nils-herrmann/clean_scirepeval huggingface dataset:
Cntr: Lightweight OS Containers

Container-based virtualization has become the de-facto standard for deploying applications in data centers. However, deployed containers frequently include a wide-range of tools (e.g., debuggers) that are not required for applications in the common use-case, but they are included for rare occasions such as in-production debugging. As a consequence, containers are significantly larger than necessary for the common case, thus increasing the build and deployment time. CNTR1 provides the performance benefits of lightweight containers and the functionality of large containers by splitting the traditional container image into two parts: the “fat” image — containing the tools, and the “slim” image — containing the main application. At run-time, CNTR allows the user to efficiently deploy the “slim” image and then expand it with additional tools, when and if necessary, by dynamically attaching the “fat” image. To achieve this, CNTR transparently combines the two container images using a new nested namespace, without any modification to the application, the container manager, or the operating system. We have implemented CNTR in Rust, using FUSE, and incorporated a range of optimizations. CNTR supports the full Linux filesystem API, and it is compatible with all container implementations (i.e., Docker, rkt, LXC, systemd-nspawn). Through extensive evaluation, we show that CNTR incurs reasonable performance overhead while reducing, on average, by 66.6% the image size of the Top-50 images available on Docker Hub.
The loss of skeletal muscle strength, mass, and quality in older adults: the health, aging and body composition study.

The loss of muscle mass is considered to be a major determinant of strength loss in aging. However, large-scale longitudinal studies examining the association between the loss of mass and strength in older adults are lacking.



Three-year changes in muscle mass and strength were determined in 1880 older adults in the Health, Aging and Body Composition Study. Knee extensor strength was measured by isokinetic dynamometry. Whole body and appendicular lean and fat mass were assessed by dual-energy x-ray absorptiometry and computed tomography.



Both men and women lost strength, with men losing almost twice as much strength as women. Blacks lost about 28% more strength than did whites. Annualized rates of leg strength decline (3.4% in white men, 4.1% in black men, 2.6% in white women, and 3.0% in black women) were about three times greater than the rates of loss of leg lean mass ( approximately 1% per year). The loss of lean mass, as well as higher baseline strength, lower baseline leg lean mass, and older age, was independently associated with strength decline in both men and women. However, gain of lean mass was not accompanied by strength maintenance or gain (ss coefficients; men, -0.48 +/- 4.61, p =.92, women, -1.68 +/- 3.57, p =.64).



Although the loss of muscle mass is associated with the decline in strength in older adults, this strength decline is much more rapid than the concomitant loss of muscle mass, suggesting a decline in muscle quality. Moreover, maintaining or gaining muscle mass does not prevent aging-associated declines in muscle strength.
Targeting Ultimate Accuracy: Face Recognition via Deep Embedding

Face Recognition has been studied for many decades. As opposed to traditional hand-crafted features such as LBP and HOG, much more sophisticated features can be learned automatically by deep learning methods in a data-driven way. In this paper, we propose a two-stage approach that combines a multi-patch deep CNN and deep metric learning, which extracts low dimensional but very discriminative features for face verification and recognition. Experiments show that this method outperforms other state-of-the-art methods on LFW dataset, achieving 99.77% pair-wise verification accuracy and significantly better accuracy under other two more practical protocols. This paper also discusses the importance of data size and the number of patches, showing a clear path to practical high-performance face recognition systems in real world.
Anomaly Detection via Minimum Likelihood Generative Adversarial Networks

Anomaly detection aims to detect abnormal events by a model of normality. It plays an important role in many domains such as network intrusion detection, criminal activity identity and so on. With the rapidly growing size of accessible training data and high computation capacities, deep learning based anomaly detection has become more and more popular. In this paper, a new domain-based anomaly detection method based on generative adversarial networks (GAN) is proposed. Minimum likelihood regularization is proposed to make the generator produce more anomalies and prevent it from converging to normal data distribution. Proper ensemble of anomaly scores is shown to improve the stability of discriminator effectively. The proposed method has achieved significant improvement than other anomaly detection methods on Cifar10 and UCI datasets.
Blockchain distributed ledger technologies for biomedical and health care applications

To introduce blockchain technologies, including their benefits, pitfalls, and the latest applications, to the biomedical and health care domains.


Target Audience
Biomedical and health care informatics researchers who would like to learn about blockchain technologies and their applications in the biomedical/health care domains.



