nils-herrmann/clean_scirepeval huggingface dataset:
Faster Parallel Traversal of Scale Free Graphs at Extreme Scale with Vertex Delegates

At extreme scale, irregularities in the structure of scale-free graphs such as social network graphs limit our ability to analyze these important and growing datasets. A key challenge is the presence of high-degree vertices (hubs), that leads to parallel workload and storage imbalances. The imbalances occur because existing partitioning techniques are not able to effectively partition high-degree vertices.
 We present techniques to distribute storage, computation, and communication of hubs for extreme scale graphs in distributed memory supercomputers. To balance the hub processing workload, we distribute hub data structures and related computation among a set of delegates. The delegates coordinate using highly optimized, yet portable, asynchronous broadcast and reduction operations. We demonstrate scalability of our new algorithmic technique using Breadth-First Search (BFS), Single Source Shortest Path (SSSP), K-Core Decomposition, and PageRank on synthetically generated scale-free graphs. Our results show excellent scalability on large scale-free graphs up to 131K cores of the IBM BG/P, and outperform the best known Graph500 performance on BG/P Intrepid by 15%.
Flowing ConvNets for Human Pose Estimation in Videos

The objective of this work is human pose estimation in videos, where multiple frames are available. We investigate a ConvNet architecture that is able to benefit from temporal context by combining information across the multiple frames using optical flow. To this end we propose a network architecture with the following novelties: (i) a deeper network than previously investigated for regressing heatmaps, (ii) spatial fusion layers that learn an implicit spatial model, (iii) optical flow is used to align heatmap predictions from neighbouring frames, and (iv) a final parametric pooling layer which learns to combine the aligned heatmaps into a pooled confidence map. We show that this architecture outperforms a number of others, including one that uses optical flow solely at the input layers, one that regresses joint coordinates directly, and one that predicts heatmaps without spatial fusion. The new architecture outperforms the state of the art by a large margin on three video pose estimation datasets, including the very challenging Poses in the Wild dataset, and outperforms other deep methods that don't use a graphical model on the single-image FLIC benchmark (and also [5, 35] in the high precision region).
Embodied cognition and the magical future of interaction design

The theory of embodied cognition can provide HCI practitioners and theorists with new ideas about interaction and new principles for better designs. I support this claim with four ideas about cognition: (1) interacting with tools changes the way we think and perceive -- tools, when manipulated, are soon absorbed into the body schema, and this absorption leads to fundamental changes in the way we perceive and conceive of our environments; (2) we think with our bodies not just with our brains; (3) we know more by doing than by seeing -- there are times when physically performing an activity is better than watching someone else perform the activity, even though our motor resonance system fires strongly during other person observation; (4) there are times when we literally think with things. These four ideas have major implications for interaction design, especially the design of tangible, physical, context aware, and telepresence systems.
Investigating students' perceived satisfaction, behavioral intention, and effectiveness of English learning using augmented reality

This study uses augmented reality (AR) technology to implement an AR-learning system for English vocabulary learning. Although previous studies have indicated that multimedia courseware enhances learning effectiveness, some important issues may cause negative effects. This study investigates learners' satisfaction and behavioral intention as well as the effectiveness of the AR-learning system. The results of this study showed that system quality was a critical factor affecting perceived satisfaction, perceived usefulness, and AR-learning effectiveness. Perceived self-efficacy also affected perceived satisfaction and perceived usefulness. On the other hand, multimedia instruction was a minor factor affecting perceived usefulness and AR-learning effectiveness. We verified that learners' behavioral intention was affected by perceived satisfaction and perceived usefulness of the AR-learning system. Furthermore, the design of system function and operation process must be more straightforward for learners when adopting new technology in the learning system.
Capturing, indexing, clustering, and retrieving system history

We present a method for automatically extracting from a running system an indexable signature that distills the essential characteristic from a system state and that can be subjected to automated clustering and similarity-based retrieval to identify when an observed system state is similar to a previously-observed state. This allows operators to identify and quantify the frequency of recurrent problems, to leverage previous diagnostic efforts, and to establish whether problems seen at different installations of the same site are similar or distinct. We show that the naive approach to constructing these signatures based on simply recording the actual ``raw'' values of collected measurements is ineffective, leading us to a more sophisticated approach based on statistical modeling and inference. Our method requires only that the system's metric of merit (such as average transaction response time) as well as a collection of lower-level operational metrics be collected, as is done by existing commercial monitoring tools. Even if the traces have no annotations of prior diagnoses of observed incidents (as is typical), our technique successfully clusters system states corresponding to similar problems, allowing diagnosticians to identify recurring problems and to characterize the ``syndrome'' of a group of problems. We validate our approach on both synthetic traces and several weeks of production traces from a customer-facing geoplexed 24 x 7 system; in the latter case, our approach identified a recurring problem that had required extensive manual diagnosis, and also aided the operators in correcting a previous misdiagnosis of a different problem.
Temporal 3D ConvNets: New Architecture and Transfer Learning for Video Classification

