nils-herrmann/clean_scirepeval huggingface dataset:
REDTag: A Predictive Maintenance Framework for Parcel Delivery Services
Venue: IEEE Access
Year: 2020

The overwhelming increase of parcel transports has prompted the need for effective and scalable intelligent logistics systems. In parallel, with the advent of Industry 4.0, a tight integration of Internet of Things technologies and Big Data analytics solution has become necessary to effectively manage industrial processes and to early predict product faults or service disruptions. In the context of good transports, the development of smart monitoring tools is particularly useful for couriers to ensure effective and efficient parcel deliveries. However, the existing predictive maintenance frameworks are not tailored to parcel delivery services. We present REDTag Service, an integrated framework to track and monitor the shipped packages. It relies on a network of IoT enabled devices, called REDTags, allowing courier employees to easily collect the status of the package at each delivery step. The framework provides back end functionalities for smart data transmission, management, storage, and analytics. A machine learning process is included to promptly analyze the features describing event related data to predict potential breaks of the goods in the packages. The framework provides also a dynamic view on the integrated data tailored to the different stakeholders, as well as on the prediction outcomes, enabling immediate feedback and model improvements. We analyze a real world dataset including event related data about parcel transports. To validate the hypothesis that the acquired data contains information relevant to predict the package status (i.e. broken or safe) we empirically analyze the performance of different, scalable classifiers. The experimental results confirm, in good approximation, the predictive power of the models extracted from the event related features. To the best of the authors' knowledge, this work is the first attempt to address predictive maintenance in smart good transport logistics to predict package breaks from real world data.
School responsiveness to adoption among lesbian mothers
Venue: Journal of lesbian studies
Year: 2019

A sample of adoptive parents, including an oversampling of lesbian mothers (n 125) was surveyed regarding their family's school experiences related to adoption, satisfaction with how the school handled adoption related issues, and their child's experiences with teasing at school. Linear and logistic regression analyses showed that lesbian mothers were more likely than heterosexual parents and gay fathers to disclose their child's adoption status in the school record and to ensure that the school was aware of the child's adoption. Lesbian mothers were also more likely than heterosexual parents and gay fathers to report talking to teachers about adoption issues. There were no differences as a function of parent sexual orientation regarding rates of children being teased about adoption status. However, rates of teasing regarding child race/ethnicity were higher for children of lesbian mothers and gay fathers than for children of heterosexual parents. The implications of the findings for future research and the provision of services for lesbian mothers are considered.
Combining Long Short Term Memory and Convolutional Neural Network for Cross Sentence n ary Relation Extraction
Venue: AKBC
Year: 2019

We propose in this paper a combined model of Long Short Term Memory and Convolutional Neural Networks (LSTM CNN) that exploits word embeddings and positional embeddings for cross sentence n ary relation extraction. The proposed model brings together the properties of both LSTMs and CNNs, to simultaneously exploit long range sequential information and capture most informative features, essential for cross sentence n ary relation extraction. The LSTM CNN model is evaluated on standard dataset on cross sentence n ary relation extraction, where it significantly outperforms baselines such as CNNs, LSTMs and also a combined CNN LSTM model. The paper also shows that the LSTM CNN model outperforms the current state of the art methods on cross sentence n ary relation extraction.
Are Family Firms More Tax Aggressive Than Non Family Firms?
Year: 2010

Taxes represent a significant cost to the firm and shareholders, and it is generally expected that shareholders prefer tax aggressiveness. However, this argument ignores potential non tax costs that can accompany tax aggressiveness, especially those arising from agency problems. Firms owned/run by founding family members are characterized by a unique agency conflict between dominant and small shareholders. Using multiple measures to capture tax aggressiveness and founding family presence, we find that family firms are less tax aggressive than their non family counterparts, ceteris paribus. This result suggests that family owners are willing to forgo tax benefits to avoid the non tax cost of a potential price discount, which can arise from minority shareholders' concern with family rent seeking masked by tax avoidance activities [Desai and Dharmapala, 2006. Corporate tax avoidance and high powered incentives.  Our result is also consistent with family owners being more concerned with the potential penalty and reputation damage from an IRS audit than non family firms. We obtain similar inferences when using a small sample of tax shelter cases.
Adaptive Prototypical Networks with Label Words and Joint Representation Learning for Few Shot Relation Classification
Venue: IEEE transactions on neural networks and learning systems
Year: 2021