The covered topics include: (1) introduction to the famous Bitcoin crypto-currency and the underlying blockchain technology; (2) features of blockchain; (3) review of alternative blockchain technologies; (4) emerging nonfinancial distributed ledger technologies and applications; (5) benefits of blockchain for biomedical/health care applications when compared to traditional distributed databases; (6) overview of the latest biomedical/health care applications of blockchain technologies; and (7) discussion of the potential challenges and proposed solutions of adopting blockchain technologies in biomedical/health care domains.
Examining the Effect of Academic Procrastination on Achievement Using LMS Data in e-Learning

This study aimed to investigate the effect of academic procrastination on e-learning course achievement. Because all of the interactions among students, instructors, and contents in an e-learning environment were automatically recorded in a learning management system (LMS), procrastination such as the delays in weekly scheduled learning and late submission of assignments could be identified from log data. Among 569 college students who enrolled in an e-learning course in Korea, the absence and late submission of assignments were chosen to measure academic procrastination in e-learning. Multiple regression analysis was conducted to examine the relationship between academic procrastination and course achievement. The results showed that the absence and late submission of assignments were negatively significant in predicting course achievement. Furthermore, the study explored the predictability of academic procrastination on course achievement at four points of the 15-week course to test its potential for early prediction. The results showed that the regression model at each time point significantly predicted course achievement, and the predictability increased as time passed. Based on the findings, practical implications for facilitating a successful e-learning environment were suggested, and the potential of analyzing LMS data was discussed.
Evolving Kolb : Experiential Education in the Age of Neuroscience

In pursuing a refined Learning Styles Inventory (LSI), Kolb has moved away from the original cyclical nature of his model of experiential learning. Kolb’s model has not adapted to current research and has failed to increase understanding of learning. A critical examination of Kolb’s experiential learning theory in terms of epistemology, educational neuroscience, and model analysis reveals the need for an experiential learning theory that addresses these issues. This article re-conceptualizes experiential learning by building from cognitive neuroscience, Dynamic Skill Theory, and effective experiential education practices into a self-adjusting fractal-like cycle that we call CoConstructed Developmental Teaching Theory (CDTT). CDTT is a biologically driven model of teaching. It is a cohesive framework of ideas that have been presented before but not linked in a coherent manner to the biology of the learning process. In addition, it orders the steps in a neurobiologically supported sequence. CDTT opens new avenues of research utilizing evidenced-based teaching practices and provides a basis for a new conversation. However, thorough testing remains.
Fast Parameter Adaptation for Few-shot Image Captioning and Visual Question Answering

Given only a few image-text pairs, humans can learn to detect semantic concepts and describe the content. For machine learning algorithms, they usually require a lot of data to train a deep neural network to solve the problem. However, it is challenging for the existing systems to generalize well to the few-shot multi-modal scenario, because the learner should understand not only images and texts but also their relationships from only a few examples. In this paper, we tackle two multi-modal problems, i.e., image captioning and visual question answering (VQA), in the few-shot setting.
 We propose Fast Parameter Adaptation for Image-Text Modeling (FPAIT) that learns to learn jointly understanding image and text data by a few examples. In practice, FPAIT has two benefits. (1) Fast learning ability. FPAIT learns proper initial parameters for the joint image-text learner from a large number of different tasks. When a new task comes, FPAIT can use a small number of gradient steps to achieve a good performance. (2) Robust to few examples. In few-shot tasks, the small training data will introduce large biases in Convolutional Neural Networks (CNN) and damage the learner's performance. FPAIT leverages dynamic linear transformations to alleviate the side effects of the small training set. In this way, FPAIT flexibly normalizes the features and thus reduces the biases during training. Quantitatively, FPAIT achieves superior performance on both few-shot image captioning and VQA benchmarks.
Creativity in Story Generation From the Ground Up: Non-deterministic Simulation driven by Narrative

Creativity in narrative requires careful management of knowledge but story generation systems focusing on creativity have typically circumvented this level of detail by using high level descriptions of events and relations. While this has proven effective for plot generation, narrative generation can be drastically enriched with a grounded representation of actions based on low level simulation. This level of detail and robust knowledge representation can form the basis for a conceptual space exploration driven by narrative knowledge, namely by guiding non-deterministic generation of successive simulation states composing a story. This paper presents and updated version of the story generation system STellA that implements this hybrid model, along with results and discussion on the relative benefits of the
Consumer attitudes toward online video advertising: An empirical study on YouTube as platform

