nils-herrmann/clean_scirepeval huggingface dataset:
Computational Features of the Thinking and the Thinking Attributes of Computing: On Computational Thinking

The paper aims at revealing the essence and connotation of Computational Thinking. It analyzed some of the international academia’s research results of Computational Thinking. The author thinks Computational Thinking is discipline thinking or computing philosophy, and it is very critical to understand Computational Thinking to grasp the thinking’ s computational features and the computing’s thinking attributes. He presents the basic rules of screening the representative terms of Computational Thinking and lists some representative terms based on the rules. He thinks Computational Thinking is contained in the commonalities of those terms. The typical thoughts of Computational Thinking are structuralization, formalization, association-and-interaction, optimization and reuse-and-sharing. Training Computational Thinking must base on the representative terms and the typical thoughts. There are three innovations in the paper: the five rules of screening the representative terms, the five typical thoughts and the formalized description of Computational Thinking.
Extracting meronyms for a biology knowledge base using distant supervision

Knowledge of objects and their parts, meronym relations, are at the heart of many question-answering systems, but manually encoding these facts is impractical. Past researchers have tried hand-written patterns, supervised learning, and bootstrapped methods, but achieving both high precision and recall has proven elusive. This paper reports on a thorough exploration of distant supervision to learn a meronym extractor for the domain of college biology. We introduce a novel algorithm, generalizing the ``at least one'' assumption of multi-instance learning to handle the case where a fixed (but unknown) percentage of bag members are positive examples. Detailed experiments compare strategies for mention detection, negative example generation, leveraging out-of-domain meronyms, and evaluate the benefit of our multi-instance percentage model.
EbbRT: A Framework for Building Per-Application Library Operating Systems

Efficient use of high speed hardware requires operating system components be customized to the application workload. Our general purpose operating systems are ill-suited for this task. We present EbbRT, a framework for constructing per-application library operating systems for cloud applications. The primary objective of EbbRT is to enable highperformance in a tractable and maintainable fashion. This paper describes the design and implementation of EbbRT, and evaluates its ability to improve the performance of common cloud applications. The evaluation of the EbbRT prototype demonstrates memcached, run within a VM, can outperform memcached run on an unvirtualized Linux. The prototype evaluation also demonstrates an 14% performance improvement of a V8 JavaScript engine benchmark, and a node.js webserver that achieves a 50% reduction in 99th percentile latency compared to it run on Linux.
Applying deep learning approaches for network traffic prediction

Network traffic prediction aims at predicting the subsequent network traffic by using the previous network traffic data. This can serve as a proactive approach for network management and planning tasks. The family of recurrent neural network (RNN) approaches is known for time series data modeling which aims to predict the future time series based on the past information with long time lags of unrevealed size. RNN contains different network architectures like simple RNN, long short term memory (LSTM), gated recurrent unit (GRU), identity recurrent unit (IRNN) which is capable to learn the temporal patterns and long range dependencies in large sequences of arbitrary length. To leverage the efficacy of RNN approaches towards traffic matrix estimation in large networks, we use various RNN networks. The performance of various RNN networks is evaluated on the real data from GÉANT backbone networks. To identify the optimal network parameters and network structure of RNN, various experiments are done. All experiments are run up to 200 epochs with learning rate in the range [0.01-0.5]. LSTM has performed well in comparison to the other RNN and classical methods. Moreover, the performance of various RNN methods is comparable to LSTM.
Fingerprinting the datacenter: automated classification of performance crises

Contemporary datacenters comprise hundreds or thousands of machines running applications requiring high availability and responsiveness. Although a performance crisis is easily detected by monitoring key end-to-end performance indicators (KPIs) such as response latency or request throughput, the variety of conditions that can lead to KPI degradation makes it difficult to select appropriate recovery actions. We propose and evaluate a methodology for automatic classification and identification of crises, and in particular for detecting whether a given crisis has been seen before, so that a known solution may be immediately applied. Our approach is based on a new and efficient representation of the datacenter's state called a fingerprint, constructed by statistical selection and summarization of the hundreds of performance metrics typically collected on such systems. Our evaluation uses 4 months of trouble-ticket data from a production datacenter with hundreds of machines running a 24x7 enterprise-class user-facing application. In experiments in a realistic and rigorous operational setting, our approach provides operators the information necessary to initiate recovery actions with 80% correctness in an average of 10 minutes, which is 50 minutes earlier than the deadline provided to us by the operators. To the best of our knowledge this is the first rigorous evaluation of any such approach on a large-scale production installation.
CAP: community activity prediction based on big data analysis

