nils-herrmann/clean_scirepeval huggingface dataset:
Mining correlation between locations using human location history
Venue: GIS
Year: 2009

The advance of location acquisition technologies enables people to record their location histories with spatio temporal datasets, which imply the correlation between geographical regions. This correlation indicates the relationship between locations in the space of human behavior, and can enable many valuable services, such as sales promotion and location recommendation. In this paper, by taking into account a user's travel experience and the sequentiality locations have been visited, we propose an approach to mine the correlation between locations from a large number of users' location histories. We conducted a personalized location recommendation system using the location correlation, and evaluated this system with a large scale real world GPS dataset. As a result, our method outperforms the related work using the Pearson correlation.
A Structured Review of the Validity of BLEU
Venue: Computational Linguistics
Year: 2018

The BLEU metric has been widely used in NLP for over 15 years to evaluate NLP systems, especially in machine translation and natural language generation. I present a structured review of the evidence on whether BLEU is a valid evaluation technique, in other words whether BLEU scores correlate with real world utility and user satisfaction of NLP systems; this review covers 284 correlations reported in 34 papers. Overall, the evidence supports using BLEU for diagnostic evaluation of MT systems (which is what it was originally proposed for) but does not support using BLEU outwith MT, for evaluation of individual texts, or for scientific hypothesis testing.
GS WGAN: A Gradient Sanitized Approach for Learning Differentially Private Generators
Venue: NeurIPS
Year: 2020

The wide spread availability of rich data has fueled the growth of machine learning applications in numerous domains. However, growth in domains with highly sensitive data (e.g. medical) is largely hindered as the private nature of data prohibits it from being shared. To this end, we propose Gradient sanitized Wasserstein Generative Adversarial Networks (GS WGAN) which allows releasing a sanitized form of the sensitive data with rigorous privacy guarantees. In contrast to prior work, our approach is able to distort gradient information more precisely, and thereby enabling training deeper models which generate more informative samples. Moreover, our formulation naturally allows for training GANs in both centralized and federated (i.e. decentralized) data scenarios. Through extensive experiments, we find our approach consistently outperforms state of the art approaches across multiple metrics (e.g. sample quality) and datasets.
The Limited Effects of Federal Environmental Justice Policy on State Enforcement
Year: 2009

The federal government adopted several measures during the mid 1990s to address concerns about race and class based disparities in environmental protection. This article examines whether these measures affected the pattern of state enforcement of three federal pollution control laws. Using differences in differences models to estimate the effects of the federal policy adoption, I find evidence of increases in state enforcement of the Clean Air Act in large African American communities, but declines in enforcement in communities with large poor and Hispanic populations. Similarly there is evidence that state enforcement of the Clean Water Act decreased in poor and African American communities, but there were no real changes in enforcement of facilities regulated under the Resource Conservation and Recovery Act. Collectively, the analysis suggests that the federal policy had minimal positive effects on state regulatory enforcement.
Strategi Pengembangan Usaha pada Industri Kayu Gergajian Melalui Pendekatan SWOT dan IFAS EFAS Analysis (Business Development Strategy in Sawn Timber Industry Through the SWOT Approach and IFAS EFAS Analysis)
Year: 2019

Sawn timber industry is one of the growing timber industries in Indonesia; however, this industry is faced with several problems, including lack of raw materials, product quality, in efficiency and similar industry competition. The purpose of this study was to determine the quadrant position in the SWOT diagram of sawmill lumber industry and determine the most appropriate strategy in accordance with the position of the quadrant on the SWOT diagram. In depth interviews method was select to measure strengths; weaknesses, opportunity and threat included senior manager operational and employees of the PT. Madani Cahaya utama. Data were analyzed using SWOT in which the scores of internal factor (Internal Factor Analysis Summary IFAS) and external factor (External Factor Analysis Summary EFAS) were calculated. The results showed that score of internal factor and external factor were greater than 2. Hence, the position of SWOT for the small sawmill was located in quadrant 1, suggesting that a company has strength to take an advantage of the existing opportunities.
Shifting Religious Identities and Sharia in Othello
Venue: Religions
Year: 2019