Relation classification (RC) task is one of fundamental tasks of information extraction, aiming to detect the relation information between entity pairs in unstructured natural language text and generate structured data in the form of entity relation triple. Although distant supervision methods can effectively alleviate the problem of lack of training data in supervised learning, they also introduce noise into the data and still cannot fundamentally solve the long tail distribution problem of the training instances. In order to enable the neural network to learn new knowledge through few instances such as humans, this work focuses on few shot relation classification (FSRC) where a classifier should generalize to new classes that have not been seen in the training set, given only a number of samples for each class. To make full use of the existing information and get a better feature representation for each instance, we propose to encode each class prototype in an adaptive way from two aspects. First, based on the prototypical networks, we propose an adaptive mixture mechanism to add label words to the representation of the class prototype, which, to the best of our knowledge, is the first attempt to integrate the label information into features of the support samples of each class so as to get more interactive class prototypes. Second, to more reasonably measure the distances between samples of each category, we introduce a loss function for joint representation learning (JRL) to encode each support instance in an adaptive manner. Extensive experiments have been conducted on FewRel under different few shot (FS) settings, and the results show that the proposed adaptive prototypical networks with label words and JRL has not only achieved significant improvements in accuracy but also increased the generalization ability of FSRC.
A Survey on Internet of Things: Architecture, Enabling Technologies, Security and Privacy, and Applications
Venue: IEEE Internet of Things Journal
Year: 2017

Fog/edge computing has been proposed to be integrated with Internet of Things (IoT) to enable computing services devices deployed at network edge, aiming to improve the user's experience and resilience of the services in case of failures. With the advantage of distributed architecture and close to end users, fog/edge computing can provide faster response and greater quality of service for IoT applications. Thus, fog/edge computing based IoT becomes future infrastructure on IoT development. To develop fog/edge computing based IoT infrastructure, the architecture, enabling techniques, and issues related to IoT should be investigated first, and then the integration of fog/edge computing and IoT should be explored. To this end, this paper conducts a comprehensive overview of IoT with respect to system architecture, enabling technologies, security and privacy issues, and present the integration of fog/edge computing and IoT, and applications. Particularly, this paper first explores the relationship between cyber physical systems and IoT, both of which play important roles in realizing an intelligent cyber physical world. Then, existing architectures, enabling technologies, and security and privacy issues in IoT are presented to enhance the understanding of the state of the art IoT development. To investigate the fog/edge computing based IoT, this paper also investigate the relationship between IoT and fog/edge computing, and discuss issues in fog/edge computing based IoT. Finally, several applications, including the smart grid, smart transportation, and smart cities, are presented to demonstrate how fog/edge computing based IoT to be implemented in real world applications.
Diffraction series on a sphere and conical asymptotics
Venue: Proceedings of the International Conference Days on Diffraction 2011
Year: 2011

The problem of finding field components for a conical diffraction problem is studied. All components except the spherical wave diffracted by cone tip are under consideration. As a starting point, the integral formula (7) derived by Babich et al. is used. A geometrical optics approximation of the spherical Green's function is constructed in the form of diffraction series. There is a finite set of terms of the diffraction series on sphere, to each of which the conical field components correspond. Formula (7) is simplified, giving a convenient field representation (26) In many cases further simplification can be performed, giving formula (30) directly converting the terms of the diffraction series on sphere into the field component in the 3D space.
Unveiling the bioactive potential of the essential oil of a Portuguese endemism, Santolina impressa.
Venue: Journal of ethnopharmacology
Year: 2019

ETHNOPHARMACOLOGICAL RELEVANCE Santolina species are widely used in traditional medicine in the Mediterranean region for their anti inflammatory, antimicrobial, antispasmodic, digestive, and analgesic properties. S. impressa, a Portuguese endemism, is traditionally recognized for its beneficial anti inflammatory properties in several gastrointestinal affections and is also used in oropharyngeal infections.  The present study aims to characterize the essential oil of S. impressa growing in Portugal and validate its traditional uses by assessing the anti inflammatory potential of its essential oil at concentrations without toxicity. The antifungal properties of the oil are also addressed, as well as, the putative mechanism of action underlying these effects.  The essential oil was obtained in accordance with the European Pharmacopoeia and characterized by GC and GC MS. The anti inflammatory potential of the oil was assessed on LPS stimulated macrophages, by assessing the production of nitric oxide (NO) using the Griess reaction. Putative mechanisms of action included the role of the oil as a NO scavenger, as well as its effect on the expression of two key pro inflammatory enzymes, iNOS and COX 2 by Western blot analysis. The antifungal effect of the oil was evaluated according to the CLSI guidelines on several yeast and filamentous strains and on two major virulence factors in Candida albicans, namely germ tubes and biofilms. Ultrastructural modifications on dermatophytes were also unveiled by transmission electron microscopy.  S. impressa essential oil was primarily characterized by the presence of monoterpene hydrocarbons and oxygenated monoterpenes, being the main compounds b pinene (22.5% 1,8 cineole (10% limonene (9.1% camphor (8.1% and b phellandrene (8% A significant decrease (ca 60% in nitrite levels was observed in LPS stimulated macrophages treated with the oil without affecting cell viability. This effect could be explained by a great reduction on iNOS expression (85% inhibition) thus underpinning the anti inflammatory potential of the oil. The oil also showed a fungicidal effect, being more active against Cryptococcus neoformans, Epidermophyton floccosum and Trichophytum rubrum. For these dermatophytes, significant ultrastructural modifications in cell wall structure were detected. Strikingly, for C. albicans, the oil showed a significant anti infective potential (at 0.07 mg/mL for germ tube inhibition and 0.02 mg/mL for biofilm disruption) before fungal growth inhibition occurred.  Our results validate the main traditional use ascribed to S. impressa, namely its anti inflammatory effect. In addition, an antifungal potential is pointed out, thus corroborating the antimicrobial uses and adding new value to an endemic species poorly recognized by the industry.
Whole exome sequencing identifies a novel missense mutation in EDAR causing autosomal recessive hypohidrotic ectodermal dysplasia with bilateral amastia and palmoplantar hyperkeratosis
Venue: The British journal of dermatology
Year: 2013