The work in this paper is driven by the question how to exploit the temporal cues available in videos for their accurate classification, and for human action recognition in particular? Thus far, the vision community has focused on spatio-temporal approaches with fixed temporal convolution kernel depths. We introduce a new temporal layer that models variable temporal convolution kernel depths. We embed this new temporal layer in our proposed 3D CNN. We extend the DenseNet architecture which normally is 2D with 3D filters and pooling kernels. We name our proposed video convolutional network ‘Temporal 3D ConvNet’ (T3D) and its new temporal layer ‘Temporal Transition Layer’ (TTL). Our experiments show that T3D outperforms the current state-of-the-art methods on the HMDB51, UCF101 and Kinetics datasets. The other issue in training 3D ConvNets is about training them from scratch with a huge labeled dataset to get a reasonable performance. So the knowledge learned in 2D ConvNets is completely ignored. Another contribution in this work is a simple and effective technique to transfer knowledge from a pre-trained 2D CNN to a randomly initialized 3D CNN for a stable weight initialization. This allows us to significantly reduce the number of training samples for 3D CNNs. Thus, by finetuning this network, we beat the performance of generic and recent methods in 3D CNNs, which were trained on large video datasets, e.g. Sports-1M, and finetuned on the target datasets, e.g. HMDB51/UCF101. The T3D codes will be released soon1.
BotHunter: Detecting Malware Infection Through IDS-Driven Dialog Correlation

We present a new kind of network perimeter monitoring strategy, which focuses on recognizing the infection and coordination dialog that occurs during a successful malware infection. BotHunter is an application designed to track the two-way communication flows between internal assets and external entities, developing an evidence trail of data exchanges that match a state-based infection sequence model. BotHunter consists of a correlation engine that is driven by three malware-focused network packet sensors, each charged with detecting specific stages of the malware infection process, including inbound scanning, exploit usage, egg downloading, outbound bot coordination dialog, and outbound attack propagation. The BotHunter correlator then ties together the dialog trail of inbound intrusion alarms with those outbound communication patterns that are highly indicative of successful local host infection. When a sequence of evidence is found to match BotHunter’s infection dialog model, a consolidated report is produced to capture all the relevant events and event sources that played a role during the infection process. We refer to this analytical strategy of matching the dialog flows between internal assets and the broader Internet as dialog-based correlation, and contrast this strategy to other intrusion detection and alert correlation methods. We present our experimental results using BotHunter in both virtual and live testing environments, and discuss our Internet release of the BotHunter prototype. BotHunter is made available both for operational use and to help stimulate research in understanding the life cycle of malware infections.
Addressing Safety and Security Contradictions in Cyber-Physical Systems

Modern cyber-physical systems are found in important domains such as automobiles, medical devices, building automation, avionics, etc.. Hence, they are increasingly prone to security violations. Often such vulnerabilities occur as a result of contradictory requirements between the safety/real-time properties and the security needs of the system. In this paper we propose a formal framework that assists designers in detecting such conflicts early, thus increasing both, the safety and the security of the overall system.
PUFatt: Embedded platform attestation based on novel processor-based PUFs

Software-based attestation schemes aim at proving the integrity of code and data residing on a platform to a verifying party. However, they do not bind the hardware characteristics to the attestation protocol and are vulnerable to impersonation attacks.
 We present PUFatt, a new automatable method for linking software-based attestation to intrinsic device characteristics by means of a novel processor-based Physically Unclonable Function, which enables secure timed (and even) remote attestation particularly suitable for embedded and low-cost devices. Our proof-of-concept implementation on FPGA demonstrates the effectiveness, applicability and practicability of the approach.
Sensorless control of PMSM in high speed range with iterative sliding mode observer

This paper proposes two control algorithms for a sensorless speed control of a PMSM. One is a new low pass filter. This filter is designed to have the variable cutoff frequency according to the rotor speed. And the phase delay angle is so small as to be ignored not only in the low speed region but also in the high speed region including the field weakening region. Sensorless control of a PMSM can be guaranteed without any delay angle by using the proposed low pass filter. The other is a new iterative sliding mode observer (I-SMO). Generally the sliding mode observer (SMO) has the attractive features of the robustness to disturbances, and parameter variations. In the high speed region the switching gain of SMO must be large enough to operate the sliding mode stably. But the estimated currents and back EMF can not help having much ripple or chattering components especially in the high speed region including the flux weakening region. Using I-SMO can reduce chattering components of the estimated currents and back EMF in all speed regions without any help of the expensive hardware such as the high performance DSP and A/D converter. Experimental results show the usefulness of the proposed two algorithms for the sensorless drive system of a PMSM.
Intersecting Faces: Non-negative Matrix Factorization With New Guarantees

