological agent or treatment protocol has been identified, the main therapeutic interventions remain ineffective and limited to the manipulation of systemic blood pressure, alteration of blood volume or viscosity, and control of arterial dioxide tension.
Face Detection and Tracking using OpenCV

—An application for automatic face detection and tracking on video streams from surveillance cameras in public or commercial places is discussed in this paper. In many situations it is useful to detect where the people are looking for, e.g. in exhibits, commercial malls, and public places in buildings. Prototype is designed to work with web cameras for the face detection and tracking system based on open source platforms Arduino and OpenCV. The system is based on AdaBoost algorithm and abstracts faces Haar-Like features. This system can be used for security purpose to record the visitor face as well as to detect and track the face. A program is developed using OpenCV that can detect people's face and also track from the web camera.
How Emotions Change Time

Experimental evidence suggests that emotions can both speed-up and slow-down the internal clock. Speeding up has been observed for to-be-timed emotional stimuli that have the capacity to sustain attention, whereas slowing down has been observed for to-be-timed neutral stimuli that are presented in the context of emotional distractors. These effects have been explained by mechanisms that involve changes in bodily arousal, attention, or sentience. A review of these mechanisms suggests both merits and difficulties in the explanation of the emotion-timing link. Therefore, a hybrid mechanism involving stimulus-specific sentient representations is proposed as a candidate for mediating emotional influences on time. According to this proposal, emotional events enhance sentient representations, which in turn support temporal estimates. Emotional stimuli with a larger share in ones sentience are then perceived as longer than neutral stimuli with a smaller share.
A Neural Attention Model for Urban Air Quality Inference: Learning the Weights of Monitoring Stations

Urban air pollution has attracted much attention these years for its adverse impacts on human health. While monitoring stations have been established to collect pollutant statistics, the number of stations is very limited due to the high cost. Thus, inferring fine-grained urban air quality information is becoming an essential issue for both government and people. In this paper, we propose a generic neural approach, named ADAIN, for urban air quality inference. We leverage both the information from monitoring stations and urban data that are closely related to air quality, including POIs, road networks and meteorology. ADAIN combines feedforward and recurrent neural networks for modeling static and sequential features as well as capturing deep feature interactions effectively. A novel attempt of ADAIN is an attention-based pooling layer that automatically learns the weights of features from different monitoring stations, to boost the performance. We conduct experiments on a real-world air quality dataset and our approach achieves the highest performance compared with various state-of-the-art solutions.
Slip estimation for small-scale robotic tracked vehicles

A method is presented for using an extended Kalman filter with state noise compensation to estimate the trajectory, orientation, and slip variables for a small-scale robotic tracked vehicle. The principal goal of the method is to enable terrain property estimation. The methodology requires kinematic and dynamic models for skid-steering, as well as tractive force models parameterized by key soil parameters. Simulation studies initially used to verify the model basis are described, and results presented from application of the estimation method to both simulated and experimental study of a 60-kg robotic tracked vehicle. Preliminary results show the method can effectively estimate vehicle trajectory relying only on the model-based estimation and onboard sensor information. Estimates of slip on the left and right track as well as slip angle are essential for ongoing work in vehicle-based soil parameter estimation. The favorable comparison against motion capture data suggests this approach will be useful for laboratory and field-based application.
Gesture-Based Human-Computer-Interaction Using Kinect for Windows Mouse Control and PowerPoint Presentation

One of the most important research areas in the field of Human-Computer-Interaction (HCI) is gesture recognition as it provides a natural and intuitive way to communicate between people and machines. Gesture-based HCI applications range from computer games to virtual/augmented reality and is recently being explored in other fields. The idea behind this work is to develop and implement a gesture-based HCI system using the recently developed Microsoft Kinect depth sensor to control the Windows Mouse Cursor as well as PowerPoint presentations. The paper can be divided into two major modules namely, hand detection and gesture recognition. For hand detection, the application uses the Kinect for Windows Software Development Kit (SDK) and its skeletal tracking features to detect a user's hand which enables the user to control the Windows mouse cursor. Gesture recognition involves capturing user gestures and interpreting motions or signs that the user performs to simulate different mouse events.
Combination of Diane-35 and Metformin to Treat Early Endometrial Carcinoma in PCOS Women with Insulin Resistance