Despite twenty first century research advances regarding the role of Islam in Shakespeare's plays, questions remain concerning the extent of William Shakespeare's knowledge of Muslim culture and his use of that knowledge in writing Othello. I suggest that the playwright had access to numerous sources that informed his depiction of Othello as a man divided between Christian faith and Islamic duty, a division which resulted in the Moor's destruction. Sharia, a code of moral and legal conduct for Muslims based on the Qur'an's teachings, appears to be a guiding force in Othello's ultimate quest for honor. The advance of the Ottoman Empire into Europe with the threat of conquest and forced conversion to Islam was a source of fascination and fear to Elizabethan audiences. Yet, as knowledge increased, so did tolerance to a certain degree. But the defining line between Christian and Muslim remained a firm one that could not be breached without risking the loss of personal identity and spiritual sanctity. Denizens of the Middle East and followers of the Islamic faith, as well as travel encounters between eastern and western cultures, influenced Shakespeare's treatment of this theme. His play Othello is possibly the only drama of this time period to feature a Moor protagonist who wavers between Christian and Muslim beliefs. To better understand the impetus for Othello's murder of his wife, the influence of Islamic culture is considered, and in particular, the system of Sharia that governs social, political, and religious conventions of Muslim life, as well as Othello's conflicting loyalties between Islam as the religion of his youth, and Christianity, the faith to which he had been converted. From Act I celebrating his marriage through Act V recording his death, Othello is overshadowed by fears of who he really is uncertainty bred of his conversion to Christian faith and his potential to revert to Islamic duty. Without indicating Sharia directly, Shakespeare hints at its subtle influence as Othello struggles between two faiths and two theologies. In killing Desdemona and orchestrating Michael Cassio's death in response to their alleged adultery, Othello obeys the Old Testament injunction for personal sanctification. But in reverting to Muslim beliefs, he attempts to follow potential Sharia influence to reclaim personal and societal honor.
Diagnosis and management of McFarland fractures.
Venue: The American journal of emergency medicine
Year: 2018

McFarland fracture is the eponym for a rare Salter Harris III or IV fracture involving the medial distal tibia. These fractures can be difficult to diagnose without a high index suspicion and appropriate radiographic imaging. These fractures may result in significant growth disturbances to the pediatric patient. When diagnosed and treated acutely, these fractures can be managed with cast immobilization and close follow up. If diagnoses in a delayed fashion they can result in significant morbidity including prolonged casting and possible surgical treatment.  In this case report we discuss a pediatric patient with a delayed presentation McFarland fracture which was initially diagnosed and treated as an ankle sprain. He required a prolonged course of treatment and we describe his clinical progression. We review the literature regarding this fracture pattern including history, acute management and outcomes. WHY SHOULD AN EMERGENCY PHYSICIAN BE AWARE OF THIS? McFarland fractures are rare Salter Harris III fractures of the medial malleolus. These ankle fractures affect patients nearing skeletal maturity and may be difficult to diagnose without the appropriate orthogonal X ray imaging. Also, a missed diagnosis can lead to unnecessary morbidity to the patient. If diagnosed acutely, these fractures can be easily treated with immobilization but if allowed to become chronic they require prolonged periods of casting and possibly even surgical intervention. A patient with a specific constellation of symptoms and history should raise suspicion for these injuries and prompt a thorough workup.
One Equation Turbulence Model Based on Extended Bradshaw Assumption
Year: 2015

A one equation turbulence model is presented. The modeling tries to adopt a more reliable source and less empirical coefficients. The governing equation is the turbulent kinetic energy equation, in which e is closed phenomenologically. The stress intensity ratio t12/k is calibrated to be a function controlled by the local variables but not a constant 0.3. The extension of the Bradshaw assumption turns out to be of good accuracy, forming a new Reynolds stress constitutive relation. The two model coefficients are both extracted from one slice of the flat plate boundary layer, yet extensive tests show that the model can significantly improve the precision of prediction for the boundary layer flows, the adverse pressure gradient, shallow separation, transport effects, and massively separated flows.
Superpixel Contracted Graph Based Learning for Hyperspectral Image Classification
Venue: IEEE Transactions on Geoscience and Remote Sensing
Year: 2020

A central problem in hyperspectral image (HSI) classification is obtaining high classification accuracy when using a limited amount of labeled data. In this article we present a novel graph based semi supervised framework to tackle this problem. Our framework uses a superpixel approach, allowing it to define meaningful local regions in HSIs, which with high probability share the same classification label. We then extract spectral and spatial features from these regions and use them to produce a contracted weighted graph representation, where each node represents a region rather than a pixel. The graph is then fed into a graph based semi supervised classifier which gives the final classification. We show that using superpixels in a graph representation is an effective tool for speeding up graphical classifiers applied to HSIs. We demonstrate through exhaustive quantitative and qualitative results that our proposed method produces accurate classifications when an incredibly small amount of labeled data is used. We show that our approach mitigates the major drawbacks of existing approaches, resulting in our approach outperforming several comparative state of the art techniques.
Impact of Corporate Governance on Financial Institutions' Performance: A Board Composition Case
Year: 2020

