nils-herrmann/clean_scirepeval huggingface dataset:
Analyzing undergraduate students' performance using educational data mining

The tremendous growth in electronic data of universities creates the need to have some meaningful information extracted from these large volumes of data. The advancement in the data mining field makes it possible to mine educational data in order to improve the quality of the educational processes. This study, thus, uses data mining methods to study the performance of undergraduate students. Two aspects of students' performance have been focused upon. First, predicting students' academic achievement at the end of a fouryear study programme. Second, studying typical progressions and combining them with prediction results. Two important groups of students have been identified: the low and high achieving students. The results indicate that by focusing on a small number of courses that are indicators of particularly good or poor performance, it is possible to provide timely warning and support to low achieving students, and advice and opportunities to high performing students.
A primer on deep learning in genomics

Deep learning methods are a class of machine learning techniques capable of identifying highly complex patterns in large datasets. Here, we provide a perspective and primer on deep learning applications for genome analysis. We discuss successful applications in the fields of regulatory genomics, variant calling and pathogenicity scores. We include general guidance for how to effectively use deep learning methods as well as a practical guide to tools and resources. This primer is accompanied by an interactive online tutorial. This perspective presents a primer on deep learning applications for the genomics field. It includes a general guide for how to use deep learning and describes the current tools and resources that are available to the community.
Examples-Rules Guided Deep Neural Network for Makeup Recommendation

In this paper, we consider a fully automatic makeup recommendation system and propose a novel examples-rules guided deep neural network approach. The framework consists of three stages. First, makeup-related facial traits are classified into structured coding. Second, these facial traits are fed into examples-rules guided deep neural recommendation model which makes use of the pairwise of Before-After images and the makeup artist knowledge jointly. Finally, to visualize the recommended makeup style, an automatic makeup synthesis system is developed as well. To this end, a new Before-After facial makeup database is collected and labeled manually, and the knowledge of makeup artist is modeled by knowledge base system. The performance of this framework is evaluated through extensive experimental analyses. The experiments validate the automatic facial traits classification, the recommendation effectiveness in statistical and perceptual ways and the makeup synthesis accuracy which outperforms the state of the art methods by large margin. It is also worthy to note that the proposed framework is a pioneering fully automatic makeup recommendation systems to our best knowledge.
Artificial Intelligence as Structural Estimation: Economic Interpretations of Deep Blue, Bonanza, and AlphaGo

Articial intelligence (AI) has achieved superhuman performance in a growing number of tasks, but understanding and explaining AI remain challenging. This paper claries the connections between machine-learning algorithms to develop AIs and the econometrics of dynamic structural models through the case studies of three famous game AIs. Chess-playing Deep Blue is a calibrated value function, whereas shogiplaying Bonanza is an estimated value function via Rusts (1987) nested xed-point method. AlphaGos supervised-learning policy network is a deep neural network implementation of Hotz and Millers (1993) conditional choice probability estimation; its reinforcement-learning value networkis equivalent to Hotz, Miller, Sanders, and Smiths (1994) conditional choice simulation method. Relaxing these AIs implicit econometric assumptions would improve their structural interpretability.  Articial intelligence, Conditional choice probability, Deep neural network, Dynamic game, Dynamic structural model, Simulation estimator.  This paper beneted from seminar comments at Riken AIP, Georgetown, Tokyo, Osaka, Harvard, and The Third Cambridge Area Economics and Computation Day conference at Microsoft Research New England, as well as conversations with Susan Athey, Xiaohong Chen, Jerry Hausman, Greg Lewis, Robert Miller, Yusuke Narita, Aviv Nevo, Anton Popov, John Rust, Takuo Sugaya, Elie Tamer, and Yosuke Yasuda. yYale Department of Economics and MIT Department of Economics.  mitsuru.igami@gmail.com.
Teachers Learning with Digital Technologies : A review of research and projects REPORT 14 : FUTURELAB SERIES

