nils-herrmann/clean_scirepeval huggingface dataset:
NAIS: Neural Attentive Item Similarity Model for Recommendation

Item-to-item collaborative filtering (aka.item-based CF) has been long used for building recommender systems in industrial settings, owing to its interpretability and efficiency in real-time personalization. It builds a user's profile as her historically interacted items, recommending new items that are similar to the user's profile. As such, the key to an item-based CF method is in the estimation of item similarities. Early approaches use statistical measures such as cosine similarity and Pearson coefficient to estimate item similarities, which are less accurate since they lack tailored optimization for the recommendation task. In recent years, several works attempt to learn item similarities from data, by expressing the similarity as an underlying model and estimating model parameters by optimizing a recommendation-aware objective function. While extensive efforts have been made to use shallow linear models for learning item similarities, there has been relatively less work exploring nonlinear neural network models for item-based CF. In this work, we propose a neural network model named Neural Attentive Item Similarity model (NAIS) for item-based CF. The key to our design of NAIS is an attention network, which is capable of distinguishing which historical items in a user profile are more important for a prediction. Compared to the state-of-the-art item-based CF method Factored Item Similarity Model (FISM)  , our NAIS has stronger representation power with only a few additional parameters brought by the attention network. Extensive experiments on two public benchmarks demonstrate the effectiveness of NAIS. This work is the first attempt that designs neural network models for item-based CF, opening up new research possibilities for future developments of neural recommender systems.
PHM-Oriented Integrated Fusion Prognostics for Aircraft Engines Based on Sensor Data

As the heart of an aircraft, the aircraft engine's condition directly affects the safety, reliability, and operation of the aircraft. Prognostics and health management for aircraft engines can provide advance warning of failure and estimate the remaining useful life. However, aircraft engine systems are complex with both intangible and uncertain factors, it is difficult to model the complex degradation process, and no single prognostic approach can effectively solve this critical and complicated problem. Thus, fusion prognostics is conducted to obtain more accurate prognostics results. In this paper, a prognostics and health management-oriented integrated fusion prognostic framework is developed to improve the system state forecasting accuracy. This framework strategically fuses the monitoring sensor data and integrates the strengths of the data-driven prognostics approach and the experience-based approach while reducing their respective limitations. As an application example, this developed fusion prognostics framework is employed to predict the remaining useful life of an aircraft gas turbine engine based on sensor data. The results demonstrate that the proposed fusion prognostics framework is an effective prognostics tool, which can provide a more accurate and robust remaining useful life estimation than any single prognostics method.
Living Labs: A Bibliometric Analysis

The objective of this study is to understand how Living Lab(s) (LL) as a concept and research approach has developed, proliferated and influenced scholarly research to date. The goal is in assisting both the LL and Action Design Research (ADR) communities in advancing both fields by establishing understanding, commonalities and challenges in advancing both research agendas. We adopt a bibliometric methodology to understand the scholarly impact, contribution and intellectual structure of LL as a new approach to innovation. We conclude with recommendations on advancing both ADR and LL fields of research, highlighting that increased crosscollaboration going forward offers clear opportunities to both fields.
Characteristics of YouTube network traffic at a campus network - Measurements, models, and implications

1389-1286/$ see front matter 2008 Elsevier B.V doi:10.1016/j.comnet.2008.09.022 * Corresponding author.  E-mail address: zink@cs.umass.edu (M. Zink). 1 http://www.usatoday.com/tech/news/2006-07 x.htm http://en.wikipedia.org/wiki/YouTube. User-Generated Content has become very popular since new web services such as YouTube allow for the distribution of user-produced media content. YouTube-like services are different from existing traditional VoD services in that the service provider has only limited control over the creation of new content. We analyze how content distribution in YouTube is realized and then conduct a measurement study of YouTube traffic in a large university campus network. Based on these measurements, we analyzed the duration and the data rate of streaming sessions, the popularity of videos, and access patterns for video clips from the clients in the campus network. The analysis of the traffic shows that trace statistics are relatively stable over short-term periods while long-term trends can be observed. We demonstrate how synthetic traces can be generated from the measured traces and show how these synthetic traces can be used as inputs to trace-driven simulations. We also analyze the benefits of alternative distribution infrastructures to improve the performance of a YouTube-like VoD service. The results of these simulations show that P2P-based distribution and proxy caching can reduce network traffic significantly and allow for faster access to video clips.
Customer relationship management mechanisms: A systematic review of the state of the art literature and recommendations for future research

