nils-herrmann/clean_scirepeval huggingface dataset:
Investigations and stability analyses of Malin village landslide of Pune district, Maharashtra, India
Venue: Natural Hazards

This paper presents field investigations and numerical analyses of the landslide, affecting Malin village of Pune district in Maharashtra, India. The Malin village was wiped out due to Malin landslide, occurred on July 30, 2014; however, only a primary school and few houses remained safe during the event, and mass of the people buried in debris of slide. To study the causes of the event, field study has been carried out. Representative samples of slope forming geomaterials (soil/rock) have been collected at three locations of the hill viz. L1 (bottom of the hill) L2 (middle of the hill) and L3 (top of the hill) along with massive and vesicular basalt for the determination of the geotechnical properties in the laboratory. The estimated geotechnical properties have been used for numerical modeling of the hill slope that has been performed to calculate factor of safety, maximum displacement, displacement direction and accumulated maximum shear strain with the help of numerical programs based on limit equilibrium method and finite element method approaches, respectively. This study shows that the hill slope was unstable with FoS 1 and prone to failure. It was triggered by various man made and natural factors like heavy rainfall, unscientific construction activities at the top of the hill and along the hill, unplanned cultivations and lack of drainage system. Also, the results of the numerical analysis can be successfully implemented to minimize/reduce impact and frequency of landslide in the area of similar morphology.
COMPARATIVE ANALYSIS OF QUALITY METRICS FOR COMMUNITY DETECTION IN SOCIAL NETWORKS USING GENETIC ALGORITHM

Web 2.0 has led to the expansion and evolution of web based communities that enable people to share information and communicate on shared platforms. The inclination of individuals towards other individuals of similar choices, decisions and preferences to get related in a social network prompts the development of groups or communities. The identification of community structure is one of the most challenging task that has received a lot of attention from the researchers. Network community structure detection can be expressed as an optimisation problem. The objective function selected captures the instinct of a community as a group of nodes in which intra group connections are much denser than inter group connections. However, this problem often cannot be well solved by traditional optimisation methods due to the inherent complexity of network structure. Therefore, evolutionary algorithms have been embraced to deal with community detection problem. Many objective functions have been proposed to capture the notion of quality of a network community. In this paper, we assessed the performance of four important objective functions namely Modularity, Modularity Density, Community Score and Community Fitness on real world benchmark networks, using Genetic Algorithm (GA) The performance measure taken to assess the quality of partitions is NMI (Normalized mutual information) From the experimental results, we found that the communities' identified by these objectives have different characteristics and modularity density outperformed the other three objective functions by uncovering the true community structure of the networks. The experimental results provide a direction to researchers on choosing an objective function to measure the quality of community structure in various domains like social networks, biological networks, information and technological networks.
GIS based multi criteria analysis method for flood risk assessment under urbanization
Venue: 2016 24th International Conference on Geoinformatics

Flooding is one of the most common natural hazards in China, and many flood events have caused tremendous economic losses and casualties. Moreover, the process of urbanization aggravates the regional threat posed by flooding. Assessment of flood prone areas is of great importance for watershed management and reduction of potential losses. In this study, the spatial multi criteria analysis (MCA) method coupled with geographic information system (GIS) is employed to assess flood risk based on topographic and hydrological factors. The land use change under urbanization is also taken into consideration to observe the temporal change of regional flood hazard. According to the results of risk index, the flood risk in the majority of study area is in low level range. The area that received a high risk level is in the urban region, and the risk presents an increasing trend in the years 1993, 2003, and 2013.
Human genetic engineering and social justice in South Africa: Moltmann and human dignity

The realities of social injustice in the present South African context, with its great and growing gap between rich and poor and unequal distribution of wealth and resources, are also acutely visible in the health care sector. Genetic engineering would lead to some children having the cards stacked overwhelmingly in their favour, raising the concern for the justice or fairness of this type of biotechnology. In this contribution, I argue that the notion of justice as fairness, put forward by Rawls, and the focus on human dignity in Moltmann's theology can help address the bioethical challenges of genetic engineering in the context of inequality, specifically in South Africa.
Making the Invisible Visible: Integrating Engineering for Social Justice Criteria in Humanities and Social Science Courses

Engineering practice always involves social and technical dimensions. However, the bulk of an engineering education gives students little practice in thinking sociotechnically. The organization and content of the curriculum is much at fault here. While engineering science courses focus largely on the technical, engineering design courses sometimes integrate the social and the technical but in ways where the social is reduced to economic and client based constraints at the expense of broader socio cultural dimensions. Meanwhile, courses for undergraduate engineers in the Humanities and Social Sciences (HSS) focus largely on the social but marginalize linkages to the technical. This paper addresses the sociotechnical gap in the HSS by identifying six classroom tested Engineering for Social Justice criteria. We examine how those criteria function to foster enriched learning on the sociotechnical dimensions of engineering practice in two HSS courses. To conclude, we discuss both the benefits and limitations of using those criteria.
Language Models with RNNs for Rescoring Hypotheses of Russian ASR
Venue: ISNN

