nils-herrmann/clean_scirepeval huggingface dataset:
Back at the kitchen table: Reflections on decolonising and internationalising with the Global South socio legal writing workshops
Venue: Feminist Legal Studies
Year: 2019

It has been three years since we held the Feminism, Legality and Knowledge (FLaK) seminar to respond to our developing frustrations and excitement around feminist legal studies and academic publishing. In the wake of our 25th anniversary in 2018, we critically reflect further on our original intention to stock up on decolonising techniques to mix feminism, legality and knowledge whilst building on previous consideration of our self proclaimed 'international' status. These reflections are prompted by editorial board members' experiences as participants in the Cardiff Law and Global Justice Centre's British Academy funded Global South Socio Legal Writing Workshops in Accra (Ghana) Nairobi (Kenya) and Recife (Brazil) in 2018. Following an explanation of the concerns with academic publishing that have prompted this reflection, we provide a narrative of our experiences in each of the three workshops exploring the lessons learned and their impact on our practice as editors and scholars in feminist legal studies. We finish with a renewal of our commitment to decolonise our minds and practices by continuing to struggle to earn the label 'international' for the journal now and in future. A brief introduction to the contents of this issue of FLS follows the reflection.
Models for twistable elastic polymers in Brownian dynamics, and their implementation for LAMMPS.
Venue: The Journal of chemical physics
Year: 2014

An elastic rod model for semi flexible polymers is presented. Theory for a continuum rod is reviewed, and it is shown that a popular discretised model used in numerical simulations gives the correct continuum limit. Correlation functions relating to both bending and twisting of the rod are derived for both continuous and discrete cases, and results are compared with numerical simulations. Finally, two possible implementations of the discretised model in the multi purpose molecular dynamics software package LAMMPS are described.
Living Landscape: Reading Hadrian's Wall
Year: 2011

Wall has for long been considered indeed re created as something purely 'Roman' Its 'original' remains have been treated as separate from those of the 1,500 years' of use that have followed since it was 'abandoned' as if its life span ended with its final phase of use at a distant point in the early fifth century (if not with its final phase of 'proper' construction) Yet Hadrian's Wall still exists in the contemporary landscape. This paper provides a focus on the long life of the frontier landscape of the Wall and the changing materiality and function of this substantial monument. The fixation with separating a past 'authentic' Hadrian's Wall from its present living form is based on a body of archaeological theory and method that aims to delimit the ancient heritage so that it can be interpreted, protected, communicated and managed. It is argued that in landscape terms this approach paradoxically separates the past from the present, and thus breaks the chain of continuity that for many is essential to the construction of the landscape. As a living, transforming and contextualised part of landscape, the character of this ancient monument is transformational, and its living history as a debatable land should always be open for reinterpretation.
Prognostic role of FOXP3+ regulatory T cells infiltrating human carcinomas: the paradox of colorectal cancer
Venue: Cancer Immunology, Immunotherapy
Year: 2011

The accumulation of regulatory T cells (Tregs) at high density in various human carcinomas is generally associated with a poor prognosis, as expected from their capacity to inhibit antitumor immunity. Surprisingly, in patients bearing colorectal carcinoma (CRC) high regulatory T cell infiltration is associated with a favorable prognosis, as shown by the analysis of seven clinical studies. To explain this paradox, we emphasize a putative role of the dense microbiological flora present in the large intestine with a trend toward translocation through the tumor. This microbiological hazard requires a T cell mediated inflammatory anti microbial response that involves Th17 cells and can thereby promote cancer growth. This Th17 cell dependent proinflammatory and tumor enhancing response can be attenuated by Tregs, thus constituting a possible explanation for their favorable role in CRC prognosis. The link between a high density of FOXP3 positive cells in CRC immune infiltrates and favorable prognosis should lead us to consider tumor infiltrating Tregs as allies to be respected, rather than enemies to be destroyed during trials of CRC treatment.
Towards Question based High recall Information Retrieval
Venue: ACM Trans. Inf. Syst.
Year: 2020