In recent years we have seen a significant, and long overdue, shift in education from an emphasis on 'teaching' to an emphasis on 'learning'. The focus of attention is now firmly on the learner, their needs, interests and aspirations, at the heart of the education system. This change in focus is to be welcomed as education is, of course, first and foremost about learners. And yet, in this shift are we in danger of losing sight of the people who are able to make this vision for education a reality – namely, the teachers who are asked to change their practices, shift their goals and develop new approaches to meet the needs of education in a changing world? If we are interested in changing education, we need more than ever to be interested in teacher education, teacher knowledge and teacher learning. If we want to encourage different approaches to teaching and learning, and new relationships between pupils and teachers, we need to understand the ways in which teachers come to learn, adapt and make such new approaches a reality. If schools are to become 'learning organisations', with the confidence and capability to adapt to rather than be buffeted by change, we need to invest considerable time and attention to the question of how teachers as professionals can best manage their own learning and development throughout their professional lives. It is in response to these concerns that we have commissioned this review, in order to provide an overview of the research on teacher education, and to provide a roadmap for how this might best be supported in an age of digital technologies. Tellingly, for all the reams of research literature on children's learning with digital technologies, there is comparatively little research on teachers' learning with these new tools. This review focuses specifically on what we understand to constitute powerful learning environments for teachers, and maps onto this the potential affordances of digital technologies to enhance and expand these environments. In so doing, the review raises a number of possibilities for teachers to act as knowledge builders, as collaborators and as reflexive practitioners. It offers models for the use of digital technologies in both initial teacher education and in ongoing professional development. As such, it is intended to act as a stimulus for debate and a call to action for the creation of learning experiences for teachers that are as powerful, relevant …
Process mining: making knowledge discovery process centric

Recently, the Task Force on Process Mining released the Process Mining Manifesto. The manifesto is supported by 53 organizations and 77 process mining experts contributed to it. The active contributions from end-users, tool vendors, consultants, analysts, and researchers illustrate the growing relevance of process mining as a bridge between data mining and business process modeling. This paper summarizes the manifesto and explains why process mining is a highly relevant, but also very challenging, research area. This way we hope to stimulate the broader ACM SIGKDD community to look at process-centric knowledge discovery.
Strategic supply chain framework for the automotive industry

The changing business conditions of the 21 century has led to companies facing issues ranging from globalisation, economic uncertainty to new technologies and increasing consumer demands. In the automobile industry, as manufacturers design and build vehicles globally, their supply chains become increasingly complex with challenges that often stand in the way of profitability and higher shareholder value such as long order-to-delivery lead times, unreliable production schedules, excess inventory across the supply chain, lengthy demand planning cycles and lack of visibility of suppliers. The effect of the global economic meltdown increased the pressure on automotive executives to make right decisions about their supply chain for better performance. In a highly challenging and competitive environment such as today, where supply chain is a popular tool for improving the organisational competitiveness, an efficient and effective supply chain strategy is a must for automotive manufacturers and their component manufacturers so as to meet changing consumer demands. The paper explores the concept of lean and agility as generic supply chain strategies and presents a strategic supply chain framework for the automobile industry as a possible strategy to respond to changing consumer demand.
Robot-assisted Therapy in Stroke Rehabilitation

Research into rehabilitation robotics has grown rapidly and the number of therapeutic rehabilitation robots has expanded dramatically during the last two decades. Robotic rehabilitation therapy can deliver high-dosage and high-intensity training, making it useful for patients with motor disorders caused by stroke or spinal cord disease. Robotic devices used for motor rehabilitation include end-effector and exoskeleton types; herein, we review the clinical use of both types. One application of robot-assisted therapy is improvement of gait function in patients with stroke. Both end-effector and the exoskeleton devices have proven to be effective complements to conventional physiotherapy in patients with subacute stroke, but there is no clear evidence that robotic gait training is superior to conventional physiotherapy in patients with chronic stroke or when delivered alone. In another application, upper limb motor function training in patients recovering from stroke, robot-assisted therapy was comparable or superior to conventional therapy in patients with subacute stroke. With end-effector devices, the intensity of therapy was the most important determinant of upper limb motor recovery. However, there is insufficient evidence for the use of exoskeleton devices for upper limb motor function in patients with stroke. For rehabilitation of hand motor function, either end-effector and exoskeleton devices showed similar or additive effects relative to conventional therapy in patients with chronic stroke. The present evidence supports the use of robot-assisted therapy for improving motor function in stroke patients as an additional therapeutic intervention in combination with the conventional rehabilitation therapies. Nevertheless, there will be substantial opportunities for technical development in near future.
A Framework for Clustering Uncertain Data