In this paper, we describe a research of recurrent neural networks (RNNs) for language modeling in large vocabulary continuous speech recognition for Russian. We experimented with recurrent neural networks with different number of units in the hidden layer. RNN based and 3 gram language models (LMs) were trained using the text corpus of 350M words. Obtained RNN based language models were used for N best list rescoring for automatic continuous Russian speech recognition. We tested also a linear interpolation of RNN LMs with the baseline 3 gram LM and achieved 14 relative reduction of the word error rate (WER) with respect to the baseline 3 gram model.
An empirical analysis of the determinants of mobile instant messaging appropriation in university learning
Venue: J. Comput. High. Educ.

Research on technology adoption often profiles device usability (such as perceived usefulness) and user dispositions (such as perceived ease of use) as the prime determinants of effective technology adoption. Since any process of technology adoption cannot be conceived out of its situated contexts, this paper argues that any pre occupation with technology acceptance from the perspective of device usability and user dispositions potentially negates enabling contexts that make successful adoption a reality. Contributing to contemporary debates on technology adoption, this study presents flexible mobile learning contexts comprising cost (device cost and communication cost) device capabilities (portability, collaborative capabilities) and learner traits (learner control) as antecedents that enable the sustainable uptake of emerging technologies. To explore the acceptance and capacity of mobile instant messaging systems to improve student performance, the study draws on these antecedents, develops a factor model and empirically tests it on tertiary students at a South African University of Technology. The study involved 223 national diploma and bachelor's degree students and employed partial least squares for statistical analysis. Overall, the proposed model displayed a good fit with the data and rendered satisfactory explanatory power for students' acceptance of mobile learning. Findings suggest that device portability, communication cost, collaborative capabilities of device and learner control are the main drivers of flexible learning in mobile environments. Flexible learning context facilitated by learner control was found to have a positive influence on attitude towards mobile learning and exhibited the highest path coefficient of the overall model. The study implication is that educators need to create varied learning opportunities that leverage learner control of learning in mobile learning systems to enhance flexible mobile learning. The study also confirmed the statistical significance of the original Technology Acceptance Model constructs.
Ultra micro liquid volume measurement system based on potentiometrie titration principle
Venue: 2016 International Conference on Integrated Circuits and Microsystems (ICICM)

In order to overcome the shortcoming that influences of evaporation capacity on the gravimetric method, and meet the requirement of online measurement, a non gravimetric method is introduced for measuring micro liquid volume. According to Faraday's law and Karl Fischer electrochemical principle, the Karl Fischer potentiometric titration method (KFCT method) is also investigated for micro liquid volume measurement, which calculates micro water content by measuring consumed electric quantity in electrolytic process, and obtains the micro liquid volume by combining liquid density. Due to pressure variation the correction is used for micro liquid volume calculation. Comparing with gravimetric method, experimental systems for non gravimetric method is designed. The experiment indicated that the test result obtained by using KFCT method is in good agreement with that the test result obtained by using gravimetric method, and meets the technical requirements of ISO 8655, but the measured result of KFCT method is slightly higher. Comparing with gravimetric method, the non gravimetric method provides a better solution for micro liquid volume measurement, which has lower requirements on the measurement environment, is easier to implement on line calibration, and effectively reduce the impact of the liquid evaporation.
Error related brain activity and error awareness in an error classification paradigm
Venue: NeuroImage

Error related brain activity has been linked to error detection enabling adaptive behavioral adjustments. However, it is still unclear which role error awareness plays in this process. Here, we show that the error related negativity (Ne/ERN) an event related potential reflecting early error monitoring, is dissociable from the degree of error awareness. Participants responded to a target while ignoring two different incongruent distractors. After responding, they indicated whether they had committed an error, and if so, whether they had responded to one or to the other distractor. This error classification paradigm allowed distinguishing partially aware errors, (i.e. errors that were noticed but misclassified) and fully aware errors (i.e. errors that were correctly classified) The Ne/ERN was larger for partially aware errors than for fully aware errors. Whereas this speaks against the idea that the Ne/ERN foreshadows the degree of error awareness, it confirms the prediction of a computational model, which relates the Ne/ERN to post response conflict. This model predicts that stronger distractor processing a prerequisite of error classification in our paradigm leads to lower post response conflict and thus a smaller Ne/ERN. This implies that the relationship between Ne/ERN and error awareness depends on how error awareness is related to response conflict in a specific task. Our results further indicate that the Ne/ERN but not the degree of error awareness determines adaptive performance adjustments. Taken together, we conclude that the Ne/ERN is dissociable from error awareness and foreshadows adaptive performance adjustments. Our results suggest that the relationship between the Ne/ERN and error awareness is correlative and mediated by response conflict.
The Semantic Knowledge Graph: A Compact, Auto Generated Model for Real Time Traversal and Ranking of any Relationship within a Domain
Venue: 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA)