The impact and implementation of Corporate Governance (CG) has been a matter of interest for various firms, as it substantially affects the financial stability of institutions. This study analyses the impact of Corporate Governance (CG) elements that include board composition on the financial performances of the financial institutions listed on Jordan?s Amman Stock Exchange (ASE) The study population involved 40 Jordanian financial institutions belonging to both commercial banks and financial companies listed on the Amman Stock exchange (ASE) Spearman correlation was used to identify the relationship between corporate governance and financial performance of institutions. It showed an insignificant correlation between board composition in general and companies? financial performance. In the relationship between each board composition factor to each financial performance measure, the study found positive, negative, and mixed correlation results. While analyzing the institution?s performance, any prospective researcher should consider other CG factors so that the study could better represent the CG impact on institutions? performance in developing countries such as Jordan.
The Influence of Feelings While Driving Regular Cars on the Perception and Acceptance of Self Driving Cars.
Venue: Risk analysis an official publication of the Society for Risk Analysis
Year: 2019

Self driving vehicles will affect the future of transportation, but factors that underlie perception and acceptance of self driving cars are yet unclear. Research on feelings as information and the affect heuristic has suggested that feelings are an important source of information, especially in situations of complexity and uncertainty. In this study (N 1,484) we investigated how feelings related to traditional driving affect risk perception, benefit perception, and trust related to self driving cars as well as people's acceptance of the technology. Due to limited experiences with and knowledge of self driving cars, we expected that feelings related to a similar experience, namely, driving regular cars, would influence judgments of self driving cars. Our results support this assumption. While positive feelings of enjoyment predicted higher benefit perception and trust, negative affect predicted higher risk and higher benefit perception of self driving cars. Feelings of control were inversely related to risk and benefit perception, which is in line with research on the affect heuristic. Furthermore, negative affect was an important source of information for judgments of use and acceptance. Interest in using a self driving car was also predicted by lower risk perception, higher benefit perception, and higher levels of trust in the technology. Although people's individual experiences with advanced vehicle technologies and knowledge were associated with perceptions and acceptance, many simply have never been exposed to the technology and know little about it. In the absence of this experience or knowledge, all that is left is the knowledge, experience, and feelings they have related to regular driving.
Coordinating on lower prices: pharmaceutical pricing under political pressure.
Venue: The Rand journal of economics
Year: 2006

We investigate the effects of political activity on pharmaceutical prices, focusing on the health care reform period in the early 1990s.We characterize firms based on their vulnerability to future price regulation and find that the more vulnerable firms were more likely to take various actions to forestall regulation, most notably coordinating on a specific percentage price increase during 1993. Since moderating price increases could have averted regulation, the coordination appears to be the industry's response to a collective action problem.
On modular forms for some noncongruence subgroups of SL2(Z)
Year: 2008

In this paper, we consider modular forms for finite index subgroups of the modular group whose Fourier coefficients are algebraic. It is well known that the Fourier coefficients of any holomorphic modular form for a congruence subgroup (with algebraic coefficients) have bounded denominators. It was observed by Atkin and Swinnerton Dyer that this is no longer true for modular forms for noncongruence subgroups and they pointed out that unbounded denominator property is a clear distinction between modular forms for noncongruence and congruence modular forms. It is an open question whether genuine noncongruence modular forms (with algebraic coefficients) always satisfy the unbounded denominator property. Here, we give a partial positive answer to the above open question by constructing special finite index subgroups of SL2(Z) called character groups and discuss the properties of modular forms for some groups of this kind.
Split crest technique: a solution for atrophic anterior maxilla case report
Year: 2018

The rehabilitation of atrophic anterior maxilla can be done by different techniques. Among the procedures for bone augmentation, we can use block grafting, guided bone regeneration, and split crest technique (SCT) SCT consists in bone crest osteotomy, followed by manual/mechanical expansion up to the splitting of the buccal plate from the lingual/palatal plate through greenstick fracture. SCT advantage is the possibility of simultaneously installing a dental implant. However, SCT planning should consider the remaining bone width and the the flap type to obtain success.  To report a case of implant supported rehabilitation of an atrophic anterior maxilla using the split crest technique with insertion of 4 immediate implants, showing the effectiveness of the technique.
An Analysis of the Translation of Cultural Humor in the Novel "Huo Zhe"
Year: 2019