While continuous active learning algorithms have proven effective in finding most of the relevant documents in a collection, the cost for locating the last few remains high for applications such as Technology assisted Reviews (TAR) To locate these last few but significant documents efficiently, Zou et al. [2018] have proposed a novel interactive algorithm. The algorithm is based on constructing questions about the presence or absence of entities in the missing relevant documents. The hypothesis made is that entities play a central role in documents carrying key information and that the users are able to answer questions about the presence or absence of an entity in the missing relevance documents. Based on this, a Sequential Bayesian Search based approach that selects the optimal sequence of questions to ask was devised. In this work, we extend Zou et al. [2018] by (a) investigating the noise tolerance of the proposed algorithm; (b) proposing an alternative objective function to optimize, which accounts for user "erroneous" answers; (c) proposing a method that sequentially decides the best point to stop asking questions to the user; and (d) conducting a small user study to validate some of the assumptions made by Zou et al. [2018] Furthermore, all experiments are extended to demonstrate the effectiveness of the proposed algorithms not only in the phase of abstract appraisal (i.e. finding the abstracts of potentially relevant documents in a collection) but also finding the documents to be included in the review (i.e. finding the subset of those relevant abstracts for which the article remains relevant) The experimental results demonstrate that the proposed algorithms can greatly improve performance, requiring reviewing fewer irrelevant documents to find the last relevant ones compared to state of the art methods, even in the case of noisy answers. Further, they show that our algorithm learns to stop asking questions at the right time. Last, we conduct a small user study involving an expert reviewer. The user study validates some of the assumptions made in this work regarding the user's willingness to answer the system questions and the extent of it, as well as the ability of the user to answer these questions.
Identifying personality traits associated with entrepreneurial success: does gender matter?
Year: 2018

This study provides a conceptual and empirical clarification of the extent to which the Big Five personality traits of openness to experience, extraversion, neuroticism, conscientiousness, and agreeableness affect entrepreneurial success in an emerging country context. We also examine whether gender moderates the association between these five personality traits and entrepreneurial success. 174 Tunisian entrepreneurs were surveyed by using a questionnaire and the partial least squares method and multi group analysis were piloted to test the six research hypotheses. The results clearly demonstrate that entrepreneurial personality traits that relate significantly to business success are characterized by high emotional stability, openness to experience, and conscientiousness. Further, the results illustrate that gender moderates the association between personality and entrepreneurial success. Government agencies that promote and encourage entrepreneurs and business creation may find the presented findings useful in helping to recognize future successful entrepreneurs on the basis of their personality traits.
A Semantic Path Based Similarity Measure for Weighted Heterogeneous Information Networks
Venue: KSEM
Year: 2018

In recent years, recommender systems based on heterogeneous information networks (HIN) have gained wide attention. In order to generate more attractive recommendations, weighted heterogeneous information network (WHIN) has been proposed, which attaches attribute values to links. The widely used similarity measures for HIN may fail to capture the semantics of weighted meta path. This makes designing a similarity measure specially for WHIN more necessary. In this paper, we propose a semantic path based similarity measure called WgtSim, which is a generalization of PathSim presented by Sun et al. Furthermore, to demonstrate the capability of WgtSim in capturing semantics, we apply WgtSim to recommender system on WHIN to predict ratings given by users. The experiments on two real datasets show that the recommender system with WgtSim outperforms that with previous measures.
Implicit Regularization of Accelerated Methods in Hilbert Spaces
Venue: NeurIPS
Year: 2019

