nils-herrmann/clean_scirepeval huggingface dataset:
Troxy: Transparent Access to Byzantine Fault-Tolerant Systems

Various protocols and architectures have been proposed to make Byzantine fault tolerance (BFT) increasingly practical. However, the deployment of such systems requires dedicated client-side functionality. This is necessary as clients have to connect to multiple replicas and perform majority voting over the received replies to outvote faulty responses. Deploying custom client-side code is cumbersome, and often not an option, especially in open heterogeneous systems and for well-established protocols (e.g., HTTP and IMAP) where diverse client-side implementations co-exist. We propose Troxy, a system which relocates the BFT-specific client-side functionality to the server side, thereby making BFT transparent to legacy clients. To achieve this, Troxy relies on a trusted subsystem built upon hardware protection enabled by Intel SGX. Additionally, Troxy reduces the replication cost of BFT for read-heavy workloads by offering an actively maintained cache that supports trustworthy read operations while preserving the consistency guarantees offered by the underlying BFT protocol. A prototype of Troxy has been built and evaluated, and results indicate that using Troxy (1) leads to at most 43% performance loss with small ordered messages in a local network environment, while (2) improves throughput by 130% with read-heavy workloads in a simulated wide-area network.
Deep generative models: Survey

Generative models have found their way to the forefront of deep learning the last decade and so far, it seems that the hype will not fade away any time soon. In this paper, we give an overview of the most important building blocks of most recent revolutionary deep generative models such as RBM, DBM, DBN, VAE and GAN. We will also take a look at three of state-of-the-art generative models, namely PixelRNN, DRAW and NADE. We will delve into their unique architectures, the learning procedures and their potential and limitations. We will also review some of the known issues that arise when trying to design and train deep generative architectures using shallow ones and how different models deal with these issues. This paper is not meant to be a comprehensive study of these models, but rather a starting point for those who bear an interest in the field.
A fast thumbnail extraction method in H.264/AVC video streams

In this paper, we propose a theoretical method for extracting a thumbnail directly from an H.264/AVC intra frame in the frequency domain. Furthermore, the proposed scheme can extract the thumbnail very fast since all operations are directly applied to transformed coefficients and a lookup table (LUT) for each intra mode is used for this scheme. Through performance evaluation, while the subjective quality difference between our scheme and a conventional method is negligible, the former can extract the thumbnail faster than the latter by up to 63%.
Snapshot Image Mapping Spectrometer (IMS) with high sampling density for hyperspectral microscopy

A snapshot Image Mapping Spectrometer (IMS) with high sampling density is developed for hyperspectral microscopy, measuring a datacube of dimensions 285 x 285 x 60 (x, y, lambda). The spatial resolution is approximately 0.45 microm with a FOV of 100 x 100 microm(2). The measured spectrum is from 450 nm to 650 nm and is sampled by 60 spectral channels with average sampling interval approximately 3.3 nm. The channel's spectral resolution is approximately 8nm. The spectral imaging results demonstrate the potential of the IMS for real-time cellular fluorescence imaging.
Learning Multi-level Features For Sensor-based Human Action Recognition

This paper proposes amulti-level feature learning framework for human action recognition using a single body-worn inertial sensor. The framework consists of three phases, respectively designed to analyze signal-based (low-level), components (mid-level) and semantic (high-level) information. Low-level features capture the time and frequency domain property while mid-level representations learn the composition of the action. The Maxmargin Latent Pattern Learning (MLPL) method is proposed to learn high-level semantic descriptions of latent action patterns as the output of our framework. The proposedmethod achieves the state-of-the-art performances, 88.7%, 98.8% and 72.6% (weighted F1 score) respectively, on Skoda, WISDM and OPP datasets.
FCM : THE FUZZY c-MEANS CLUSTERING ALGORITHM

nThis paper transmits a FORTRAN-IV coding of the fuzzy c-means (FCM) clustering program. The FCM program is applicable to a wide variety of geostatistical data analysis problems. This program generates fuzzy partitions and prototypes for any set of numerical data. These partitions are useful for corroborating known substructures or suggesting substructure in unexplored data. The clustering criterion used to aggregate subsets is a generalized least-squares objective function. Features of this program include a choice of three norms (Euclidean, Diagonal, or Mahalonobis), an adjustable weighting factor that essentially controls sensitivity to noise, acceptance of variable numbers of clusters, and outputs that include several measures of cluster validity.
A throwable miniature robotic system

