nils-herrmann/clean_scirepeval huggingface dataset:
Performance-oriented DevOps: A Research Agenda

DevOps is a trend towards a tighter integration between development (Dev) and operations (Ops) teams. The need for such an integration is driven by the requirement to continuously adapt enterprise applications (EAs) to changes in the business environment. As of today, DevOps concepts have been primarily introduced to ensure a constant flow of features and bug fixes into new releases from a functional perspective. In order to integrate a non-functional perspective into these DevOps concepts this report focuses on tools, activities, and processes to ensure one of the most important quality attributes of a software system, namely performance. Performance describes system properties concerning its timeliness and use of resources. Common metrics are response time, throughput, and resource utilization. Performance goals for EAs are typically defined by setting upper and/or lower bounds for these metrics and specific business transactions. In order to ensure that such performance goals can be met, several activities are required during development and operation of these systems as well as during the transition from Dev to Ops. Activities during development are typically summarized by the term Software Performance Engineering (SPE), whereas activities during operations are called Application Performance Management (APM). SPE and APM were historically tackled independently from each other, but the newly emerging DevOps concepts require and enable a tighter integration between both activity streams. This report presents existing solutions to support this integration as well as open research challenges in this area. The report starts by defining EAs and summarizes their characteristics that make performance evaluations for these systems particularly challenging. It continues by describing our understanding of DevOps and explaining the roots of this trend to set the context for the remaining parts of the report. Afterwards, performance management activities that are common in both life cycle phases are explained, until the particularities of SPE and APM are discussed in separate sections. Finally, the report concludes by outlining activities and challenges to support the rapid iteration between Dev and Ops.
Evolution for automatic assessment of the difficulty of sokoban boards

Many games have a collection of boards with the difficulty of an instance of the game determined by the starting configuration of the board. Correctly rating the difficulty of the boards is somewhat haphazard and required either a remarkable level of understanding of the game or a good deal of play-testing. In this study we explore evolutionary algorithms as a tool to automatically grade the difficulty of boards for a version of the game sokoban. Mean time-to-solution by an evolutionary algorithm and number of failures to solve a board are used as a surrogate for the difficulty of a board. Initial testing with a simple string-based representation, giving a sequence of moves for the sokoban agent, provided very little signal; it usually failed. Two other representations, based on a reactive linear genetic programming structure called an ISAc list, generated useful hardness-classification information for both hardness surrogates. These two representations differ in that one uses a randomly initialized population of ISAc lists while the other initializes populations with competent agents pre-trained on random collections of sokoban boards. The study encompasses four hardness surrogates: probability-of-failure and mean time-to-solution for each of these two representations. All four are found to generate similar information about board hardness, but probability-of-failure with pre-evolved agents is found to be faster to compute and to have a clearer meaning than the other three board-hardness surrogates.
The discontinuous conduction mode Sepic and Cuk power factor preregulators: analysis and design

Sepic and Ćuk converters working as power factor preregulators (PFP) in discontinuous conduction mode (DCM) present the following desirable characteristics for a PFP: 1) the converter works as a voltage follower (no current loop is needed); 2) theoretical power factor is unity; and 3) the input current ripple is defined at the design stage. Besides, input-output galvanic isolation is easily obtained. This paper analyzes the operation of both converters as DCM–PFP. Design equations are derived, as well as a small-signal model to aid the control loop design. Both simulation and experimental results are presented that are in agreement with the theoretical analysis and complement the work.
Improving your data transformations : Applying the Box-Cox transformation

Many of us in the social sciences deal with data that do not conform to assumptions of normality and/or homoscedasticity/homogeneity of variance. Some research has shown that parametric tests (e.g., multiple regression, ANOVA) can be robust to modest violations of these assumptions. Yet the reality is that almost all analyses (even nonparametric tests) benefit from improved the normality of variables, particularly where substantial non-normality is present. While many are familiar with select traditional transformations (e.g., square root, log, inverse) for improving normality, the Box-Cox transformation (Box & Cox, 1964) represents a family of power transformations that incorporates and extends the traditional options to help researchers easily find the optimal normalizing transformation for each variable. As such, Box-Cox represents a potential best practice where normalizing data or equalizing variance is desired. This paper briefly presents an overview of traditional normalizing transformations and how Box-Cox incorporates, extends, and improves on these traditional approaches to normalizing data. Examples of applications are presented, and details of how to automate and use this technique in SPSS and SAS are included.
Exploring Session Context using Distributed Representations of Queries and Reformulations