Crowd sensing harnesses the power of the crowd by mobilizing a large number of users carrying various mobile and networked devices to collect data with the intrinsic multi-modal and large-volume features. With traditional methods, it is highly challenging to analyze the vast data volume generated by crowd sensing. In the era of big data, although several individual-oriented approaches are proposed to analyze human behavior based on big data, the common features of individual activity have not been fully investigated. In this article, we design a novel community- centric framework for community activity prediction based on big data analysis. Specifically, we propose an approach to extract community activity patterns by analyzing the big data collected from both the physical world and virtual social space. The proposed approach consists of community detection based on singular value decomposition and clustering, and community activity modeling based on tensors. The proposed approach is evaluated with a case study where a real dataset collected over a 15-month period is analyzed.
Effective Capacitive Power Transfer

Capacitive power transfer (CPT) systems have up to date been used for very low power delivery due to a number of limitations. A fundamental treatment of the problem is carried out and a CPT system is presented that achieves many times higher power throughput into low-impedance loads than traditional systems with the same interface capacitance and frequency of operation and with reasonable ratings for the switching devices. The development and analysis of the system is based well on the parameters of the capacitive interface and a design procedure is provided. The validity of the concept has been verified by an experimental CPT system that delivered more than 25W through a combined interface capacitance of 100 pF, at an operating frequency of only 1 MHz, with efficiency exceeding 80%.
Efficient Learning of Domain-invariant Image Representations

We present an algorithm that learns representations which explicitly compensate for domain mismatch and which can be efficiently realized as linear classifiers. Specifically, we form a linear transformation that maps features from the target (test) domain to the source (training) domain as part of training the classifier. We optimize both the transformation and classifier parameters jointly, and introduce an efficient cost function based on misclassification loss. Our method combines several features previously unavailable in a single algorithm: multi-class adaptation through representation learning, ability to map across heterogeneous feature spaces, and scalability to large datasets. We present experiments on several image datasets that demonstrate improved accuracy and computational advantages compared to previous approaches.
Detecting bad smells in source code using change history information

Code smells represent symptoms of poor implementation choices. Previous studies found that these smells make source code more difficult to maintain, possibly also increasing its fault-proneness. There are several approaches that identify smells based on code analysis techniques. However, we observe that many code smells are intrinsically characterized by how code elements change over time. Thus, relying solely on structural information may not be sufficient to detect all the smells accurately. We propose an approach to detect five different code smells, namely Divergent Change, Shotgun Surgery, Parallel Inheritance, Blob, and Feature Envy, by exploiting change history information mined from versioning systems. We applied approach, coined as HIST (Historical Information for Smell deTection), to eight software projects written in Java, and wherever possible compared with existing state-of-the-art smell detectors based on source code analysis. The results indicate that HIST's precision ranges between 61% and 80%, and its recall ranges between 61% and 100%. More importantly, the results confirm that HIST is able to identify code smells that cannot be identified through approaches solely based on code analysis.
BabelDomains: Large-Scale Domain Labeling of Lexical Resources

In this paper we present BabelDomains, a unified resource which provides lexical items with information about domains of knowledge. We propose an automatic method that uses knowledge from various lexical resources, exploiting both distributional and graph-based clues, to accurately propagate domain information. We evaluate our methodology intrinsically on two lexical resources (WordNet and BabelNet), achieving a precision over 80% in both cases. Finally, we show the potential of BabelDomains in a supervised learning setting, clustering training data by domain for hypernym discovery.
Let's play!: mobile health games for adults

Researchers have designed a variety of systems that promote wellness. However, little work has been done to examine how casual mobile games can help adults learn how to live healthfully. To explore this design space, we created OrderUP!, a game in which players learn how to make healthier meal choices. Through our field study, we found that playing OrderUP! helped participants engage in four processes of change identified by a well-established health behavior theory, the Transtheoretical Model: they improved their understanding of how to eat healthfully and engaged in nutrition-related analytical thinking, reevaluated the healthiness of their real life habits, formed helping relationships by discussing nutrition with others and started replacing unhealthy meals with more nutritious foods. Our research shows the promise of using casual mobile games to encourage adults to live healthier lifestyles.
How to Fit when No One Size Fits