Before the soldiers or police take actions, a fast and real-time detection in dangerous places, such as indoor, passageway and underpass, can both guarantee the soldiers' safety and increase the battle's accuracy, so it is significative and necessary to design a kind of scout robot which is able to arrive in the target region to acquire the message fast by the ways of thrown, shot or airdrop. This kind of robot should be small, easy to hide and have the ability of anti-impact and semi-autonomous. This paper mainly proposes a design method of the throwable miniature scout robot, analyses the anti-pact mechanism and autonomous strategy, and shows that the anti-impact mechanism is useful to attenuate the impact and the semi-autonomous control strategy can be fit for the robot's application through several experiments at last.
Sharpness Enhancement and Super-Resolution of Around-View Monitor Images

In the wide-angle (WA) images embedded in an around-view monitor system, the subject(s) in the peripheral region is normally small and has little information. Furthermore, since the outer region suffers from the non-uniform blur phenomenon and artifact caused by the inherent optical characteristic of WA lenses, its visual quality tends to deteriorate. According to our experiments, conventional image enhancement techniques rarely improve the degraded visual quality of the outer region of WA images. In order to solve the above-mentioned problem, this paper proposes a joint sharpness enhancement (SE) and super-resolution (SR) algorithm which can improve the sharpness and resolution of WA images together. The proposed SE algorithm improves the sharpness of the deteriorated WA images by exploiting self-similarity. Also, the proposed SR algorithm generates super-resolved images by using high-resolution information which is classified according to the extended local binary pattern-based classifier and learned on a pattern basis. Experimental results show that the proposed scheme effectively improves the sharpness and resolution of the input deteriorated WA images. Even in terms of quantitative metrics such as just noticeable blur, structural similarity, and peak signal-to-noise ratio. Finally, the proposed scheme guarantees real-time processing such that it achieves 720p video at 29 Hz on a low cost GPU platform.
Agents on Stage: Advancing the State of the Art of AI

Intelligent computer agents are both the original goal and the ultimate goal of artificial intelligence research. In striving toward that goal, our community has followed a practical research strategy of "divide-and-conquer," with different sub-communities attacking important component functions of intelligence, such as planning, search, knowledge representation, vision, and natural language. This strategy has been almost too successful, yielding both challenging theoretical problems that have come to dominate the inquiries of many researchers and a spate of practical techniques that have enabled other researchers to move into profitable commercial enterprises. While each of these pursuits is worthy in its own right, together they have had the unfortunate sideeffect of fragmenting the field and almost completely diverting it from efforts to build intelligent agents.
Neural correlates of heart rate variability during emotion

The vagal (high frequency [HF]) component of heart rate variability (HRV) predicts survival in post-myocardial infarction patients and is considered to reflect vagal antagonism of sympathetic influences. Previous studies of the neural correlates of vagal tone involved mental stress tasks that included cognitive and emotional elements. To differentiate the neural substrates of vagal tone due to emotion, we correlated HF-HRV with measures of regional cerebral blood flow (rCBF) derived from positron emission tomography (PET) and (15)O-water in 12 healthy women during different emotional states. Happiness, sadness, disgust and three neutral conditions were each induced by film clips and recall of personal experiences (12 conditions). Inter-beat intervals derived from electrocardiographic recordings during the 60-second scans were spectrally-analyzed, generating 12 separate measures of HF-HRV in each subject. The six emotion and six neutral conditions were grouped together and contrasted. We observed substantial overlap between emotion-specific rCBF and the correlation between emotion-specific rCBF and HF-HRV, particularly in the medial prefrontal cortex. Emotion-specific rCBF also correlated with HF-HRV in the caudate nucleus, periacqueductal gray and left mid-insula. We also observed that the elements of cognitive control inherent in this experiment (that involved focusing on the target mental state) had definable neural substrates that correlated with HF-HRV and to a large extent differed from the emotion-specific correlates of HF-HRV. No statistically significant asymmetries were observed. Our findings are consistent with the view that the medial visceromotor network is a final common pathway by which emotional and cognitive functions recruit autonomic support.
Application of Stereo Vision on Determination of End-Effector Position and Orientation of Manipulators

the main idea of this paper is to present application of stereo vision approach to determine position and orientation of end-effector of mechanical manipulators. Stereo vision is a technique based on inferring depth of an object from two cameras. A stereo vision test set up that is made of two parallel CMOS cameras is provided and USB port is used to transmit image data to the computer. Experimental tests are exerted for 6R mechanical manipulator where the end-effector moves on circular trajectory and Scout mobile robot to move a predefined optimal trajectory. The image processing algorithm, matching algorithm and triangulation calculations are optimized to decrease time required for matching procedure. The results illustrate that the measurement error is less than 1 mm. Because of rapid image processing algorithm; proposed method can be used to detect end-effector in dynamic trajectories. This stereo vision set up can be applied easily as measuring unit in a closed loop control system. Manipulators; End-Effector; Positioning; Orientation;
Dual-Band Microstrip Bandpass Filter Using Stepped-Impedance Resonators With New Coupling Schemes