This paper describes a new kind of knowledge representation and mining system which we are calling the Semantic Knowledge Graph. At its heart, the Semantic Knowledge Graph leverages an inverted index, along with a complementary uninverted index, to represent nodes (terms) and edges (the documents within intersecting postings lists for multiple terms/nodes) This provides a layer of indirection between each pair of nodes and their corresponding edge, enabling edges to materialize dynamically from underlying corpus statistics. As a result, any combination of nodes can have edges to any other nodes materialize and be scored to reveal latent relationships between the nodes. This provides numerous benefits: the knowledge graph can be built automatically from a real world corpus of data, new nodes along with their combined edges can be instantly materialized from any arbitrary combination of preexisting nodes (using set operations) and a full model of the semantic relationships between all entities within a domain can be represented and dynamically traversed using a highly compact representation of the graph. Such a system has widespread applications in areas as diverse as knowledge modeling and reasoning, natural language processing, anomaly detection, data cleansing, semantic search, analytics, data classification, root cause analysis, and recommendations systems. The main contribution of this paper is the introduction of a novel system the Semantic Knowledge Graph which is able to dynamically discover and score interesting relationships between any arbitrary combination of entities (words, phrases, or extracted concepts) through dynamically materializing nodes and edges from a compact graphical representation built automatically from a corpus of data representative of a knowledge domain. The source code for our Semantic Knowledge Graph implementation is being published along with this paper to facilitate further research and extensions of this work.
Valuing the Economic Costs of Allergic Rhinitis, Acute Bronchitis, and Asthma from Exposure to Indoor Dampness and Mold in the US
Venue: Journal of environmental and public health

Two foundational methods for estimating the total economic burden of disease are cost of illness (COI) and willingness to pay (WTP) WTP measures the full cost to society, but WTP estimates are difficult to compute and rarely available. COI methods are more often used but less likely to reflect full costs. This paper attempts to estimate the full economic cost (2014$ of illnesses resulting from exposure to dampness and mold using COI methods and WTP where the data is available. A limited sensitivity analysis of alternative methods and assumptions demonstrates a wide potential range of estimates. In the final estimates, the total annual cost to society attributable to dampness and mold is estimated to be $3.7 (2.3 4.7) billion for allergic rhinitis, $1.9 (1.1 2.3) billion for acute bronchitis, $15.1 (9.4 20.6) billion for asthma morbidity, and $1.7 (0.4 4.5) billion for asthma mortality. The corresponding costs from all causes, not limited to dampness and mold, using the same approach would be $24.8 billion for allergic rhinitis, $13.5 billion for acute bronchitis, $94.5 billion for asthma morbidity, and $10.8 billion for asthma mortality.
Microorganisms Causing Community Acquired Acute Bronchitis: The Role of Bacterial Infection
Venue: PloS one

Although acute bronchitis is quite common, there is relatively limited information regarding the microorganisms that are involved in this illness.  We performed a prospective study of acute bronchitis at 31 hospitals and clinics in Korea from July 2011 to June 2012. Sputum specimens were collected for polymerase chain reaction (PCR) and culture of microorganisms.  Of the 811 enrolled patients, 291 had acceptable sputum specimens that were included for analysis of the etiologic distribution. With multiplex PCR testing, viruses were identified in 36.1% (105/291) most commonly rhinovirus (25.8% and coronavirus (3.8% Typical bacteria were isolated in 126/291 (43.3% patients. Among these patients Haemophilus influenzae (n 39) and Streptococcus pneumoniae (n 30) were isolated most commonly; atypical bacteria were identified in 44 (15.1% patients. Bacteria only, virus only, and mixed infections (bacteria plus virus) accounted for 36.7% (98/291) 17.2% (50/291) and 18.9% (55/291) of infections, respectively. In particular, 52.4% of patients with viral infection had a concurrent bacterial infection, and rhinovirus was the most common virus in mixed infections (40/55) Additionally, infections with typical bacteria were more common in patients with chronic lung disease (p 0.029) and typical bacterial infections showed a trend towards a higher prevalence with older age (p 0.001) Conclusions Bacteria were associated with almost half of community acquired acute bronchitis cases. Additional studies are required to further illuminate the role of bacteria and to identify patient groups most likely to benefit from antibiotic treatment.
Acute Bronchitis.
Venue: American family physician