Young women with polycystic ovary syndrome (PCOS) have a high risk of developing endometrial carcinoma. There is a need for the development of new medical therapies that can reduce the need for surgical intervention so as to preserve the fertility of these patients. The aim of the study was to describe and discuss cases of PCOS and insulin resistance (IR) women with early endometrial carcinoma while being co-treated with Diane-35 and metformin.



Five PCOS-IR women who were scheduled for diagnosis and therapy for early endometrial carcinoma were recruited. The hospital records and endometrial pathology reports were reviewed. All patients were co-treated with Diane-35 and metformin for 6 months to reverse the endometrial carcinoma and preserve their fertility. Before, during, and after treatment, endometrial biopsies and blood samples were obtained and oral glucose tolerance tests were performed. Endometrial pathology was evaluated. Body weight (BW), body mass index (BMI), follicle-stimulating hormone (FSH), luteinizing hormone (LH), total testosterone (TT), sex hormone-binding globulin (SHBG), free androgen index (FAI), insulin area under curve (IAUC), and homeostasis model assessment of insulin resistance (HOMA-IR) were determined.



Clinical stage 1a, low grade endometrial carcinoma was confirmed before treatment. After 6 months of co-treatment, all patients showed normal epithelia. No evidence of atypical hyperplasia or endometrial carcinoma was found. Co-treatment resulted in significant decreases in BW, BMI, TT, FAI, IAUC, and HOMA-IR in parallel with a significant increase in SHBG. There were no differences in the FSH and LH levels after co-treatment.



Combined treatment with Diane-35 and metformin has the potential to revert the endometrial carcinoma into normal endometrial cells in PCOS-IR women. The cellular and molecular mechanisms behind this effect merit further investigation.
A tutorial on mathematical modeling of biological signaling pathways.

Mathematical models have been widely used in the studies of biological signaling pathways. Among these studies, two systems biology approaches have been applied: top-down and bottom-up systems biology. The former approach focuses on X-omics researches involving the measurement of experimental data in a large scale, for example proteomics, metabolomics, or fluxomics and transcriptomics. In contrast, the bottom-up approach studies the interaction of the network components and employs mathematical models to gain some insights about the mechanisms and dynamics of biological systems. This chapter introduces how to use the bottom-up approach to establish mathematical models for cell signaling studies.
A Probabilistic Retrieval Model for Semistructured Data

Retrieving semistructured (XML) data typically requires either a structured query such as XPath, or a keyword query that does not take structure into account. In this paper, we infer structural information automatically from keyword queries and incorporate this into a retrieval model. More specifically, we propose the concept of a mapping probability, which maps each query word into a related field (or XML element). This mapping probability is used as a weight to combine the language models estimated from each field. Experiments on two test collections show that our retrieval model based on mapping probabilities outperforms baseline techniques significantly.
Scalable Distributed Computing Hierarchy: Cloud, Fog and Dew Computing

The paper considers the conceptual approach for organization of the vertical hierarchical links between the scalable distributed computing paradigms: Cloud Computing, Fog Computing and Dew Computing. In this paper, the Dew Computing is described and recognized as a new structural layer in the existing distributed computing hierarchy. In the existing computing hierarchy, the Dew computing is positioned as the ground level for the Cloud and Fog computing paradigms. Vertical, complementary, hierarchical division from Cloud to Dew Computing satisfies the needs of highand low-end computing demands in everyday life and work. These new computing paradigms lower the cost and improve the performance, particularly for concepts and applications such as the Internet of Things (IoT) and the Internet of Everything (IoE). In addition, the Dew computing paradigm will require new programming models that will efficiently reduce the complexity and improve the productivity and usability of scalable distributed computing, following the principles of High-Productivity computing. TYPE OF PAPER AND