nils-herrmann/clean_scirepeval huggingface dataset:
Neighborhood search based heuristic for the k clustering minimum biclique completion problem
Venue: 2016 International Conference on Control, Decision and Information Technologies (CoDIT)

In this paper, we propose the neighborhood search based heuristic to solve the k clustering minimum biclique completion problem. The KCmBCP consists in partitioning a bipartite undirected graph into k clusters such that the total number of edges that added for complete each cluster into a biclique is minimum. Given a bipartite graph G (S, T, E) we consider the problem of finding k bipartite sub graphs, called clusters, such that each vertex i of S appears in exactly one of them, every vertex j of T appears in each cluster in which at least one of its neighbors appears, and the total number of edges needed to make each cluster complete is minimized. This problem is known as k clustering minimum biclique completion problem (noted KCmBCP) and has been shown strongly NP hard. The proposed approach begins by construction of an initial solution. After, the proposed approach enters on iterative phase, which destroys part of the initial solution and build of a feasible solution by exploring the neighborhood of the current solution. The experimental results show the effectiveness of the proposed approach.
Cross Lingual Sentiment Classification with Bilingual Document Representation Learning
Venue: ACL

Cross lingual sentiment classification aims to adapt the sentiment resource in a resource rich language to a resource poor language. In this study, we propose a representation learning approach which simultaneously learns vector representations for the texts in both the source and the target languages. Different from previous research which only gets bilingual word embedding, our Bilingual Document Representation Learning model BiDRL directly learns document representations. Both semantic and sentiment correlations are utilized to map the bilingual texts into the same embedding space. The experiments are based on the multilingual multi domain Amazon review dataset. We use English as the source language and use Japanese, German and French as the target languages. The experimental results show that BiDRL outperforms the state of the art methods for all the target languages.
Attention based LSTM Network for Cross Lingual Sentiment Classification
Venue: EMNLP

Most of the state of the art sentiment classification methods are based on supervised learning algorithms which require large amounts of manually labeled data. However, the labeled resources are usually imbalanced in different languages. Cross lingual sentiment classification tackles the problem by adapting the sentiment resources in a resource rich language to resource poor languages. In this study, we propose an attention based bilingual representation learning model which learns the distributed semantics of the documents in both the source and the target languages. In each language, we use Long Short Term Memory (LSTM) network to model the documents, which has been proved to be very effective for word sequences. Meanwhile, we propose a hierarchical attention mechanism for the bilingual LSTM network. The sentence level attention model learns which sentences of a document are more important for determining the overall sentiment while the word level attention model learns which words in each sentence are decisive. The proposed model achieves good results on a benchmark dataset using English as the source language and Chinese as the target language.
Exploring Distributional Representations and Machine Translation for Aspect based Cross lingual Sentiment Classification.
Venue: COLING

Cross lingual sentiment classification (CLSC) seeks to use resources from a source language in order to detect sentiment and classify text in a target language. Almost all research into CLSC has been carried out at sentence and document level, although this level of granularity is often less useful. This paper explores methods for performing aspect based cross lingual sentiment classification (aspect based CLSC) for under resourced languages. Given the limited nature of parallel data for many languages, we would like to make the most of this resource for our task. We compare zero shot learning, bilingual word embeddings, stacked denoising autoencoder representations and machine translation techniques for aspect based CLSC. Each of these approaches requires differing amounts of parallel data. We show that models based on distributed semantics can achieve comparable results to machine translation on aspect based CLSC and give an analysis of the errors found for each method.
Pengaruh Motivasi Terhadap Kinerja Karyawan (studi Pada Pt.primarindo Asia Infrastructure,tbk)