Cough is the most common illness related reason for ambulatory care visits in the United States. Acute bronchitis is a clinical diagnosis characterized by cough due to acute inflammation of the trachea and large airways without evidence of pneumonia. Pneumonia should be suspected in patients with tachypnea, tachycardia, dyspnea, or lung findings suggestive of pneumonia, and radiography is warranted. Pertussis should be suspected in patients with cough persisting for more than two weeks that is accompanied by symptoms such as paroxysmal cough, whooping cough, and post tussive emesis, or recent pertussis exposure. The cough associated with acute bronchitis typically lasts about two to three weeks, and this should be emphasized with patients. Acute bronchitis is usually caused by viruses, and antibiotics are not indicated in patients without chronic lung disease. Antibiotics have been shown to provide only minimal benefit, reducing the cough or illness by about half a day, and have adverse effects, including allergic reactions, nausea and vomiting, and Clostridium difficile infection. Evaluation and treatment of bronchitis include ruling out secondary causes for cough, such as pneumonia; educating patients about the natural course of the disease; and recommending symptomatic treatment and avoidance of unnecessary antibiotic use. Strategies to reduce inappropriate antibiotic use include delayed prescriptions, patient education, and calling the infection a chest cold.
Robust transcriptome wide discovery of RNA binding protein binding sites with enhanced CLIP (eCLIP)
Venue: Nature Methods

As RNA binding proteins (RBPs) play essential roles in cellular physiology by interacting with target RNA molecules, binding site identification by UV crosslinking and immunoprecipitation (CLIP) of ribonucleoprotein complexes is critical to understanding RBP function. However, current CLIP protocols are technically demanding and yield low complexity libraries with high experimental failure rates. We have developed an enhanced CLIP (eCLIP) protocol that decreases requisite amplification by ~1,000 fold, decreasing discarded PCR duplicate reads by ~60% while maintaining single nucleotide binding resolution. By simplifying the generation of paired IgG and size matched input controls, eCLIP improves specificity in the discovery of authentic binding sites. We generated 102 eCLIP experiments for 73 diverse RBPs in HepG2 and K562 cells (available at https:/www.encodeproject.org) demonstrating that eCLIP enables large scale and robust profiling, with amplification and sample requirements similar to those of ChIP seq. eCLIP enables integrative analysis of diverse RBPs to reveal factor specific profiles, common artifacts for CLIP and RNA centric perspectives on RBP activity.
Towards AI Complete Question Answering: A Set of Prerequisite Toy Tasks
Venue: ICLR

One long term goal of machine learning research is to produce methods that are applicable to reasoning and natural language, in particular building an intelligent dialogue agent. To measure progress towards that goal, we argue for the usefulness of a set of proxy tasks that evaluate reading comprehension via question answering. Our tasks measure understanding in several ways: whether a system is able to answer questions via chaining facts, simple induction, deduction and many more. The tasks are designed to be prerequisites for any system that aims to be capable of conversing with a human. We believe many existing learning systems can currently not solve them, and hence our aim is to classify these tasks into skill sets, so that researchers can identify (and then rectify) the failings of their systems. We also extend and improve the recently introduced Memory Networks model, and show it is able to solve some, but not all, of the tasks.
Psychologically Based Virtual Suspect for Interrogative Interview Training
Venue: IVA

This paper presents a Virtual Suspect system designed for use in police interrogation training simulations. The system allows users to preconfigure various scenarios based on real cases, as well as different suspect histories and personality types. The responses given by the Virtual Suspect during the interrogation are selected based on context and the suspect's psychological state, which changes in response to each interrogator's statement. Experiments with 24 subjects have shown that the Virtual Suspect's responses in an interrogation scenario are similar to those of a human respondent.
Investigating Social Networking applications on smartphones detecting Facebook, Twitter, LinkedIn and Google+ artefacts on Android and iOS platforms

The rapid growth in usage and application of Social Networking (SN) platforms make them a potential target by cyber criminals to conduct malicious activities such as identity theft, piracy, illegal trading, sexual harassment, cyber stalking and cyber terrorism. Many SN platforms are extending their services to mobile platforms, making them an important source of evidence in cyber investigation cases. Therefore, understanding the types of potential evidence of users' SN activities available on mobile devices is crucial to forensic investigation and research. In this paper, we examine four popular SN applications: Facebook, Twitter, LinkedIn and Google+ on Android and iOS platforms, to detect remnants of users' activities that are of forensic interest. We detect a variety of artefacts (e.g. usernames, passwords, login information, personal information, uploaded posts, exchanged messages and uploaded comments from SN applications) that could facilitate a criminal investigation.