185 An Analysis of the Translation of Cultural Humor in the Novel "Huo Zhe" Xiao Qin (Xiao Qin) Pratomo Widodo Sulis Triyono Master, Graduate School, Yogyakarta State University, Indonesia Head of Applied Linguistics Department, Graduate School, Yogyakarta State University, Indonesia Teacher, Graduate School, Yogyakarta State University, Indonesia  Xiao Qin (Xiao Qin) E mail: xiaoqin190323@foxmail.com
Design and Development of a BlueBee Gateway for Bluetooth and ZigBee Wireless Protocols
Venue: 2011 IEEE Electronics, Robotics and Automotive Mechanics Conference
Year: 2011

The ubiquity of the new generation of mobile devices with embedded wireless capability is one of the most rapidly growing technologies in the Short Range Wireless Networks applications. In this paper, we propose to combine the functions and capabilities of the maturity wireless protocol such as Bluetooth with the simplified data processing of ZigBee technologies, to design a powerful gateway tool for embedded systems: the BlueBee system. In addition, our approach describes the design based on UML diagrams. Also included, is the construction of the BlueBee node, providing a flexible platform with the possibility of expanding the functionality.
The effects of suggested tags and autocomplete features on social tagging behaviors
Year: 2020

Many websites employ social tagging to allow users to label and classify information. These tagging user interfaces use a variety of features to support efficient and consistent tag creation, including suggested tags and autocomplete for tags. This study uses a custom built tagging interface in a controlled experiment to determine how these features affect social tagging behavior. The study finds that suggested tags do not have a significant effect on the number of tags, number of unique tags, number of typos, or time elapsed per tagged provided. However, autocomplete significantly increases the number and consistency of tags provided, significantly decreases the rate of typos, and significantly decreases the elapsed time per tag provided. These findings for the autocomplete feature align with the priorities and constraints of social tagging folksonomies that support retrieval and site navigation and suggest that autocomplete is an important aid for text entry in social tagging user interfaces.
Tackling maladaptive memories through reconsolidation: From neural to clinical science
Venue: Neurobiology of Learning and Memory
Year: 2017

HighlightsInduction of reconsolidation is complex, posing difficulties for clinical translation.Experimental work in humans and animals can inform clinical approaches.  Behavioral neuroscience has greatly informed how we understand the formation, persistence, and plasticity of memory. Research has demonstrated that memory reactivation can induce a labile period, during which previously consolidated memories are sensitive to change, and in need of restabilization. This process is known as reconsolidation. Such findings have advanced not only our basic understanding of memory processes, but also hint at the prospect of harnessing these insights for the development of a new generation of treatments for disorders of emotional memory. However, even in simple experimental models, the conditions for inducing memory reconsolidation are complex: memory labilization appears to result from the interplay of learning history, reactivation, and also individual differences, posing difficulties for the translation of basic experimental research into effective clinical interventions. In this paper, we review a selection of influential animal and human research on memory reconsolidation to illustrate key insights these studies afford. We then consider how these findings can inform the development of new treatment approaches, with a particular focus on the transition of memory from reactivation, to reconsolidation, to new memory formation, as well as highlighting possible limitations of experimental models. If the challenges of translational research can be overcome, and if reconsolidation based procedures become a viable treatment option, then they would be one of the first mental health treatments to be directly derived from basic neuroscience research. This would surely be a triumph for the scientific study of mind and brain.
Sentiment Analysis in Drug Reviews using Supervised Machine Learning Algorithms
Venue: ArXiv
Year: 2020

Sentiment Analysis is an important algorithm in Natural Language Processing which is used to detect sentiment within some text. In our project, we had chosen to work on analyzing reviews of various drugs which have been reviewed in form of texts and have also been given a rating on a scale from 1 10. We had obtained this data set from the UCI machine learning repository which had 2 data sets: train and test (split as 75 25\ We had split the number rating for the drug into three classes in general: positive (7 10) negative (1 4) or neutral(4 7) There are multiple reviews for the drugs that belong to a similar condition and we decided to investigate how the reviews for different conditions use different words impact the ratings of the drugs. Our intention was mainly to implement supervised machine learning classification algorithms that predict the class of the rating using the textual review. We had primarily implemented different embeddings such as Term Frequency Inverse Document Frequency (TFIDF) and the Count Vectors (CV) We had trained models on the most popular conditions such as "Birth Control" "Depression" and "Pain" within the data set and obtained good results while predicting the test data sets.