In the information systems, customer relationship management (CRM) is the overall process of building and maintaining profitable customer relationships by delivering superior customer value and satisfaction with the goal of improving the business relationships with customers. Also, it is the strongest and the most efficient approach to maintaining and creating the relationships with customers. However, to the best of our knowledge and despite its importance, there is not any comprehensive and systematic study about reviewing and analyzing its important techniques. Therefore, in this paper, a comprehensive study and survey on the state of the art mechanisms in the scope of the CRM are done. It follows this goal by looking at five categories in which CRM plays a significant role: E-CRM, knowledge management, data mining, data quality and, social CRM. In each category, a couple of studies are presented and determinants of CRM are described and discussed. The major development in these five categories is reviewed and the new challenges are outlined. Also, a systematic literature review (SLR) in each of these five categories is provided. Furthermore, insights into the identification of open issues and guidelines for future research are provided.
Errors in clinical laboratories or errors in laboratory medicine?

Laboratory testing is a highly complex process and, although laboratory services are relatively safe, they are not as safe as they could or should be. Clinical laboratories have long focused their attention on quality control methods and quality assessment programs dealing with analytical aspects of testing. However, a growing body of evidence accumulated in recent decades demonstrates that quality in clinical laboratories cannot be assured by merely focusing on purely analytical aspects. The more recent surveys on errors in laboratory medicine conclude that in the delivery of laboratory testing, mistakes occur more frequently before (pre-analytical) and after (post-analytical) the test has been performed. Most errors are due to pre-analytical factors (46-68.2% of total errors), while a high error rate (18.5-47% of total errors) has also been found in the post-analytical phase. Errors due to analytical problems have been significantly reduced over time, but there is evidence that, particularly for immunoassays, interference may have a serious impact on patients. A description of the most frequent and risky pre-, intra- and post-analytical errors and advice on practical steps for measuring and reducing the risk of errors is therefore given in the present paper. Many mistakes in the Total Testing Process are called "laboratory errors", although these may be due to poor communication, action taken by others involved in the testing process (e.g., physicians, nurses and phlebotomists), or poorly designed processes, all of which are beyond the laboratory's control. Likewise, there is evidence that laboratory information is only partially utilized. A recent document from the International Organization for Standardization (ISO) recommends a new, broader definition of the term "laboratory error" and a classification of errors according to different criteria. In a modern approach to total quality, centered on patients' needs and satisfaction, the risk of errors and mistakes in pre- and post-examination steps must be minimized to guarantee the total quality of laboratory services.
EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples

Recent studies have highlighted the vulnerability of deep neural networks (DNNs) to adversarial examples a visually indistinguishable adversarial image can easily be crafted to cause a well-trained model to misclassify. Existing methods for crafting adversarial examples are based on L2 and L∞ distortion metrics. However, despite the fact that L1 distortion accounts for the total variation and encourages sparsity in the perturbation, little has been developed for crafting L1-based adversarial examples. In this paper, we formulate the process of attacking DNNs via adversarial examples as an elastic-net regularized optimization problem. Our elastic-net attacks to DNNs (EAD) feature L1oriented adversarial examples and include the state-of-the-art L2 attack as a special case. Experimental results on MNIST, CIFAR10 and ImageNet show that EAD can yield a distinct set of adversarial examples with small L1 distortion and attains similar attack performance to the state-of-the-art methods in different attack scenarios. More importantly, EAD leads to improved attack transferability and complements adversarial training for DNNs, suggesting novel insights on leveraging L1 distortion in adversarial machine learning and security implications of DNNs.
A Japanese-English Patent Parallel Corpus

We describe a Japanese-English patent parallel corpus created from the Japanese and US patent data provided for the NTCIR-6 patent retrieval task. The corpus contains about 2 million sentence pairs that were aligned automatically. This is the largest Japanese-English parallel corpus, which will be available to the public after the 7th NTCIR workshop meeting. We estimated that about 97% of the sentence pairs were correct alignments and about 90% of the alignments were adequate translations whose English sentences reflected almost perfectly the contents of the corresponding Japanese sentences.
BetterChoice: A migraine drug recommendation system based on Neo4J

Migraine is a common disease throughout the world. Not only does it affect the life of people tremendously, but it also leads to high costs, e.g. due to inability to work or various required drug-taking cycles for finding the best drug for a patient. Solving the latter aspect could help to improve the life of patients and decrease the impact of the other consequences. Therefore, in this paper, we present an approach for a drug recommendation system based on the highly scalable native graph database Neo4J. The presented system uses simulated patient data to help physicians gain more transparency about which drug fits a migraine patient best considering her individual features. Our evaluation shows that the proposed system works as intended. This means that only drugs with highest relevance scores and no interactions with the patient's diseases, drugs or pregnancy are recommended.
Online Ranking Prediction in Non-stationary Environments