The challenges associated with handling uncertain data, in particular with querying and mining, are finding increasing attention in the research community. Here we focus on clustering uncertain data and describe a general framework for this purpose that also allows to visualize and understand the impact of uncertainty—using different uncertainty models—on the data mining results. Our framework constitutes release 0.7 of ELKI (http://elki.dbs.ifi.lmu.de/) and thus comes along with a plethora of implementations of algorithms, distance measures, indexing techniques, evaluation measures and visualization components.
Words or Characters? Fine-grained Gating for Reading Comprehension

Previous work combines word-level and character-level representations using concatenation or scalar weighting, which is suboptimal for high-level tasks like reading comprehension. We present a fine-grained gating mechanism to dynamically combine word-level and character-level representations based on properties of the words. We also extend the idea of fine-grained gating to modeling the interaction between questions and paragraphs for reading comprehension. Experiments show that our approach can improve the performance on reading comprehension tasks, achieving new state-of-the-art results on the Children’s Book Test and Who Did What datasets. To demonstrate the generality of our gating mechanism, we also show improved results on a social media tag prediction
Data, privacy, and the greater good

Large-scale aggregate analyses of anonymized data can yield valuable results and insights that address public health challenges and provide new avenues for scientific discovery. These methods can extend our knowledge and provide new tools for enhancing health and wellbeing. However, they raise questions about how to best address potential threats to privacy while reaping benefits for individuals and to society as a whole. The use of machine learning to make leaps across informational and social contexts to infer health conditions and risks from nonmedical data provides representative scenarios for reflections on directions with balancing innovation and regulation.
Recommendation by Mining Multiple User Behaviors with Group Sparsity

Recently, some recommendation methods try to improve the prediction results by integrating information from user’s multiple types of behaviors. How to model the dependence and independence between different behaviors is critical for them. In this paper, we propose a novel recommendation model, the Group-Sparse Matrix Factorization (GSMF), which factorizes the rating matrices for multiple behaviors into the user and item latent factor space with group sparsity regularization. It can (1) select out the different subsets of latent factors for different behaviors, addressing that users’ decisions on different behaviors are determined by different sets of factors; (2) model the dependence and independence between behaviors by learning the shared and private factors for multiple behaviors automatically ; (3) allow the shared factors between different behaviors to be different, instead of all the behaviors sharing the same set of factors. Experiments on the real-world dataset demonstrate that our model can integrate users’ multiple types of behaviors into recommendation better, compared with other state-of-the-arts.
Does the Geometry of Word Embeddings Help Document Classification? A Case Study on Persistent Homology Based Representations

We investigate the pertinence of methods from algebraic topology for text data analysis. These methods enable the development of mathematically-principled isometric-invariant mappings from a set of vectors to a document embedding, which is stable with respect to the geometry of the document in the selected metric space. In this work, we evaluate the utility of these topology-based document representations in traditional NLP tasks, specifically document clustering and sentiment classification. We find that the embeddings do not benefit text analysis. In fact, performance is worse than simple techniques like tf-idf, indicating that the geometry of the document does not provide enough variability for classification on the basis of topic or sentiment in the chosen
Argumentation Quality Assessment: Theory vs. Practice

Selected findings from the study: – Generally high correlations (.30 – 1.00). – Most correlations very intutive, very few unintuitive. – Some theoretical dimensions hard to separate. – More convincing correlates most with overall quality (.64). – Thought through shows the highest ratings (overall quality 1.8). – Off-topic shows the lowest ratings (overall quality 1.1). www.webis.de Bauhaus-Universität Weimar * www.cs.toronto.edu/compling University of Toronto ** www.ukp.tu-darmstadt.de Technische Universität Darmstadt *** ie.ibm.com IBM Research Ireland ****
A review of induction motors signature analysis as a medium for faults detection

The purpose is to introduce in a concise manner the fundamental theory, main results, and practical applications of motor signature analysis for the detection and the localization of abnormal electrical and mechanical conditions that indicate, or may lead to, a failure of induction motors. The paper is focused on the so-called motor current signature analysis which utilizes the results of spectral analysis of the stator current. The paper is purposefully written without “state-of-the-art” terminology for the benefit of practicing engineers in facilities today who may not be familiar with signal processing.
Oligarchic Control of Business-to-Business Blockchains