The usage of Internet is getting widespread, and the service of online video is getting more and more popular. The revenue of the web service providers comes mostly from the advertisements. This study investigates the attitudes toward the advertisements while watching online videos in YouTube. We followed the research of users' attitudes toward advertisements (Brackett & Carr, 2001) and combined it with the theory of reasoned action and the flow theory in the psychology. This study investigates the factor affecting attitudes toward advertisements and the influence to behaviors. Our findings show that the model explained most of the variance of attitudes toward advertisements in sites providing services of online videos indicating that the model is confirmed in the situation of online video advertising. The conclusion and managerial implications have further discussions.
Overview of Maximum Power Point Tracking Control Methods for PV Systems

Maximum power point tracking (MPPT) controllers play an important role in photovoltaic systems. They maximize the output power of a PV array for a given set of conditions. This paper presents an overview of the different MPPT techniques. Each technique is evaluated on its ability to detect multiple maxima, convergence speed, ease of implementation, efficiency over a wide output power range, and cost of implementation. The perturbation and observation (P & O), and incremental conductance (IC) algorithms are widely used techniques, with many variants and optimization techniques reported. For this reason, this paper evaluates the performance of these two common approaches from a dynamic and steady state perspective.
Design and natural science research on information technology

Research in IT must address the design tasks faced by practitioners. Real problems must be properly conceptualized and represented, appropriate techniques for their solution must be constructed, and solutions must be implemented and evaluated using appropriate criteria. If significant progress is to be made, IT research must also develop an understanding of how and why IT systems work or do not work. Such an understanding must tie together natural laws governing IT systems with natural laws governing the environments in which they operate. This paper presents a two dimensional framework for research in information technology. The first dimension is based on broad types of design and natural science research activities: build, evaluate, theorize, and justify. The second dimension is based on broad types of outputs produced by design research: representational constructs, models, methods, and instantiations. We argue that both design science and natural science activities are needed to insure that IT research is both relevant and effective.
Learning to classify e-mail

In this paper we study supervised and semi-supervised classification of e-mails. We consider two tasks: filing e-mails into folders and spam e-mail filtering. Firstly, in a supervised learning setting, we investigate the use of random forest for automatic e-mail filing into folders and spam e-mail filtering. We show that random forest is a good choice for these tasks as it runs fast on large and high dimensional databases, is easy to tune and is highly accurate, outperforming popular algorithms such as decision trees, support vector machines and naïve Bayes. We introduce a new accurate feature selector with linear time complexity. Secondly, we examine the applicability of the semi-supervised co-training paradigm for spam e-mail filtering by employing random forests, support vector machines, decision tree and naïve Bayes as base classifiers. The study shows that a classifier trained on a small set of labelled examples can be successfully boosted using unlabelled examples to accuracy rate of only 5% lower than a classifier trained on all labelled examples. We investigate the performance of co-training with one natural feature split and show that in the domain of spam e-mail filtering it can be as competitive as co-training with two natural feature splits.
Causality in the Social and Behavioral Sciences ∗

This paper aims to acquaint researchers in the quantitative social and behavior sciences with recent advances in causal inference which provide a systematic methodology for defining, estimating, testing, and defending causal claims in experimental and observational studies. These advances are illustrated using a general theory of causation based on nonparametric structural equation models (SEM) – a natural generalization of those used by econometricians and social scientists in the 1950-60s, which provides a coherent mathematical foundation for the analysis of causes and counterfactuals. In particular, the paper surveys the development of mathematical tools for inferring (from a combination of data and assumptions) answers to three types of causal queries: (1) queries about the effects of potential interventions, (also called “causal effects” or “policy evaluation”) (2) queries about probabilities of counterfactuals, (including assessment of “regret,” “attribution” or “causes of effects”) and (3) queries about direct and indirect effects (also known as “mediation”). Finally, the paper clarifies the role of propensity score matching in causal analysis, defines the relationships between the structural and potential-outcome frameworks, and develops symbiotic tools that use the strong features of both. A paper submitted to Sociological Methodology. This research benefited from conversations with Peter Bentler, Stephen Morgan, Jeffrey Wooldridge and was supported in parts by NIH grant #1R01 LM009961-01, NSF grant #IIS-0914211, and ONR grant #N000-14-09-1-0665.
Predicting the Next Location: A Recurrent Model with Spatial and Temporal Contexts