Recommender systems have to serve in online environments which can be highly non-stationary.1. Traditional recommender algorithmsmay periodically rebuild their models, but they cannot adjust to quick changes in trends caused by timely information. In our experiments, we observe that even a simple, but online trained recommender model can perform significantly better than its batch version. We investigate online learning based recommender algorithms that can efficiently handle non-stationary data sets. We evaluate our models over seven publicly available data sets. Our experiments are available as an open source project2.
Joint Dimensionality Reduction and Metric Learning: A Geometric Take

To be tractable and robust to data noise, existing metric learning algorithms commonly rely on PCA as a pre-processing step. How can we know, however, that PCA, or any other specific dimensionality reduction technique, is the method of choice for the problem at hand? The answer is simple: We cannot! To address this issue, in this paper, we develop a Riemannian framework to jointly learn a mapping performing dimensionality reduction and a metric in the induced space. Our experiments evidence that, while we directly work on high-dimensional features, our approach yields competitive runtimes with and higher accuracy than state-of-the-art metric learning algorithms.
A Structured Model For Action Detection

A dominant paradigm for learning-based approaches in computer vision is training generic models, such as ResNet for image recognition, or I3D for video understanding, on large datasets and allowing them to discover the optimal representation for the problem at hand. While this is an obviously attractive approach, it is not applicable in all scenarios. We claim that action detection is one such challenging problem the models that need to be trained are large, and the labeled data is expensive to obtain. To address this limitation, we propose to incorporate domain knowledge into the structure of the model to simplify optimization. In particular, we augment a standard I3D network with a tracking module to aggregate long term motion patterns, and use a graph convolutional network to reason about interactions between actors and objects. Evaluated on the challenging AVA dataset, the proposed approach improves over the I3D baseline by 5.5% mAP and over the state-ofthe-art by 4.8% mAP.
Chapter 2. Map-reduce and the New Software Stack 2.2.7 Exercises for Section 2.2

one becomes available. The Master must also inform each Reduce task that the location of its input from that Map task has changed. Dealing with a failure at the node of a Reduce worker is simpler. The Master simply sets the status of its currently executing Reduce tasks to idle. These will be rescheduled on another reduce worker later.  Suppose we execute the word-count map-reduce program described in this section on a large repository such as a copy of the Web. We shall use 100 Map tasks and some number of Reduce tasks. (a) Suppose we do not use a combiner at the Map tasks. Do you expect there to be significant skew in the times taken by the various reducers to process their value list? Why or why not? (b) If we combine the reducers into a small number of Reduce tasks, say 10 tasks, at random, do you expect the skew to be significant? What if we instead combine the reducers into 10,000 Reduce tasks? ! (c) Suppose we do use a combiner at the 100 Map tasks. Do you expect skew to be significant? Why or why not? 2.3 Algorithms Using Map-Reduce Map-reduce is not a solution to every problem, not even every problem that profitably can use many compute nodes operating in parallel. As we mentioned in Section 2.1.2, the entire distributed-file-system milieu makes sense only when files are very large and are rarely updated in place. Thus, we would not expect to use either a DFS or an implementation of map-reduce for managing on-line retail sales, even though a large on-line retailer such as Amazon.com uses thousands of compute nodes when processing requests over the Web. The reason is that the principal operations on Amazon data involve responding to searches for products, recording sales, and so on, processes that involve relatively little calculation and that change the database. 2 On the other hand, Amazon might use map-reduce to perform certain analytic queries on large amounts of data, such as finding for each user those users whose buying patterns were most similar. The original purpose for which the Google implementation of map-reduce was created was to execute very large matrix-vector multiplications as are needed in the calculation of PageRank . We shall see that matrix-vector and matrix-matrix calculations fit nicely into the map-reduce 2 Remember that even looking at a product …
Pid and interval type-2 fuzzy logic control of double inverted pendulum system