While “no one size fits all” is a sound philosophy for system designers to follow, it poses multiple challenges for application developers and system administrators. It can be hard for an application developer to pick one system when the needs of her application match the features of multiple “one size” systems. The choice becomes considerably harder when different components of an application fit the features of different “one size” systems. Considerable manual effort goes into creating and tuning such multi-system applications. An application’s data and workload properties may change over time, often in unpredictable and bursty ways. Consequently, the “one size” system that is best for an application can change over time. Adapting to change can be hard when application development is coupled tightly with any individual “one size” system. In this paper, we make the case for developing a new breed of Database Management Systems that we term DBMS. A DBMS contains multiple “one size” systems internally. An application specifies its execution requirements on aspects like performance, availability, consistency, change, and cost to the DBMS declaratively. For all requests (e.g., queries) made by the application, the DBMS will select the execution plan that meets the application’s requirements best. A unique aspect of the execution plan in a DBMS is that the plan includes the selection of one or more “one size” systems. The plan is then deployed and managed automatically on the selected system(s). If application requirements change beyond what was planned for originally by the DBMS, then the application can be reoptimized and redeployed; usually with no additional effort required from the application developer. The DBMS approach has the potential to address the challenges that application developers and system administrators face from the vast and growing number of “one size” systems today. However, this approach poses many research challenges that we discuss in this paper. We are taking the DBMS approach in a platform, called Cyclops, that we are building for continuous query execution. We will use Cyclops throughout the paper to give concrete illustrations of the benefits and challenges of the DBMS approach.  2013, Asilomar, California, USA.
Hairpin microstrip bandpass filter for millimeter-wave applications

A compact microstrip bandpass filter is proposed in this paper for millimeter-wave applications. The filter consists of three new non-uniform coupled-line modified hairpin resonators. The filter is designed to exhibit a fractional bandwidth of about 5.0 % at a center of frequency of approximately 34 GHz. The shapes of the hairpin resonators are modified to suppress the unwanted spurious harmonic response. As a result, the filter exhibits very wide stopband with a rejection level better than 10-dB. In addition, the filter shows a transmission zero close to upper edge of the desired passband which enhances the selectivity of the passband. The filter design is successfully realized on a RT/Duroid 6002 with a dielectric constant of 2.94 and a thickness of 127μm. The filter is simulated, and fabricated to demonstrate the proposed technique where excellent agreement is obtained.
Markerless Vision-Based Augmented Reality for Urban Planning

Augmented Reality (AR) is a rapidly developing field with numerous potential applications. For example, building developers, public authorities and other construction industry stakeholders need to visually assess potential new developments with regard to aesthetics, health & safety, and other criteria. Current state-of-the-art visualization technologies are mainly fully virtual, while AR has the potential to enhance those visualizations by observing proposed designs directly within the real
Real-time pixel luminance optimization for dynamic multi-projection mapping

Using projection mapping enables us to bring virtual worlds into shared physical spaces. In this paper, we present a novel, adaptable and real-time projection mapping system, which supports multiple projectors and high quality rendering of dynamic content on surfaces of complex geometrical shape. Our system allows for smooth blending across multiple projectors using a new optimization framework that simulates the diffuse direct light transport of the physical world to continuously adapt the color output of each projector pixel. We present a real-time solution to this optimization problem using off-the-shelf graphics hardware, depth cameras and projectors. Our approach enables us to move projectors, depth camera or objects while maintaining the correct illumination, in realtime, without the need for markers on the object. It also allows for projectors to be removed or dynamically added, and provides compelling results with only commodity hardware.
A case study of trajectory transfer through non-rigid registration for a simplified suturing scenario

Suturing is an important yet time-consuming part of surgery. A fast and robust autonomous procedure could reduce surgeon fatigue, and shorten operation times. It could also be of particular importance for suturing in remote tele-surgery settings where latency can complicate the master-slave mode control that is the current practice for robotic surgery with systems like the da Vinci®. We study the applicability of the trajectory transfer algorithm proposed in  to the automation of suturing. The core idea of this procedure is to first use non-rigid registration to find a 3D warping function which maps the demonstration scene onto the test scene, then use this warping function to transform the robot end-effector trajectory. Finally a robot joint trajectory is generated by solving a trajectory optimization problem that attempts to find the closest feasible trajectory, accounting for external constraints, such as joint limits and obstacles. Our experiments investigate generalization from a single demonstration to differing initial conditions. A first set of experiments considers the problem of having a simulated Raven II system  suture two flaps of tissue together. A second set of experiments considers a PR2 robot performing sutures in a scaled-up experimental setup. The simulation experiments were fully autonomous. For the real-world experiments we provided human input to assist with the detection of landmarks to be fed into the registration algorithm. The success rate for learning from a single demonstration is high for moderate perturbations from the demonstration's initial conditions, and it gradually decreases for larger perturbations.
GPU Versus FPGA for High Productivity Computing