Search logs contain examples of frequently occurring patterns of user reformulations of queries. Intuitively, the reformulation "San Francisco" -- "San Francisco 49ers" is semantically similar to "Detroit" -- "Detroit Lions". Likewise, "London" -- "things to do in London" and "New York" -- "New York tourist attractions" can also be considered similar transitions in intent. The reformulation "movies" -- "new movies" and "york" -- "New York", however, are clearly different despite the lexical similarities in the two reformulations. In this paper, we study the distributed representation of queries learnt by deep neural network models, such as the Convolutional Latent Semantic Model, and show that they can be used to represent query reformulations as vectors. These reformulation vectors exhibit favourable properties such as mapping semantically and syntactically similar query changes closer in the embedding space. Our work is motivated by the success of continuous space language models in capturing relationships between words and their meanings using offset vectors. We demonstrate a way to extend the same intuition to represent query reformulations. Furthermore, we show that the distributed representations of queries and reformulations are both useful for modelling session context for query prediction tasks, such as for query auto-completion (QAC) ranking. Our empirical study demonstrates that short-term (session) history context features based on these two representations improves the mean reciprocal rank (MRR) for the QAC ranking task by more than 10% over a supervised ranker baseline. Our results also show that by using features based on both these representations together we achieve a better performance, than either of them individually.
A Probabilistic Approach to WLAN User Location Estimation

We estimate the location of a WLAN user based on radio signal strength measurements performed by the user’s mobile terminal. In our approach the physical properties of the signal propagation are not taken into account directly. Instead the location estimation is regarded as a machine learning problem in which the task is to model how the signal strengths are distributed in different geographical areas based on a sample of measurements collected at several known locations. We present a probabilistic framework for solving the location estimation problem. In the empirical part of the paper we demonstrate the feasibility of this approach by reporting results of field tests in which a probabilistic location estimation method is validated in a real-world indoor environment.
Embedding Information Visualization within Visual Representation

Despite its often technical nature, visualization is in many ways a form of visual representation. Just how visualization relates to illustration, information graphics, digital art, visual languages, etc., is nonetheless poorly understood. We propose a theory that embeds information visualization within other visual traditions in terms of criteria that are not purely technical: dependence on data, mapping, interactivity, and notationality. In addition to providing the means for a classification, these criteria also foster a different understanding of information visualization. We further adapt our criteria to differentiate within visualization, using mapping, readability and information loss, and notationality as the criteria. Both sets of criteria are demonstrated in a number of case studies. We believe that our novel taxonomies of visualization methods serve as a step towards a more comprehensive theoretical context to understanding the essential purposes, properties, and functions of information visualization.
NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study

This paper introduces a novel large dataset for example-based single image super-resolution and studies the state-of-the-art as emerged from the NTIRE 2017 challenge. The challenge is the first challenge of its kind, with 6 competitions, hundreds of participants and tens of proposed solutions. Our newly collected DIVerse 2K resolution image dataset (DIV2K) was employed by the challenge. In our study we compare the solutions from the challenge to a set of representative methods from the literature and evaluate them using diverse measures on our proposed DIV2K dataset. Moreover, we conduct a number of experiments and draw conclusions on several topics of interest. We conclude that the NTIRE 2017 challenge pushes the state-of-the-art in single-image super-resolution, reaching the best results to date on the popular Set5, Set14, B100, Urban100 datasets and on our newly proposed DIV2K.
Application Characteristics of Permanent Magnet Synchronous and Brushless dc Motors for Servo Drives

The permanent magnet synchronous motor (PMSM) and the brushless dc motor (BDCM) have many similarities; they both have permanent magnets on the rotor and require alternating stator currents to produce constant torque. The difference in these two machines is that the PMSM and the BDCM has sinusoidal and trapezoidal back emfs, respectively. This means these two machines have different operating characteristics and control requirements. For application considerations, these two motor drives have to be differentiated on the basis of known engineering criteria. Some of the criteria used to assess these two machines include power density, torque per unit current, speed range, feedback devices, inverter rating, cogging torque, ripple torque, and parameter sensitivity. Guidelines for the appropriate machine to be used for a given application are given based on the results of the criteria listed above.
User-friendly 3D object manipulation gesture using kinect