We study learning properties of accelerated gradient descent methods for linear least squares in Hilbert spaces. We analyze the implicit regularization properties of Nesterov acceleration and a variant of heavy ball in terms of corresponding learning error bounds. Our results show that acceleration can provides faster bias decay than gradient descent, but also suffers of a more unstable behavior. As a result acceleration cannot be in general expected to improve learning accuracy with respect to gradient descent, but rather to achieve the same accuracy with reduced computations. Our theoretical results are validated by numerical simulations. Our analysis is based on studying suitable polynomials induced by the accelerated dynamics and combining spectral techniques with concentration inequalities.
Fairness Aware Machine Learning: Practical Challenges and Lessons Learned
Venue: WSDM
Year: 2019

Researchers and practitioners from different disciplines have highlighted the ethical and legal challenges posed by the use of machine learned models and data driven systems, and the potential for such systems to discriminate against certain population groups, due to biases in algorithmic decision making systems. This tutorial aims to present an overview of algorithmic bias discrimination issues observed over the last few years and the lessons learned, key regulations and laws, and evolution of techniques for achieving fairness in machine learning systems. We will motivate the need for adopting a "fairness first" approach (as opposed to viewing algorithmic bias fairness considerations as an afterthought) when developing machine learning based models and systems for different consumer and enterprise applications. Then, we will focus on the application of fairness aware machine learning techniques in practice, by presenting case studies from different technology companies. Based on our experiences in industry, we will identify open problems and research challenges for the data mining machine learning community.
Predicting tie strength with social media
Venue: CHI
Year: 2009

Social media treats all users the same: trusted friend or total stranger, with little or nothing in between. In reality, relationships fall everywhere along this spectrum, a topic social science has investigated for decades under the theme of tie strength. Our work bridges this gap between theory and practice. In this paper, we present a predictive model that maps social media data to tie strength. The model builds on a dataset of over 2,000 social media ties and performs quite well, distinguishing between strong and weak ties with over 85% accuracy. We complement these quantitative findings with interviews that unpack the relationships we could not predict. The paper concludes by illustrating how modeling tie strength can improve social media design elements, including privacy controls, message routing, friend introductions and information prioritization.
Influencia del proceso de inclusion a la inversa en el contexto educativo
Venue: Pensamiento Americano
Year: 2019

El articulo es realizado con el objetivo de ofrecer un acercamiento al proceso de inclusion a la inversa, identificando su influencia en el contexto educativo. Ademas, se apoya en 6 antecedentes divididos en contextos internacionales, nacionales y locales. De igual manera, se determinan una serie de conceptos que tienen que ver con el tema de la inclusio?n, inclusio?n a la inversa y las formas como se presentan en el contexto escolar. Asimismo, este estudio se acoge a un paradigma cualitativo de corte descriptivo y se orienta bajo el tipo de investigacion etnografia; a su vez, se muestran una serie de resultados, producto de la aplicacion de tecnicas de recoleccion de informacion (Observacion participante, entrevistas semi estructuradas y grupo focal) Con base a lo anterior, la poblacion seleccionada para esta investigacio?n corresponde a estudiantes del grado preescolar a quinto de primaria y la muestra seleccionada consta de 20 participantes. Consecuentemente, la investigacion de la cual se deriva la escritura de este articulo, tuvo como nombre 'Inclusion a la inversa y cultura de paz en el contexto educativo' en la que participaron la Institucion educativa Francisco Luis Hernandez Betancur y la Universidad Catolica Luis amigo? ambas ubicadas en la ciudad de Medellin.
PERANCANGAN TATA LETAK GUDANG PENYIMPANAN MATERIAL DI GUDANG "K" MENGGUNAKAN CLASS BASED STORAGE DENGAN PENDEKATAN Fast Slow Non moving (FSN)
Year: 2018