Non-negative matrix factorization (NMF) is a natural model of admixture and is widely used in science and engineering. A plethora of algorithms have been developed to tackle NMF, but due to the non-convex nature of the problem, there is little guarantee on how well these methods work. Recently a surge of research have focused on a very restricted class of NMFs, called separable NMF, where provably correct algorithms have been developed. In this paper, we propose the notion of subset-separable NMF, which substantially generalizes the property of separability. We show that subset-separability is a natural necessary condition for the factorization to be unique or to have minimum volume. We developed the Face-Intersect algorithm which provably and efficiently solves subset-separable NMF under natural conditions, and we prove that our algorithm is robust to small noise. We explored the performance of Face-Intersect on simulations and discuss settings where it empirically outperformed the state-of-art methods. Our work is a step towards finding provably correct algorithms that solve large classes of NMF problems.
A learning based approach to control synthesis of Markov decision processes for linear temporal logic specifications

We propose to synthesize a control policy for a Markov decision process (MDP) such that the resulting traces of the MDP satisfy a linear temporal logic (LTL) property. We construct a product MDP that incorporates a deterministic Rabin automaton generated from the desired LTL property. The reward function of the product MDP is defined from the acceptance condition of the Rabin automaton. This construction allows us to apply techniques from learning theory to the problem of synthesis for LTL specifications even when the transition probabilities are not known a priori. We prove that our method is guaranteed to find a controller that satisfies the LTL property with probability one if such a policy exists, and we suggest empirically that our method produces reasonable control strategies even when the LTL property cannot be satisfied with probability one.
Factors that influence the development of compassion fatigue, burnout, and compassion satisfaction in emergency department nurses.

The purpose of this study was twofold: (a) to determine the prevalence of compassion satisfaction, compassion fatigue, and burnout in emergency department nurses throughout the United States and (b) to examine which demographic and work-related components affect the development of compassion satisfaction, compassion fatigue, and burnout in this nursing specialty.



This was a nonexperimental, descriptive, and predictive study using a self-administered survey. Survey packets including a demographic questionnaire and the Professional Quality of Life Scale version 5 (ProQOL 5) were mailed to 1,000 selected emergency nurses throughout the United States. The ProQOL 5 scale was used to measure the prevalence of compassion satisfaction, compassion fatigue, and burnout among emergency department nurses. Multiple regression using stepwise solution was employed to determine which variables of demographics and work-related characteristics predicted the prevalence of compassion satisfaction, compassion fatigue, and burnout. The α level was set at .05 for statistical significance.



The results revealed overall low to average levels of compassion fatigue and burnout and generally average to high levels of compassion satisfaction among this group of emergency department nurses. The low level of manager support was a significant predictor of higher levels of burnout and compassion fatigue among emergency department nurses, while a high level of manager support contributed to a higher level of compassion satisfaction.



The results may serve to help distinguish elements in emergency department nurses' work and life that are related to compassion satisfaction and may identify factors associated with higher levels of compassion fatigue and burnout.



Improving recognition and awareness of compassion satisfaction, compassion fatigue, and burnout among emergency department nurses may prevent emotional exhaustion and help identify interventions that will help nurses remain empathetic and compassionate professionals.
Précis of The brain and emotion.

The topics treated in The brain and emotion include the definition, nature, and functions of emotion (Ch. 3); the neural bases of emotion (Ch. 4); reward, punishment, and emotion in brain design (Ch. 10); a theory of consciousness and its application to understanding emotion and pleasure (Ch. 9); and neural networks and emotion-related learning (Appendix). The approach is that emotions can be considered as states elicited by reinforcers (rewards and punishers). This approach helps with understanding the functions of emotion, with classifying different emotions, and in understanding what information-processing systems in the brain are involved in emotion, and how they are involved. The hypothesis is developed that brains are designed around reward- and punishment-evaluation systems, because this is the way that genes can build a complex system that will produce appropriate but flexible behavior to increase fitness (Ch. 10). By specifying goals rather than particular behavioral patterns of responses, genes leave much more open the possible behavioral strategies that might be required to increase fitness. The importance of reward and punishment systems in brain design also provides a basis for understanding the brain mechanisms of motivation, as described in Chapters 2 for appetite and feeding, 5 for brain-stimulation reward, 6 for addiction, 7 for thirst, and 8 for sexual behavior.
Low-Profile Equiangular Spiral Antenna Backed by an EBG Reflector