Ectodermal dysplasias (EDs) are a large group of heritable complex conditions with more than 200 members and common clinical characteristics of anomalies of the hair, teeth, nails, and sweat glands with or without involvement of other organs (1) Anhidrotic or hypohidrotic ectodermal dysplasia (EDA/ HED) is the most common form of EDs which is characterized by the clinical triad of hypotrichosis (sparse hair) abnormal or missing teeth (anodontia or hypodontia) and deficient sweating (hypohidrosis or anhidrosis) (2) Different modes of inheritance have been described for HED. X linked HED (OMIM: 305100) is caused by mutations in ectodysplasin A gene (EDA1) whereas mutations in the EDA receptor (EDAR) and EDAR associated death domain (EDARADD) genes result in autosomal dominant (OMIM:129490) and autosomal recessive (OMIM: 224900) forms (3)
Human brain activity for machine attention
Venue: ArXiv
Year: 2020

Cognitively inspired NLP leverages human derived data to teach machines about language processing mechanisms. Recently, neural networks have been augmented with behavioral data to solve a range of NLP tasks spanning syntax and semantics. We are the first to exploit neuroscientific data, namely electroencephalography (EEG) to inform a neural attention model about language processing of the human brain with direct cognitive measures. Part of the challenge in working with EEG is that features are exceptionally rich and need extensive pre processing to isolate signals specific to text processing. We devise a method for finding such EEG features to supervise machine attention through combining theoretically motivated cropping with random forest tree splits. This method considerably reduces the number of dimensions of the EEG data. We employ the method on a publicly available EEG corpus and demonstrate that the pre processed EEG features are capable of distinguishing two reading tasks. We apply these features to regularise attention on relation classification and show that EEG is more informative than strong baselines. This improvement, however, is dependent on both the cognitive load of the task and the EEG frequency domain. Hence, informing neural attention models through EEG signals has benefits but requires further investigation to understand which dimensions are most useful across NLP tasks.
Vector optical fields with bipolar symmetry of linear polarization.
Venue: Optics letters
Year: 2013

We focus on a new kind of vector optical field with bipolar symmetry of linear polarization instead of cylindrical and elliptical symmetries, enriching members of family of vector optical fields. We design theoretically and generate experimentally the demanded vector optical fields and then explore some novel tightly focusing properties. The geometric configurations of states of polarization provide additional degrees of freedom assisting in engineering the field distribution at the focus to the specific applications such as lithography, optical trapping, and material processing.
Advancement of Routing Protocols and Applications of Underwater Wireless Sensor Network (UWSN) A Survey
Venue: J. Sens. Actuator Networks
Year: 2020

Water covers a greater part of the Earth's surface. However, little knowledge has been achieved regarding the underwater world as most parts of it remain unexplored. Oceans, including other water bodies, hold substantial natural resources and also the aquatic lives. These are mostly undiscovered and unknown due to the unsuited and hazardous underwater environments for the human. This inspires the unmanned exploration of these dicey environments. Neither unmanned exploration nor the distant real time monitoring is possible without deploying Underwater Wireless Sensor Network (UWSN) Consequently, UWSN has drawn the interests of the researchers recently. This vast underwater world is possible to be monitored remotely from a distant location with much ease and less risk. The UWSN is required to be deployed over the volume of the water body to monitor and surveil. For vast water bodies like oceans, rivers and large lakes, data is collected from the different heights/depths of the water level which is then delivered to the surface sinks. Unlike terrestrial communication and radio waves, conventional mediums do not serve the purpose of underwater communication due to their high attenuation and low underwater transmission range. Instead, an acoustic medium is able to transmit data in underwater more efficiently and reliably in comparison to other mediums. To transmit and relay the data reliably from the bottom of the sea to the sinks at the surface, multi hop communication is utilized with different schemes. For seabed to surface sink communication, leading researchers proposed different routing protocols. The goal of these routing protocols is to make underwater communications more reliable, energy efficient and delay efficient. This paper surveys the advancement of some of the routing protocols which eventually helps in finding the most efficient routing protocol and some recent applications for the UWSN. This work also summarizes the remaining challenging issues and the future trends of those considered routing protocols. This survey encourages further research efforts to improve the routing protocols of UWSN for enhanced underwater monitoring and exploration.
Prediction of manifest Huntington's disease with clinical and imaging measures: a prospective observational study
Venue: The Lancet Neurology
Year: 2014