A microstrip bandpass filter using stepped-impedance resonators is designed in low-temperature co-fired ceramic technology for dual-band applications at 2.4 and 5.2 GHz. New coupling schemes are proposed to replace the normal counterparts. It is found that the new coupling scheme for the interstages can enhance the layout compactness of the bandpass filter; while the new coupling scheme at the input and output can improve the performance of the bandpass filter. To validate the design and analysis, a prototype of the bandpass filter was fabricated and measured. It is shown that the measured and simulated performances are in good agreement. The prototype of the bandpass filter achieved insertion loss of 1.25 and 1.87 dB, S11 of -29 and -40 dB, and bandwidth of 21% and 12.7% at 2.4 and 5.2 GHz, respectively. The bandpass filter is further studied for a single-package solution of dual-band radio transceivers. The bandpass filter is, therefore, integrated into a ceramic ball grid array package. The integration is analyzed with an emphasis on the connection of the bandpass filter to the antenna and to the transceiver die
A robust fault modeling and prediction technique for heterogeneous network

This paper proposes a modelling technique for fault identification in a heterogeneous network. Heterogeneous network is the integration of wired network, cellular network, wireless local area network (WLAN), and Adhoc network, etc. There may be several numbers of causes and sub-causes of fault in each type of network. These causes of fault must be mitigated in order to have a secure and reliable system. The paper introduces a model based approach that starts with the enumeration of possible causes of each fault over the heterogeneous network. A framework is then created by such causes which diagrammatically describe the causes and sub-causes responsible for the occurrence of a fault. This paper suggests a conceptual fault cause tree model based on the probabilistic framework for ranking the faults and their possible causes. An effective mitigation strategy is required to mitigate the causes and sub-causes. Once mitigation of the cause creating a fault is achieved, the system is analyzed and tested for accuracy and reliability. The Proposed technique assures that all sub-causes even at the lowest level of abstraction is taken into consideration in making the system more robust against the particular fault. The proposed model is also used to analyze the faulty probability of a heterogeneous network.
Lifespan psychology: theory and application to intellectual functioning.

The focus of this review is on theory and research of lifespan (lifespan developmental) psychology. The theoretical analysis integrates evolutionary and ontogenetic perspectives on cultural and human development across several levels of analysis. Specific predictions are advanced dealing with the general architecture of lifespan ontogeny, including its directionality and age-differential allocation of developmental resources into the three major goals of developmental adaptation: growth, maintenance, and regulation of loss. Consistent with this general lifespan architecture, a meta-theory of development is outlined that is based on the orchestrated and adaptive interplay between three processes of behavioral regulation: selection, optimization, and compensation. Finally, these propositions and predictions about the general nature of lifespan development are examined and supported by empirical evidence on the development of cognition and intelligence across the life span.
The Aneka platform and QoS-driven resource provisioning for elastic applications on hybrid Clouds

Cloud computing alters the way traditional software systems are built and run by introducing a utilitybased model for delivering IT infrastructure, platforms, applications, and services. The consolidation of this new paradigm in both enterprises and academia demanded reconsideration in the way IT resources are used, so Cloud computing can be used together with available resources. A case for the utilization of Clouds for increasing the capacity of computing infrastructures is Desktop Grids: these infrastructures typically provide best effort execution of high throughput jobs and other workloads that fit the model of the platform. By enhancing Desktop Grid infrastructures with Cloud resources, it is possible to offer QoS to users, motivating the adoption of Desktop Grids as a viable platform for application execution. In this paper,we describe howAneka, a platform for developing scalable applications on the Cloud, supports such a vision by provisioning resources fromdifferent sources and supporting different applicationmodels.We highlight the key concepts and features of Aneka that support the integration between Desktop Grids and Clouds and present an experiment showing the performance of this integration.
The Possibility of an Epidemic Meme Analogy for Web Community Population Analysis

The aim of this paper is to discuss the possibility of understanding human social interaction in web communities by analogy with a disease propagation model from epidemiology. When an article is submitted by an individual to a social web service, it is potentially influenced by other participants. The submission sometimes starts a long and argumentative chain of articles, but often does not. This complex behavior makes management of server resources difficult and a more theoretical methodology is required. This paper tries to express these complex human dynamics by analogy with infection by a virus. In this first report, by fitting an epidemiological model to Bulletin Board System (BBS) logs in terms of a numerical triple, we show that the analogy is reasonable and beneficial because the analogy can estimate the community size despite the submitter’s information alone being observable.
Model-based policy gradients with parameter-based exploration by least-squares conditional density estimation