In this study, interval type2 fuzzy logic (IT2FL) and PID controller is designed for swing-up position control of double inverted pendulum (DIP) system. The double inverted pendulum system consists of two rigid bars connected by a revolute joint. Mass of the revolute joint is included in the dynamic model.Rigid bars in the system are assumed to experience planar motion. The pendulum system is connected to the base by means of a revolute joint. Torque provided through a motor mounted to the base is used for position control of the system. PID (Proportional-Derivative-Integral) and interval type2 fuzzy logic controllers are developed by using the same performance criteria for position control of double inverted pendulum system. IT2FL controller is similar with type1 fuzzy logic controller. IT2FL system provides soft decision boundaries, whereas a type-1 fuzzy logic system provides a hard decision boundary. Membership function in interval type2 fuzzy logic set as an area called Footprint of Uncertainty (FOU) which limited by two type1 membership function those are upper membership function (UMF) and lower membership function (LMF).System behaviour is obtained by computer simulation using developed controllers respectively. Computer simulation results are compared in order to evaluate applicability of developed controllers. MATLAB/Simulink software is used in computer simulations.
Technology Integration Barriers: Urban School Mathematics Teachers Perspectives.

Despite the promise of technology in education, many practicing teachers face several challenges when trying to effectively integrate technology into their classroom instruction. Additionally, while national statistics cite a remarkable improvement in access to computer technology tools in schools, teacher surveys show consistent declines in the use and integration of computer technology to enhance student learning. This article reports on primary technology integration barriers that mathematics teachers identified when using technology in their classrooms. Suggestions to overcome some of these barriers are also provided.
Intrinsic and extrinsic motivation in early adolescents' friendship development: friendship selection, influence, and prospective friendship quality.

Friendships are essential for adolescent social development. However, they may be pursued for varying motives, which, in turn, may predict similarity in friendships via social selection or social influence processes, and likely help to explain friendship quality. We examined the effect of early adolescents' (N = 374, 12-14 years) intrinsic and extrinsic friendship motivation on friendship selection and social influence by utilizing social network modeling. In addition, longitudinal relations among motivation and friendship quality were estimated with structural equation modeling. Extrinsic motivation predicted activity in making friendship nominations during the sixth grade and lower friendship quality across time. Intrinsic motivation predicted inactivity in making friendship nominations during the sixth, popularity as a friend across the transition to middle school, and higher friendship quality across time. Social influence effects were observed for both motives, but were more pronounced for intrinsic motivation.
The Four Corners of the Kosmos

An extensive data search among various types of developmental and evolutionary sequences yielded a `four quadrant' model of consciousness and its development (the four quadrants being intentional, behavioural, cultural, and social). Each of these dimensions was found to unfold in a sequence of at least a dozen major stages or levels. Combining the four quadrants with the dozen or so major levels in each quadrant yields an integral theory of consciousness that is quite comprehensive in its nature and scope. This model is used to indicate how a general synthesis and integration of twelve of the most influential schools of consciousness studies can be effected, and to highlight some of the most significant areas of future research. The conclusion is that an `all-quadrant, all-level' approach is the minimum degree of sophistication that we need into order to secure anything resembling a genuinely integral theory of consciousness.
Concept Based Short Text Stream Classification with Topic Drifting Detection

Short text stream classification is a challengingand significant task due to the characteristics of short length, weak signal, high velocity and especially topic drifting in short text stream. However, this challenge has received little attention from the research community. Motivated by this, we propose a new feature extension approach for short text stream classification using a large scale, general purpose semantic network obtained from a web corpus. Our approach is built on an incremental ensemble classification model. First, in terms of the open semantic network, we introduce more semantic contexts in short texts to make up of the data sparsity. Meanwhile, we disambiguate terms by their semantics to reduce the noise impact. Second, to effectively track hidden topic drifts, we propose a concept cluster based topic drifting detection method. Finally, extensive experiments demonstratethat our approach can detect topic drifts effectively compared to several well-known concept drifting detection methods in data streams. Meanwhile, our approach can perform best in the classification of text data streams compared to several stateof-the-art short text classification approaches.
Pigeon-inspired optimization: a new swarm intelligence optimizer for air robot path planning

The purpose of this paper is to present a novel swarm intelligence optimizer — pigeoninspired optimization (PIO) — and describe how this algorithm was applied to solve air robot path planning problems.  The formulation of threat resources and objective function in air robot path planning is given. The mathematical model and detailed implementation process of PIO is presented. Comparative experiments with standard differential evolution (DE) algorithm are also conducted.  The feasibility, effectiveness and robustness of the proposed PIO algorithm are shown by a series of comparative experiments with standard DE algorithm. The computational results also show that the proposed PIO algorithm can effectively improve the convergence speed, and the superiority of global search is also verified in various cases.  In this paper, the authors first presented a PIO algorithm. In this newly presented algorithm, map and compass operator model is presented based on magnetic field and sun, while landmark operator model is designed based on landmarks. The authors also applied this newly proposed PIO algorithm for solving air robot path planning problems.