PT. Industri Telekomonikasi Indonesia atau bisa disebut PT.INTI (persero) merupakan perusahaan yang bergerak dibidang logistic pergudangan. PT.INTI memiliki gudang yaitu gudang "K" Gudang tersebut harus mendapatkan penanganan khusus karena banyaknya jenis dan jumlah material. Tata letak gudang di PT.INTI masih terdapat penumpukan material yang tidak sesuai dengan slot yang telah disediakan. Tata letak gudang hanya memanfaatkan tinggi ruangan untuk proses pinyimpanan material. Penempatan barang digudang dilakukan dengan pendekatan randomized storage yang artinya penempatan barang yang masuk dalam gudang diletakan secara acak dan terjadi penumpukan. Proses tersebut terjadi karena material yang masuk bisa saja diletakan di slot yang sama. Selain terjadinya penumpukan, cara randomized storage juga tidak memperhitungkan ongkos material handling. Namun, terdapat cara lain untuk menata material dalam gudang yakni dengan metode class based storage. Metode tersebut digunakan untuk perancangan tata letak material dan dapat mengurangi tumpukan. Perancangannya dibantu dengan pendekatan Fast Slow Non moving (FSN) Pendekatan tersebut digunakan untuk menempatkan material berdasarkan kelas dan menge. Material yang masuk kedalam kelas F akan diletakan didekat pintu masuk, untuk kelas S diletakan setelah kelas F dan diikuti dengan kelas N. Kemudian dilakukan slotting untuk setiap material dan didapatkan rata rata material yang ada dalam gudang sebanyak 4127 unit dengan maksimal kapasitas rak 186 unit. Selanjutnya memperhitungkan ongkos material handling dengan Algoritma CRAFT. Dari perhitungan CRAFT didapat hasil ongkos material handling awal Rp 197.850. Kemudian dibuat 2 alternatif dan didapat ongkos yang minimum yakni alternatif 1 dengan ongkos Rp 153.900. hasil tersebut menyatakan bahwa alternatif 1 dapat menghemat Rp 25.950 dibanding yang awal.  Warehouse, Class based storage, FSN (Fast Slow Non moving) Ongkos Material Handling, CRAFT
Palliative Rehabilitation: The Essence of Personalized Care
Venue: Indian journal of palliative care
Year: 2020

Patients with advanced life limiting illnesses experience the symptom burden of varying severity through the continuum of their illness trajectory. These symptoms negatively impact and impair the normal functioning of the individual.[1] This ongoing impairment in normal functioning leads to debility, if left unaddressed disables the patient due to increased dependency, psychological distress, neurological and musculoskeletal deconditioning. Progressive disease leads to a cluster of new symptoms such as pain, fatigue, dyspnea culminating in further decline in performance status, decreased activity of daily living (ADL) and significant reduction in the quality of life (QOL) of patients and caregivers.[2] The importance of comprehensive medical care has been established well, with the two critical components of personalized care being palliation and rehabilitation. At the outset, it may seem the most unlikely combination palliative care being commonly associated with care of patients with life limiting illness and dying, while rehabilitation being commonly associated with recovery and full functionality. The relevance of integrating both the specialties as part of the interdisciplinary team is evident from the ethos governing both the services at improving the comfort and functionality,[3] in keeping with the personal goals of patients and family.
Simulation of crystallization evolution of polyoxymethylene during microinjection molding cycle
Year: 2019

A mathematical model coupled with a numerical investigation of the evolving material properties due to thermal and flow effects and in particular the evolution of the crystallinity during the full microinjection molding cycle of poly (oxymethylene) POM is presented using a multi scale approach. A parametric analysis is performed, including all the steps of the process using an asymmetrical stepped contracting part. The velocity and temperature fields are discussed. A parabolic distribution of the velocity across the part thickness, and a temperature rise in the thin zone toward the wall have been obtained. It is attributed to the viscous energy dissipation during the filling phase, but also to the involved characteristic times for the thermal behavior of the material. Depending on the molding conditions and the locations within the micro part, different evolution of crystallization rates are obtained leading to at least three to five morphological layers, obtained in the same part configuration of a previously work, allowing a clear understanding of the process material interaction.
Road Damage Detection using Deep Ensemble Learning
Venue: 2020 IEEE International Conference on Big Data (Big Data)
Year: 2020