Tujuan penelitian ini dilakukan untuk mengetahui pengaruh motivasi terhadap kinerja karyawan PT.Primarindo Asia Infrastructure,Tbk. Metode pada penelitian ini bersifat kuantitatif dengan melibatkan 104 karyawan PT.Primarindo Asia Infrastructure,Tbk sebagai sampel. Penelitian ini menggunakan analisis regresi sederhana dengan menguji uji T dan uji koefisien determinasi Pengolahan data dilakukan dengan menggunakan software SPSS 13. Temuan pada penelitian ini menunjukan bahwa variabel motivation (x) berpengaruh pada kinerja karyawan PT.Primarindo Asia Infrastructure,Tbk. Koefisien determinasi dari hasil perhitungan didapat sebesar 42,3% Hal ini menunjukkan bahwa Motivasi memberikan pengaruh sebesar 42,3% terhadap Kinerja, sedangkan sisanya sebesar 57,7% Kinerja dapat dijelaskan oleh variabel lainnya yang tidak diteliti. Kesimpulan pada penelitian ini adalah motivasi yang diberikan oleh PT.Primarindo Asia Infrastructure Tbk ke karyawan perusahaan berpengaruh pada kinerja karyawan. Saran dari peneliti Pada segi kinerja yang ada pada PT.Primarindo Asia Infrastructure perlu adanya perbaikan dari segi dampak interpersonal karyawannya,dikarenakan memiliki persentase responden paling rendah.Hal ini diperlukan untuk dilakukan perbaikan karena itikad,dan kerja sama antar sesama karyawan sangat di butuhkan dalam penyelesaian pekerjaan di Kantor.  Motivasi, kinerja karyawan
Essays On The Sociology Of Knowledge

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An ensemble method for job recommender systems
Venue: RecSys Challenge '16

In this paper, we present an ensemble method for job recommendation to ACM RecSys Challenge 2016. Given a user, the goal of a job recommendation system is to predict those job postings that are likely to be relevant to the user1. Firstly, we analyze the train dataset and find several interesting patterns. Secondly, we describe our solution, which is an ensemble of two filters, combining the merits of traditional collaborative filtering and content based filtering. Our approach finally achieved a score of 1632828.82, ranked at the 10th place on the public leaderboard.
LOGISTICS OF WASTE MANAGEMENT IN HEALTHCARE INSTITUTIONS

Logistics of waste management generated in healthcare institutions is a chain of operations related to the movement of waste from its origin to recovery or disposal. The purpose of waste management logistics is the prevention of environment pollution by eliminating improper waste management and protection of natural resources as well as quantitative protection by replacing them with recyclable materials. Along with ecological purposes, waste management logistics is committed to the task of reducing the costs associated with collection, storage and transportation of waste. The article presents logistics solutions in hospital waste management in the aspect of legal requirements [Marszelewski 2014, Project 2015] and proposals for measures to improve the effectiveness of these solutions.
Hybrid Project Management Approach for Software Modernization

Software modernization or legacy modernization, refers to the conversion, rewriting or porting of a legacy system to a modern computer programming language, software libraries, protocols, or hardware platform. Legacy transformation aims to retain and extend the value of the legacy investment through migration to new platforms  Currently, I am working as a Senior Business Analyst with Blue Cross Blue Shield, one of the largest insurance provider in the United States across the health sector. I am deployed to work on their initiative towards software modernization along the healthcare industry through their software as a service (SaaS) platform "The Healthcare Online Resources (THOR)" This service is available to numerous providers in the healthcare vertical to leverage their current operations and comprises of multiple modules including but not limited to claim adjustments, preauthorization and group coverage status. With the ongoing centralization of resources and increased competition, most of the companies are focusing on software modernization by leveraging their current technologies and hardware platforms, to remain the trendsetters and viable in the marketplace. To enhance my efficacy and understanding of the business requirements, I would like to conduct my thesis research towards the topic of "Software Modernization" I would be able to leverage my experience as Senior Business Analyst to understand and bridge the gap between business requirements and the supporting technology backup. Being working in a multinational company, with multiple crossfunctional teams, I plan to include different research results through self surfing and interaction with these team members. Additionally, I had like to conduct surveys across these cross functional teams mainly business and technical teams to understand and document their challenges and experience. Therefore, I strongly feel that I would be able to justify the thesis and the corresponding research.
Use of a discrete event simulation in a Kaizen event: A case study in healthcare
Venue: Eur. J. Oper. Res.