The goal of reinforcement learning (RL) is to let an agent learn an optimal control policy in an unknown environment so that future expected rewards are maximized. The model-free RL approach directly learns the policy based on data samples. Although using many samples tends to improve the accuracy of policy learning, collecting a large number of samples is often expensive in practice. On the other hand, the model-based RL approach first estimates the transition model of the environment and then learns the policy based on the estimated transition model. Thus, if the transition model is accurately learned from a small amount of data, the model-based approach is a promising alternative to the model-free approach. In this paper, we propose a novel model-based RL method by combining a recently proposed model-free policy search method called policy gradients with parameter-based exploration and the state-of-the-art transition model estimator called least-squares conditional density estimation. Through experiments, we demonstrate the practical usefulness of the proposed method.
Automatic Gait Optimization with Gaussian Process Regression

Gait optimization is a basic yet challenging problem for both quadrupedal and bipedal robots. Although techniques for automating the process exist, most involve local function optimization procedures that suffer from three key drawbacks. Local optimization techniques are naturally plagued by local optima, make no use of the expensive gait evaluations once a local step is taken, and do not explicitly model noise in gait evaluation. These drawbacks increase the need for a large number of gait evaluations, making optimization slow, data inefficient, and manually intensive. We present a Bayesian approach based on Gaussian process regression that addresses all three drawbacks. It uses a global search strategy based on a posterior model inferred from all of the individual noisy evaluations. We demonstrate the technique on a quadruped robot, using it to optimize two different criteria: speed and smoothness. We show in both cases our technique requires dramatically fewer gait evaluations than state-of-the-art local gradient approaches.
Understanding Intra-Class Knowledge Inside CNN

Convolutional Neural Network (CNN) has been successful in image recognition tasks, and recent works shed lights on how CNN separates different classes with the learned inter-class knowledge through visualization [8, 10, 13]. In this work, we instead visualize the intra-class knowledge inside CNN to better understand how an object class is represented in the fully-connected layers. To invert the intraclass knowledge into more interpretable images, we propose a non-parametric patch prior upon previous CNN visualization models [8, 10]. With it, we show how different “styles” of templates for an object class are organized by CNN in terms of location and content, and represented in a hierarchical and ensemble way. Moreover, such intra-class knowledge can be used in many interesting applications, e.g. style-based image retrieval and style-based object completion.
CONTENDING WITH GROUP IMAGE : THE PSYCHOLOGY OF STEREOTYPE AND SOCIAL IDENTITY THREAT

Our research on stereotype threat began with a practical question: Do social psychological processes play a significant role in the academic underperformance of certain minority groups, and if so, what is the nature of those processes? In our search for answers, we soon came upon an intriguing finding: Women at the University of Michigan seemed to perform lower than their tested skills would predict in difficult math classes yet at their predicted levels in other classes that we examined such as English or, as we later found, in entry-level math classes. By that time we had been long aware of what is known in the standardized testing literature as the "underperformance phenomenon": At each level of academic skill as measured by prior tests, such as the Scholastic Assessment Test (SAT), and grades, a group of students sharing a given social identity gets lower subsequent grades than other students. Underperformance such as this characterizes the school and college performance of a number of American minority groups--African Americans, Native Americans, and many Latino groups (e.g., Bowen & Bok, 1998; Jensen, 1980; Ramist, Lewis, & McCamley-Jenkins, 1994). And this fact has a rather startling implication: Their poorer performance in school is not due entirely to their lack of skills or preparation. The underperformance phenomenon documents lower performance by these groups at each level of skill that is, when skill and preparation as measured by tests are essentially held constant. Clearly, then, something beyond weaker skills and preparation undermines the school performance of these groups.
E-commerce : the role of familiarity and trust

Familiarity is a precondition for trust, claims Luhmann [28: Luhmann N. Trust and power. Chichester, UK: Wiley, 1979 (translation from German)], and trust is a prerequisite of social behavior, especially regarding important decisions. This study examines this intriguing idea in the context of the E-commerce involved in inquiring about and purchasing books on the Internet. Survey data from 217 potential users support and extend this hypothesis. The data show that both familiarity with an Internet vendor and its processes and trust in the vendor in ̄uenced the respondents' intentions to inquire about books, and their intentions to purchase them. Additionally, the data show that while familiarity indeed builds trust, it is primarily people's disposition to trust that aected their trust in the vendor. Implications for research and practice are discussed. 7 2000 Elsevier Science Ltd. All rights reserved.