With the rapid development and wide spread of the virtual reality technology, we can easily find VR systems in various places such as school, library and home. Translation and rotation, the most frequently used gestures for manipulating objects in the real world, need to be implemented in order to make a user feel comfortable while manipulating 3D objects in the systems. In this paper, we propose a set of user-friendly 3D object manipulation gestures and develop a recognizer using Kinect for the VR systems. The usefulness and suitableness of the proposed system is shown from the user study performed.
Deep Generative Model using Unregularized Score for Anomaly Detection with Heterogeneous Complexity

Accurate and automated detection of anomalous samples in a natural image dataset can be accomplished with a probabilistic model for end-to-end modeling of images. Such images have heterogeneous complexity, however, and a probabilistic model overlooks simply shaped objects with small anomalies. This is because the probabilistic model assigns undesirably lower likelihoods to complexly shaped objects that are nevertheless consistent with set standards. To overcome this difficulty, we propose an unregularized score for deep generative models (DGMs), which are generative models leveraging deep neural networks. We found that the regularization terms of the DGMs considerably influence the anomaly score depending on the complexity of the samples. By removing these terms, we obtain an unregularized score, which we evaluated on a toy dataset and real-world manufacturing datasets. Empirical results demonstrate that the unregularized score is robust to the inherent complexity of samples and can be used to better detect anomalies.
Lava flow Simulation with Cellular Automata: Applications for Civil Defense and Land Use Planning

The determination of areas exposed to new eruptive events in volcanic regions is crucial for diminishing consequences in terms of human causalities and damages of material properties. In this paper, we illustrate a methodology for defining flexible high-detailed lava invasion hazard maps which is based on an robust and efficient Cellular Automata model for simulating lava flows. We also present some applications for land use planning and civil defense to some inhabited areas of Mt Etna (South Italy), Europe’s most active volcano, showing the methodology’s appropriateness.
Cross-cultural adaptation of health-related quality of life measures: literature review and proposed guidelines.

Clinicians and researchers without a suitable health-related quality of life (HRQOL) measure in their own language have two choices: (1) to develop a new measure, or (2) to modify a measure previously validated in another language, known as a cross-cultural adaptation process. We propose a set of standardized guidelines for this process based on previous research in psychology and sociology and on published methodological frameworks. These guidelines include recommendations for obtaining semantic, idiomatic, experiential and conceptual equivalence in translation by using back-translation techniques and committee review, pre-testing techniques and re-examining the weight of scores. We applied these guidelines to 17 cross-cultural adaptation of HRQOL measures identified through a comprehensive literature review. The reporting standards varied across studies but agreement between raters in their ratings of the studies was substantial to almost perfect (weighted kappa = 0.66-0.93) suggesting that the guidelines are easy to apply. Further research is necessary in order to delineate essential versus optional steps in the adaptation process.
Clockwork Convnets for Video Semantic Segmentation

Recent years have seen tremendous progress in still-image segmentation; however the naı̈ve application of these state-of-the-art algorithms to every video frame requires considerable computation and ignores the temporal continuity inherent in video. We propose a video recognition framework that relies on two key observations: 1) while pixels may change rapidly from frame to frame, the semantic content of a scene evolves more slowly, and 2) execution can be viewed as an aspect of architecture, yielding purpose-fit computation schedules for networks. We define a novel family of “clockwork” convnets driven by fixed or adaptive clock signals that schedule the processing of different layers at different update rates according to their semantic stability. We design a pipeline schedule to reduce latency for real-time recognition and a fixed-rate schedule to reduce overall computation. Finally, we extend clockwork scheduling to adaptive video processing by incorporating data-driven clocks that can be tuned on unlabeled video. The accuracy and efficiency of clockwork convnets are evaluated on the Youtube-Objects, NYUD, and Cityscapes video datasets.
Arithmetic circuit design with memristor based high fan-out logic gates