To improve service delivery, healthcare facilities look toward operations research techniques, discrete event simulation and continuous improvement approaches such as Lean manufacturing. Lean management often includes a Kaizen event to facilitate the acceptance of the project by the employees. Business game is also used as a tool to increase understanding of Lean management concepts. In this paper, we study how a business game can be used jointly with discrete event simulation to test scenarios defined by team members during a Kaizen event. The aim is to allow a rapid and successful implementation of the solutions developed during the Kaizen. Our approach has been used to improve patients' trajectory in an outpatient hematology oncology clinic. Patient delays before receiving their treatment were reduced by 74 percent after 19 weeks.
Customer gender prediction based on E commerce data
Venue: 2016 Eighth International Conference on Knowledge and Systems Engineering (KSE)

Demographic attributes of customers such as gender, age, etc. provide important information for e commerce service providers in marketing and personalization of web applications. However, online customers often do not provide this kind of information due to privacy issues and other security related reasons. In this paper, we proposed a method for predicting the gender of customers based on their catalog viewing data on e commerce systems, such as the date and time of access, list of categories and products viewed, etc. We employ a machine learning approach and investigate a number of features derived from catalog viewing information to predict the gender of viewers. Experiments were conducted on datasets provided by the PAKDD'15 Data Mining Competition and achieved the good result. The results 81.2% on balanced accuracy and 81.4% on macro F1 score showed that basic features such as viewing time, products/categories features used together with more advanced features such as products/categories sequence and transfer features effectively facilitate gender prediction of customers.
Analyzing Recurrent Customer Purchases and Unobserved Defections: a Bayesian Data Augmentation Scheme

Understanding customer purchase behavior is important for firms' customer relationship management (CRM) efforts. In certain contexts of firm customer relationship (e.g. retailing and catalog marketing) a firm does not observe customer defections or termination of relationship. Thus, specifying and estimating models of customer lifetime purchases is more difficult in such contexts, specifically in analyzing two key issues, viz. how often will a customer purchase from the firm (purchase frequency) and how long will the customer continue purchasing from the firm (customer lifetime) In this paper, we use a Bayesian data augmentation scheme that overcomes the estimation constraints and allows the use of all available information on customers. Using data from a direct marketing company and also an online classifieds company, we demonstrate the flexibility of this scheme by estimating existing models of lifetime purchase behavior, along with a new proposed model. We show how different types of customer heterogeneity (i.e. observed, unobserved, and time varying) can be incorporated in these models, which is made possible due to the data augmentation.
Genetic modification for disease resistance: a position paper
Venue: Food Security

This Position Paper was prepared by members of the Task Force on Global Food Security of the International Society for Plant Pathology. An objective approach is proposed to the assessment of the potential of genetic modification (GM) to reduce the impact of crop diseases. The addition of GM to the plant breeder's conventional toolbox facilitates gene by gene introduction into breeding programmes of well defined characters, while also allowing access to genes from a greatly extended range of organisms. The current status of GM crops is outlined. GM could make an additional contribution to food security but its potential has been controversial, sometimes because of fixed views that GM is unnatural and risky. These have no factual basis: GM technology, where adopted, is widely regulated and no evidence has been reported of adverse consequences for human health. The potential benefits of GM could be particularly valuable for the developing world but there are numerous constraints. These include cost, inadequate seed supply systems, reluctance to adopt unfamiliar technology, concern about markets, inadequacy of local regulatory systems, mismatch between research and growers' needs, and limited technical resources. The lower cost of new gene editing methods should open the practice of GM beyond multinational corporations. As yet there are few examples of utilization of GM based resistance to plant diseases. Two cases, papaya ringspot virus and banana xanthomonas wilt, are outlined. In the developing world there are many more potential cases whose progress is prevented by the absence of adequate biosafety regulation. It is concluded that there is untapped potential for using GM to introduce disease resistance. An objective approach to mobilizing this potential is recommended, to address the severe impact of plant disease on food security.
Critical values education in the early years: Alignment of teachers' personal epistemologies and practices for active citizenship

To date, little research has explored teaching values education for active citizenship in which young children are supported to express their own ideas and opinions and take moral responsibility for their actions. Using personal epistemology as the conceptual framework, this study investigated the nature of, and alignment between, teachers' personal epistemologies and practices for promoting critical values education in elementary education. The study drew on interview and observational data from 29 teachers in Australia. Findings showed that four patterns could be discerned, showing complex relationships between teaching practices and beliefs. Implications for preservice and inservice professional learning experiences are discussed.
Moral and citizenship educational goals in values education: A cross cultural study of Swedish and Turkish student teachers' preferences

The aim of the present study was to examine Swedish and Turkish student teachers' moral educational and citizenship educational goal preferences in values education. The participants were 198 Swedish and 190 Turkish student teachers. While Turkish student teachers seemed to be more morally committed and eclectic than Swedish student teachers, hypothesized gender differences could only be found in the Swedish sample. Whereas there was no difference in their commitment towards critical progressive goals, Turkish student teachers expressed a stronger commitment to traditional conservative goals than Swedish student teachers.
Living Values education in teaching Materials to Develop students' Civic Disposition