Although the association between cytosine adenine guanine (CAG) repeat length and age at onset of Huntington's disease is well known, improved prediction of onset would be advantageous for clinical trial design and prognostic counselling. We compared various measures for tracking progression and predicting conversion to manifest Huntington's disease.  In this prospective observational study, we assessed the ability of 40 measures in five domains (motor, cognitive, psychiatric, functional, and imaging) to predict time to motor diagnosis of Huntington's disease, accounting for CAG repeat length, age, and the interaction of CAG repeat length and age. Eligible participants were individuals from the PREDICT HD study (from 33 centres in six countries [USA, Canada, Germany, Australia, Spain, UK] with the gene mutation for Huntington's disease but without a motor diagnosis (a rating below 4 on the diagnostic confidence level from the 15 item motor assessment of the Unified Huntington's Disease Rating Scale) Participants were followed up between September, 2002, and July, 2014. We used joint modelling of longitudinal and survival data to examine the extent to which baseline and change of measures analysed separately was predictive of CAG adjusted age at motor diagnosis.  1078 individuals with a CAG expansion were included in this analysis. Participants were followed up for a mean of 5*1 years (SD 3*3, range 0*0 12*0) 225 (21% of these participants received a motor diagnosis of Huntington's disease during the study. 37 of 40 cross sectional and longitudinal clinical and imaging measures were significant predictors of motor diagnosis beyond CAG repeat length and age. The strongest predictors were in the motor, imaging, and cognitive domains: an increase of one SD in total motor score (motor domain) increased the risk of a motor diagnosis by 3*07 times (95% CI 2*26 4*16) a reduction of one SD in putamen volume (imaging domain) increased risk by 3*32 times (2*37 4*65) and a reduction of one SD in Stroop word score (cognitive domain) increased risk by 2*32 times (1*88 2*87) INTERPRETATION Prediction of diagnosis of Huntington's disease can be improved beyond that obtained by CAG repeat length and age alone. Such knowledge about potential predictors of manifest Huntington's disease should inform discussions about guidelines for diagnosis, prognosis, and counselling, and might be useful in guiding the selection of participants and outcome measures for clinical trials.
Black Economic Empowerment Transactions in South Africa: Understanding When Corporate Social Responsibility May Create or Destroy Value
Year: 2011

Why can some firms create value from a particular corporate social responsibility (CSR) action while other firms destroy value from the same type of CSR action? This study explores this question in the context of Black Economic Empowerment (BEE) in South Africa. BEE has helped to drive the transformation in the economic, political and social landscapes of post apartheid South Africa. It strives to increase black wealth through the sale of an equity stake in a company to black empowerment groups. Thus, BEE appears to represent corporate social responsibility that benefits the previously disadvantaged population. This inductive study investigates whether BEE can represent a value creating CSR action and under what conditions. We explore the economic impact of BEE for the shareholders and find that South African firms that engaged in BEE transactions that were completed at a discount experienced positive and significant average shareholder returns. However, the firms that engaged in BEE transactions that were completed at a premium experienced negative and significant average shareholder returns. Furthermore, for empowerment deals completed at a discount, the size of the equity stake was positively associated with shareholder returns. Our analysis suggests that the CSR aspects of BEE deals created value when pursued in earnest, but destroyed value otherwise. We offer several implications from our findings for managers. We also link the insights from our findings to implications for stewardship theory, signalling theory and broader CSR perspectives.
Exploring Cross cultural Crowd Sentiments on Twitter
Venue: HT
Year: 2017

Twitter is frequently used to express personal opinions and sentiments. This work presents a novel crowd sentiment analysis of Twitter for exploring cross cultural differences. We aim to find similar meanings but different sentiments between Twitter data collected over diverse geographic places. For this, we detect sentiments and topics of each tweet and assign sentiments to each topic based on the sentiments of the corresponding tweets. This permits finding interesting cross cultural patterns. We demonstrate a visualization system that supports the interactive analysis of two countries: France and Italy.