Spatial and temporal contextual information plays a key role for analyzing user behaviors, and is helpful for predicting where he or she will go next. With the growing ability of collecting information, more and more temporal and spatial contextual information is collected in systems, and the location prediction problem becomes crucial and feasible. Some works have been proposed to address this problem, but they all have their limitations. Factorizing Personalized Markov Chain (FPMC) is constructed based on a strong independence assumption among different factors, which limits its performance. Tensor Factorization (TF) faces the cold start problem in predicting future actions. Recurrent Neural Networks (RNN) model shows promising performance comparing with PFMC and TF, but all these methods have problem in modeling continuous time interval and geographical distance. In this paper, we extend RNN and propose a novel method called Spatial Temporal Recurrent Neural Networks (ST-RNN). ST-RNN can model local temporal and spatial contexts in each layer with time-specific transition matrices for different time intervals and distance-specific transition matrices for different geographical distances. Experimental results show that the proposed ST-RNN model yields significant improvements over the competitive compared methods on two typical datasets, i.e., Global Terrorism Database (GTD) and Gowalla dataset.
A scientific theory of Ars Memoriae: Spatial view cells in a continuous attractor network with linked items.

The art of memory (ars memoriae) used since classical times includes using a well-known scene to associate each view or part of the scene with a different item in a speech. This memory technique is also known as the "method of loci." The new theory is proposed that this type of memory is implemented in the CA3 region of the hippocampus where there are spatial view cells in primates that allow a particular view to be associated with a particular object in an event or episodic memory. Given that the CA3 cells with their extensive recurrent collateral system connecting different CA3 cells, and associative synaptic modifiability, form an autoassociation or attractor network, the spatial view cells with their approximately Gaussian view fields become linked in a continuous attractor network. As the view space is traversed continuously (e.g., by self-motion or imagined self-motion across the scene), the views are therefore successively recalled in the correct order, with no view missing, and with low interference between the items to be recalled. Given that each spatial view has been associated with a different discrete item, the items are recalled in the correct order, with none missing. This is the first neuroscience theory of ars memoriae. The theory provides a foundation for understanding how a key feature of ars memoriae, the ability to use a spatial scene to encode a sequence of items to be remembered, is implemented.
Development of IoT based smart security and monitoring devices for agriculture

Agriculture sector being the backbone of the Indian economy deserves security. Security not in terms of resources only but also agricultural products needs security and protection at very initial stage, like protection from attacks of rodents or insects, in fields or grain stores. Such challenges should also be taken into consideration. Security systems which are being used now a days are not smart enough to provide real time notification after sensing the problem. The integration of traditional methodology with latest technologies as Internet of Things and Wireless Sensor Networks can lead to agricultural modernization. Keeping this scenario in our mind we have designed, tested and analyzed an 'Internet of Things' based device which is capable of analyzing the sensed information and then transmitting it to the user. This device can be controlled and monitored from remote location and it can be implemented in agricultural fields, grain stores and cold stores for security purpose. This paper is oriented to accentuate the methods to solve such problems like identification of rodents, threats to crops and delivering real time notification based on information analysis and processing without human intervention. In this device, mentioned sensors and electronic devices are integrated using Python scripts. Based on attempted test cases, we were able to achieve success in 84.8% test cases.
On the Scientific Relevance of eSports

Competitive computer gaming or eSports is a phenomenon that has become a fundamental element in today’s digital youth culture. So far very little effort has been made to study eSports in particular with respect to its potentials to positively influence research developments in other areas. This paper therefore tries to lay a foundation for a proper academic treatment of eSports. It presents a short overview on the history of eSports, provides a definition that is suitable for academic studies on eSports related issues and discusses first approaches to this topic that might lead to results that are applicable to problems in seemingly unrelated fields such as strategic decision making or management training.
Ripple Joins for Online Aggregation

We present a new family of join algorithms, called ripple joins, for online processing of multi-table aggregation queries in a relational database management system (DBMS). Such queries arise naturally in interactive exploratory decision-support applications.
Traditional offline join algorithms are designed to minimize the time to completion of the query. In contrast, ripple joins are designed to minimize the time until an acceptably precise estimate of the query result is available, as measured by the length of a confidence interval. Ripple joins are adaptive, adjusting their behavior during processing in accordance with the statistical properties of the data. Ripple joins also permit the user to dynamically trade off the two key performance factors of on-line aggregation: the time between successive updates of the running aggregate, and the amount by which the confidence-interval length decreases at each update. We show how ripple joins can be implemented in an existing DBMS using iterators, and we give an overview of the methods used to compute confidence intervals and to adaptively optimize the ripple join “aspect-ratio” parameters. In experiments with an initial implementation of our algorithms in the POSTGRES DBMS, the time required to produce reasonably precise online estimates was up to two orders of magnitude smaller than the time required for the best offline join algorithms to produce exact answers.