The application of memristors in logic operations have recently gained importance and are being investigated. The work presented in this paper describes a new low power technique of using memristors to perform arithmetic operations like addition and multiplication. It focuses on reducing the hardware costs and associated design complexity. The idea of “logic implication” is combined with an analog technique of using current mirrors to perform logic operations. By using current mirrors, the problem of limited fan-out of logic implication can be overcome, enabling more parallel operations to run together. The delay for even an XOR operation can be reduced to 1 cycle, compared to the 5 cycles taken by logic implication. The design of a full adder, ripple carry adder and array multiplier are described. Metrics of area and latency are compared to previous works.
Bankshot: caching slow storage in fast non-volatile memory

Emerging non-volatile storage (e.g., Phase Change Memory, STT-RAM) allow access to persistent data at latencies an order of magnitude lower than SSDs. The density and price gap between NVMs and denser storage make NVM economically most suitable as a cache for larger, more conventional storage (i.e., NAND flash-based SSDs and disks). Existing storage caching architectures (even those that use fast flash-based SSDs) introduce significant software overhead that can obscure the performance benefits of faster memories. We propose Bankshot, a caching architecture that allows cache hits to bypass the OS (and the associated software overheads) entirely, while relying on the OS for heavy-weight operations like servicing misses and performing write backs. We evaluate several design decisions in Bankshot including different cache management policies and different levels of hardware, software support for tracking dirty data and maintaining meta-data. We find that with hardware support Bankshot can offer upto 5× speedup over conventional caching systems.
Status of market, regulation and research of genetically modified crops in Chile.

Agricultural biotechnology and genetically modified (GM) crops are effective tools to substantially increase productivity, quality, and environmental sustainability in agricultural farming. Furthermore, they may contribute to improving the nutritional content of crops, addressing needs related to public health. Chile has become one of the most important global players for GM seed production for counter-season markets and research purposes. It has a comprehensive regulatory framework to carry out this activity, while at the same time there are numerous regulations from different agencies addressing several aspects related to GM crops. Despite imports of GM food/feed or ingredients for the food industry being allowed without restrictions, Chilean farmers are not using GM seeds for farming purposes because of a lack of clear guidelines. Chile is in a rather contradictory situation about GM crops. The country has invested considerable resources to fund research and development on GM crops, but the lack of clarity in the current regulatory situation precludes the use of such research to develop new products for Chilean farmers. Meanwhile, a larger scientific capacity regarding GM crop research continues to build up in the country. The present study maps and analyses the current regulatory environment for research and production of GM crops in Chile, providing an updated overview of the current status of GM seeds production, research and regulatory issues.
An Overview of Sustainable Green 5G Networks

The stringent requirements of a 1000x increase in data traffic and 1 ms round-trip latency have made limiting the potentially tremendous ensuing energy consumption one of the most challenging problems for the design of the upcoming 5G networks. To enable sustainable 5G networks, new technologies have been proposed to improve the system energy efficiency, and alternative energy sources are introduced to reduce our dependence on traditional fossil fuels. In particular, various 5G techniques target the reduction of the energy consumption without sacrificing the quality of service. Meanwhile, energy harvesting technologies, which enable communication transceivers to harvest energy from various renewable resources and ambient radio frequency signals for communication, have drawn significant interest from both academia and industry. In this article, we provide an overview of the latest research on both green 5G techniques and energy harvesting for communication. In addition, some technical challenges and potential research topics for realizing sustainable green 5G networks are also identified.
Technology adoption: A conjoint analysis of consumers' preference on future online banking services

The importance of service delivery technology and online service adoption and usage in the banking industry has received an increased discussion in the literature in recent years. Owing to the fact that Strong online banking services are important drivers for bank performance and customer service delivery; several studies have been carried out on online banking service adoption or acceptance where services are already deployed and on the factors that influence customers' adoption and use or intention to use those services. However, despite the increasing discussion in the literatures, no attempt has been made to look at consumers' preference in terms of future online banking service adoption. This study used conjoint analysis and stated preference methods with discrete choice model to analyze the technology adoption pattern regarding consumers' preference for potential future online banking services in the Nigerian banking industry. The result revealed that to increase efficiency and strengthen competitiveness, banks need to promote smart and practical branded services especially self-services at the same time promote a universal adoption of e-banking system services that add entertainment or extra convenience to customers such as ease of usage including digital wallet, real-time interaction (video banking), ATMs integrated with smart phones, website customization, biometric services, and digital currency. These services can contribute to an increasing adoption of online services. & 2015