The study describes a model of living values education in teaching materials and its impact on the civic disposition of students. It employs the design of research and development in Civic Education Department, Indonesia University of Education. The model of living values education in teaching materials is an integration of the living values in the teaching materials presented in the framework of the material. It includes introduction, keyword, map of concepts, subject matter, discussion, living values, conclusions, reflection on the value application, and references. The application of the model of living values education in teaching materials significantly affects students' civic disposition.
Personal storytelling: Using Natural Language Generation for children with complex communication needs, in the wild
Venue: Int. J. Hum. Comput. Stud.

This paper describes a Natural Language Generation system (NLG) How was School Today? that automatically creates a personal narrative from sensor data and other media (photos and audio) It can be used by children with complex communication needs in schools to support interactive narrative about personal experiences. The robustness of story generation to missing data was identified as a key area for improvement in a feasibility study of the system at a first special needs school. This paper therefore suggests three possible methods for generating stories from unstructured data: clustering by voice recording, by location, or by time. Clustering based on voice recordings resulted in stories that were perceived as most easy to read, and to make most sense, by parents in a quantitative evaluation. This method was implemented in the live system, which was developed and evaluated iteratively at a second special needs school with children with different usage profiles. Open challenges and possibilities for NLG in augmented and alternative communication are also discussed.
Studying in the region of proximal learning reduces mind wandering
Venue: Memory cognition

Insofar as mind wandering has been linked to poor learning, finding ways to reduce the propensity to mind wander should have implications for improving learning. We investigated the possibility that studying materials at an appropriate level of difficulty with respect to the individual's capabilities that is, studying in the region of proximal learning (RPL) might reduce mind wandering. In Experiments 1 and 2, participants were probed for their attentional state while they studied blocks of English Spanish word pairs that were (a) easy, (b) in the RPL, or (c) difficult. We found that studying materials in the RPL was associated with reduced mind wandering. Test performance on items studied while mind wandering was also poorer. In Experiment 3, we investigated the relation between differences in participants' mastery and mind wandering. We found that high performers mind wandered more when studying the easier word pairs, whereas low performers mind wandered more when studying the difficult items. These results indicate that the RPL is specific to the individual's level of mastery and that mind wandering occurs when people are outside that region.
People mind wander more during massed than spaced inductive learning.
Venue: Journal of experimental psychology. Learning, memory, and cognition

This article investigates the relation between mind wandering and the spacing effect in inductive learning. Participants studied works of art by different artists grouped in blocks, where works by a particular artist were either presented all together successively (the massed condition) or interleaved with the works of other artists (the spaced condition) The works of 24 artists were shown, with 12, 15, or 18 works by each artist being provided as exemplars. Later, different works by the same artists were presented for a test of the artists' identity. During the course of studying these works, participants were probed for mind wandering. It was found that people mind wandered more when the exemplars were presented in a massed rather than in a spaced manner, especially as the task progressed. There was little mind wandering and little difference between massed and spaced conditions toward the beginning of study. People were better able to correctly attribute the new works to the appropriate artist (inductive learning) when (a) they were in the spaced condition and (b) they had not been mind wandering. This research suggests that inductive learning may be influenced by mind wandering and that the impairment in learning with massed practice (compared to spaced practice) may be attributable, at least in part, to attentional factors people are "on task" less fully when the stimuli are massed rather than spaced.
If Your Mind Can Grasp It, Your Hands Will Help
Venue: Tangible and Embedded Interaction

This paper describes a study comparing the information recall of participants using 2D and 3D physical visualizations. Specifically, it focuses on physical bar charts and evaluates the difference between a paper based visualization and a version built with wooden blocks. We conducted a repeated measures study involving 16 participants in which we measured the recall of information immediately after the exploration and with a delay of one week. We used questionnaires and semi structured interviews to obtain more information about the process of recall and participants' opinions whether and how the visualizations differ in their potential for memorizing information. The results point out that participants believe to remember the 3D visualizations better, but besides the recall of extreme values the quantitative data cannot completely verify this appreciation. Furthermore the results highlight that the in the study used physical interaction techniques are not able to compensate lacking visual differentiation. One surprising finding was the strong dependency of the different data sets on the recall performance.