Heterogeneous or co-processor architectures are becoming an important component of high productivity computing systems (HPCS). In this work the performance of a GPU based HPCS is compared with the performance of a commercially available FPGA based HPC. Contrary to previous approaches that focussed on specific examples, a broader analysis is performed by considering processes at an architectural level. A set of benchmarks is employed that use different process architectures in order to exploit the benefits of each technology. These include the asynchronous pipelines common to "map" tasks, a partially synchronous tree common to "reduce" tasks and a fully synchronous, fully connected mesh. We show that the GPU is more productive than the FPGA architecture for most of the benchmarks and conclude that FPGA-based HPCS is being marginalised by GPUs.
Social power and information technology implementation: a contentious framing lens

Research on the organizational implementation of information technology (IT) and social power has favoured explanations based on issues of resource power and process power at the expense of matters of meaning power. As a result, although the existence and importance of meaning power is acknowledged, its distinctive practices and enacted outcomes remain relatively under-theorized and under-explored by IT researchers. This paper focused on unpacking the practices and outcomes associated with the exercise of meaning power within the IT implementation process. Our aim was to analyze the practices employed to construct meaning and enact a collective ‘definition of the situation’. We focused on framing and utilizing the signature matrix technique to represent and analyze the exercise of meaning power in practice. The paper developed and illustrated this conceptual framework using a case study of a conflictual IT implementation in a challenging public sector environment. We concluded by pointing out the situated nature of meaning power practices and the enacted outcomes. Our research extends the literature on IT and social power by offering an analytical framework distinctly suited to the analysis and deeper understanding of the meaning power properties.
Information Dropout: learning optimal representations through noise

We introduce Information Dropout, a generalization of dropout that is motivated by the Information Bottleneck principle and highlights the way in which injecting noise in the activations can help in learning optimal representations of the data. Information Dropout is rooted in information theoretic principles, it includes as special cases several existing dropout methods, like Gaussian Dropout and Variational Dropout, and, unlike classical dropout, it can learn and build representations that are invariant to nuisances of the data, like occlusions and clutter. When the task is the reconstruction of the input, we show that the information dropout method yields a variational autoencoder as a special case, thus providing a link between representation learning, information theory and variational inference. Our experiments validate the theoretical intuitions behind our method, and we find that information dropout achieves a comparable or better generalization performance than binary dropout, especially on smaller models, since it can automatically adapt the noise to the structure of the network, as well as to the test sample.
A sparse texture representation using local affine regions

This paper introduces a texture representation suitable for recognizing images of textured surfaces under a wide range of transformations, including viewpoint changes and nonrigid deformations. At the feature extraction stage, a sparse set of affine Harris and Laplacian regions is found in the image. Each of these regions can be thought of as a texture element having a characteristic elliptic shape and a distinctive appearance pattern. This pattern is captured in an affine-invariant fashion via a process of shape normalization followed by the computation of two novel descriptors, the spin image and the RIFT descriptor. When affine invariance is not required, the original elliptical shape serves as an additional discriminative feature for texture recognition. The proposed approach is evaluated in retrieval and classification tasks using the entire Brodatz database and a publicly available collection of 1,000 photographs of textured surfaces taken from different viewpoints.
Free Riding in BitTorrent is Cheap

While it is well-known that BitTorrent is vulnerable to selfish behavior, this paper demonstrates that even entire files can be downloaded without reciprocating at all in BitTorrent. To this end, we present BitThief, a free riding client that never contributes any real data. First, we show that simple tricks suffice in order to achieve high download rates, even in the absence of seeders. We also illustrate how peers in a swarm react to various sophisticated attacks. Moreover, our analysis reveals that sharing communities—communities originally intended to offer downloads of good quality and to promote cooperation among peers—provide many incentives to cheat.