Road damage detection is critical for the maintenance of a road, which traditionally has been performed using expensive high performance sensors. With the recent advances in technology, especially in computer vision, it is now possible to detect and categorize different types of road damages, which can facilitate efficient maintenance and resource management. In this work, an ensemble model for efficient detection and classification of road damages, which has been submitted to the IEEE BigData Cup Challenge 2020. The solution utilizes a state of the art object detector known as You Only Look Once (YOLO v4) which is trained on images of various types of road damages from Czech, Japan and India. The ensemble approach was extensively tested with several different model versions and it was able to achieve an F1 score of 0.628 on the test 1 dataset and 0.6358 on the test 2 dataset.
Question Answering as an Automatic Evaluation Metric for News Article Summarization
Venue: NAACL
Year: 2019

Recent work in the field of automatic summarization and headline generation focuses on maximizing ROUGE scores for various news datasets. We present an alternative, extrinsic, evaluation metric for this task, Answering Performance for Evaluation of Summaries. APES utilizes recent progress in the field of reading comprehension to quantify the ability of a summary to answer a set of manually created questions regarding central entities in the source article. We first analyze the strength of this metric by comparing it to known manual evaluation metrics. We then present an end to end neural abstractive model that maximizes APES, while increasing ROUGE scores to competitive results.
The Impact Of Storytelling In The Classroom
Year: 2020

Storytelling, the art of narrating a tale from memory rather than reading it is one of the oldest of all art forms, reaching back to prehistoric times. Many EFL teachers are interested in storytelling as a resource in teaching. A successful storyteller chooses adequate stories and must be a good performer, for the delivery is crucial and requires both preparation and rehearsal. Storytelling is the original form of teaching and has the potential of fostering emotional intelligence and help the child gain insight into human behavior. Moreover, storytelling can provide a motivating and low anxiety context for language learning. In the current article, I am also going to shed light on some storytelling tips which are meant to help the teacher as storyteller as s/he prepares for a storytelling "performance" for students.
Research on Prediction Algorithm of Vehicle Speed Based on Driving Intention Classification
Venue: 2021 6th International Conference on Automation, Control and Robotics Engineering (CACRE)
Year: 2021

Speed prediction of surrounding vehicles has received much attention because of its great potential to improve the vehicle's safety, fuel consumption, and efficiency. However, its driving prediction faces significant challenges due to the uncertainty of its driving intention, vehicle dynamics, and the interaction between the predicted object and its surrounding environment. First, the Fuzzy C mean (FCM) method is used to achieve automatic recognition of driving intentions by offline training using information related to vehicle acceleration; Second, the Gaussian process regression (GPR) method is used to predict future speeds under three different driving intentions using historical and cur rent vehicle speeds; Third, the three predicted speeds are fused according to the fuzzy classification results of driving intentions by iterative method for 1s rolling prediction; Finally, experimental validation is performed on a 6.6m long electric mobile robot. The results show that 90.33% of the prediction errors are less than 1m/s in the future 1s speed prediction, and the maximum speed prediction error is 2.45m/s.
Cancer therapy with Newcastle disease virus: rationale for new immunotherapeutic combinations
Year: 2015

Cancer immunotherapy with Newcastle disease virus (NDV) dates back to 1960s, when intratumoral treatment of a cervical cancer patient was noted to result in an abscopal effect with distant tumor regression. Since then, multiple preclinical and clinical studies have demonstrated the inherent ability of NDV to increase immunogenicity of tumor cells. Recently, these properties were explored in animal models within the context of novel immunotherapies such as antibodies targeting immune checkpoints, with demonstration of induction of systemic antitumor immunity and regression of tumors not directly affected by the virus. These studies highlight the immunotherapeutic potential of NDV and provide a strong rationale for exploration of NDV and perhaps other oncolytic viruses in combination with immunomodulatory antibodies in the clinic.