The innovative ideas behind blockchain offer exciting perspectives in research and development of electronic distributed ledger technologies. Bitcoin introduced an "eventual consistency" blockchain with the apparent intent of providing an open and inclusive system for secure, anonymous, yet cost-effective financial transactions based on peer-to-peer computing. It uses Proof ofWork as a cryptographic puzzle to control growth of a chain and to strengthen the resiliency of a chain against subsequent rewrite attempts. Commercial sectors and the public sector have realized the potential of this technical approach, and we now see a number of blockchains that may be called private, closed or permissioned – and that may eschew the values inherent in the design of Bitcoin type systems. From a value-neutral research perspective, there are no clear definitions of blockchain attributes such as "private" or "permissioned", nor is there typically a good understanding of the trust assumptions that clients must make when using such services – for example in systems that do not rely on Proof of Work but on a limited and controlled set of consensus-creating agents. We believe that more research in this direction could establish firm foundations for domain-specific or coalition-specific blockchains, and that such underpinnings would offer novel trust architectures beyond those provided by completely open/public blockchains and third-party operated, closed/private blockchains.
Generative Adversarial Network Architectures For Image Synthesis Using Capsule Networks

In this paper, we propose Generative Adversarial Network (GAN) architectures that use Capsule Networks for image-synthesis. Based on the principal of positionalequivariance of features, Capsule Network’s ability to encode spatial relationships between the features of the image helps it become a more powerful critic in comparison to Convolutional Neural Networks (CNNs) used in current architectures for image synthesis. Our proposed GAN architectures learn the data manifold much faster and therefore, synthesize visually accurate images in significantly lesser number of training samples and training epochs in comparison to GANs and its variants that use CNNs. Apart from analyzing the quantitative results corresponding the images generated by different architectures, we also explore the reasons for the lower coverage and diversity explored by the GAN architectures that use CNN critics.
VTR 7.0: Next Generation Architecture and CAD System for FPGAs

Exploring architectures for large, modern FPGAs requires sophisticated software that can model and target hypothetical devices. Furthermore, research into new CAD algorithms often requires a complete and open source baseline CAD flow. This article describes recent advances in the open source Verilog-to-Routing (VTR) CAD flow that enable further research in these areas. VTR now supports designs with multiple clocks in both timing analysis and optimization. Hard adder/carry logic can be included in an architecture in various ways and significantly improves the performance of arithmetic circuits. The flow now models energy consumption, an increasingly important concern. The speed and quality of the packing algorithms have been significantly improved. VTR can now generate a netlist of the final post-routed circuit which enables detailed simulation of a design for a variety of purposes. We also release new FPGA architecture files and models that are much closer to modern commercial architectures, enabling more realistic experiments. Finally, we show that while this version of VTR supports new and complex features, it has a 1.5× compile time speed-up for simple architectures and a 6× speed-up for complex architectures compared to the previous release, with no degradation to timing or wire-length quality.
Interventions for alleviating loneliness among older persons: a critical review.

Loneliness is common among older persons and has been associated with health and mental health risks. This systematic review examines the utility of loneliness interventions among older persons.



Thirty-four intervention studies were used.  The study was conducted between 1996 and 2011, included a sample of older adults, implemented an intervention affecting loneliness or identified a situation that directly affected loneliness, included in its outcome measures the effects of the intervention or situation on loneliness levels or on loneliness-related measures (e.g., social interaction), and included in its analysis pretest-posttest comparisons.



Studies were accessed using the databases PsycINFO, MEDLINE, ScienceDirect, AgeLine, PsycBOOKS, and Google Scholar for the years 1996-2011.



Interventions were classified based on population, format, and content and were evaluated for quality of design and efficacy.



Twelve studies were effective in reducing loneliness according to the review criteria, and 15 were evaluated as potentially effective. The findings suggest that it is possible to reduce loneliness by using educational interventions focused on social networks maintenance and enhancement.



Multiple approaches show promise, although flawed design often prevents proper evaluation of efficacy. The value of specific therapy techniques in reducing loneliness is highlighted and warrants a wider investigation. Studies of special populations, such as the cognitively impaired, are also needed.
A 900MHz shielded bow-tie antenna system for ground penetrating radar

A back-cavity shielded bow-tie antenna system working at 900MHz center frequency for ground-coupled GPR application is investigated numerically and experimentally in this paper. Bow-tie geometrical structure is modified for a compact design and back-cavity assembly. A layer of absorber is employed to overcome the back reflection by omni-directional radiation pattern of a bow-tie antenna in H-plane, thus increasing the SNR and improve the isolation between T and R antennas as well. The designed antenna system is applied to a prototype GPR system. Tested data shows that the back-cavity shielded antenna works satisfactorily in the 900MHz GPR system.