The bi-directional beam from an equiangular spiral antenna (EAS) is changed to a unidirectional beam using an electromagnetic band gap (EBG) reflector. The antenna height, measured from the upper surface of the EBG reflector to the spiral arms, is chosen to be extremely small to realize a low-profile antenna: 0.07 wavelength at the lowest analysis frequency of 3 GHz. The analysis shows that the EAS backed by the EBG reflector does not reproduce the inherent wideband axial ratio characteristic observed when the EAS is isolated in free space. The deterioration in the axial ratio is examined by decomposing the total radiation field into two field components: one component from the equiangular spiral and the other from the EBG reflector. The examination reveals that the amplitudes and phases of these two field components do not satisfy the constructive relationship necessary for circularly polarized radiation. Based on this finding, next, the EBG reflector is modified by gradually removing the patch elements from the center region of the reflector, thereby satisfying the required constructive relationship between the two field components. This equiangular spiral with a modified EBG reflector shows wideband characteristics with respect to the axial ratio, input impedance and gain within the design frequency band (4-9 GHz). Note that, for comparison, the antenna characteristics for an EAS isolated in free space and an EAS backed by a perfect electric conductor are also presented.
Fingerprint Image Reconstruction from Standard Templates

A minutiae-based template is a very compact representation of a fingerprint image, and for a long time, it has been assumed that it did not contain enough information to allow the reconstruction of the original fingerprint. This work proposes a novel approach to reconstruct fingerprint images from standard templates and investigates to what extent the reconstructed images are similar to the original ones (that is, those the templates were extracted from). The efficacy of the reconstruction technique has been assessed by estimating the success chances of a masquerade attack against nine different fingerprint recognition algorithms. The experimental results show that the reconstructed images are very realistic and that, although it is unlikely that they can fool a human expert, there is a high chance to deceive state-of-the-art commercial fingerprint recognition systems.
Indoor navigation using a diverse set of cheap, wearable sensors

Machine learning techniques are applied to the task of context awareness, or inferring aspects of the user's state given a stream of inputs from sensors worn by the person. We focus on the task of indoor navigation and show that, by integrating information from accelerometers, magnetometers and temperature and light sensors, we can collect enough information to infer the user's location. However, our navigation algorithm performs very poorly, with almost a 50% error rate, if we use only the raw sensor signals. Instead, we introduce a "data cooking" module that computes appropriate high-level features from the raw sensor data. By introducing these high-level features, we are able to reduce the error rate to 2% in our example environment.
Exposure to benevolent sexism and complementary gender stereotypes: consequences for specific and diffuse forms of system justification.

Many have suggested that complementary gender stereotypes of men as agentic (but not communal) and women as communal (but not agentic) serve to increase system justification, but direct experimental support has been lacking. The authors exposed people to specific types of gender-related beliefs and subsequently asked them to complete measures of gender-specific or diffuse system justification. In Studies 1 and 2, activating (a) communal or complementary (communal + agentic) gender stereotypes or (b) benevolent or complementary (benevolent + hostile) sexist items increased support for the status quo among women. In Study 3, activating stereotypes of men as agentic also increased system justification among men and women, but only when women's characteristics were associated with higher status. Results suggest that complementary stereotypes psychologically offset the one-sided advantage of any single group and contribute to an image of society in which everyone benefits through a balanced dispersion of benefits.
Perceptually Optimized Image Rendering

We develop a framework for rendering photographic images by directly optimizing their perceptual similarity to the original visual scene. Specifically, over the set of all images that can be rendered on a given display, we minimize the normalized Laplacian pyramid distance (NLPD), a measure of perceptual dissimilarity that is derived from a simple model of the early stages of the human visual system. When rendering images acquired with a higher dynamic range than that of the display, we find that the optimization boosts the contrast of low-contrast features without introducing significant artifacts, yielding results of comparable visual quality to current state-of-the-art methods, but without manual intervention or parameter adjustment. We also demonstrate the effectiveness of the framework for a variety of other display constraints, including limitations on minimum luminance (black point), mean luminance (as a proxy for energy consumption), and quantized luminance levels (halftoning). We show that the method may generally be used to enhance details and contrast, and, in particular, can be used on images degraded by optical scattering (e.g., fog). Finally, we demonstrate the necessity of each of the NLPD components-an initial power function, a multiscale transform, and local contrast gain control-in achieving these results and we show that NLPD is competitive with the current state-of-the-art image quality metrics.