nils-herrmann/clean_scirepeval huggingface dataset:
Psychological Safety , Trust , and Learning in Organizations : A Group-level Lens

This paper discusses psychological safety and distinguishes it from the related construct of interpersonal trust. Trust is the expectation that others' future actions will be favorable to one's interests; psychological safety refers to a climate in which people are comfortable being (and expressing) themselves. Although both constructs involve a willingness to be vulnerable to others' actions, they are conceptually and theoretically distinct. In particular, psychological safety is centrally tied to learning behavior, while trust lowers transactions costs and reduces the need to monitor behavior. This paper proposes a model of antecedents and consequences of psychological safety in work teams and emphasizes the centrality of psychological safety for learning behavior. Drawing from field research in a variety of organizational settings, I describe different approaches to studying and measuring psychological safety in teams. I conclude with implications of this work including limitations of psychological safety in practice and suggestions areas for future research.
A Survey on Consensus Mechanisms and Mining Management in Blockchain Networks

The past decade has witnessed the rapid evolution in blockchain technologies, which has attracted tremendous interests from both the research communities and industries. The blockchain network was originated from the Internet financial sector as a decentralized, immutable ledger system for transactional data ordering. Nowadays, it is envisioned as a powerful backbone/framework for decentralized data processing and datadriven self-organization in flat, open-access networks. In particular, the plausible characteristics of decentralization, immutability and self-organization are primarily owing to the unique decentralized consensus mechanisms introduced by blockchain networks. This survey is motivated by the lack of a comprehensive literature review on the development of decentralized consensus mechanisms in blockchain networks. In this survey, we provide a systematic vision of the organization of blockchain networks. By emphasizing the unique characteristics of incentivized consensus in blockchain networks, our in-depth review of the state-ofthe-art consensus protocols is focused on both the perspective of distributed consensus system design and the perspective of incentive mechanism design. From a game-theoretic point of view, we also provide a thorough review on the strategy adoption for self-organization by the individual nodes in the blockchain backbone networks. Consequently, we provide a comprehensive survey on the emerging applications of the blockchain networks in a wide range of areas. We highlight our special interest in how the consensus mechanisms impact these applications. Finally, we discuss several open issues in the protocol design for blockchain consensus and the related potential research directions.
A unified framework for context assisted face clustering

Automatic face clustering, which aims to group faces referring to the same people together, is a key component for face tagging and image management. Standard face clustering approaches that are based on analyzing facial features can already achieve high-precision results. However, they often suffer from low recall due to the large variation of faces in pose, expression, illumination, occlusion, etc. To improve the clustering recall without reducing the high precision, we leverage the heterogeneous context information to iteratively merge the clusters referring to same entities. We first investigate the appropriate methods to utilize the context information at the cluster level, including using of "common scene", people co-occurrence, human attributes, and clothing. We then propose a unified framework that employs bootstrapping to automatically learn adaptive rules to integrate this heterogeneous contextual information, along with facial features, together. Experimental results on two personal photo collections and one real-world surveillance dataset demonstrate the effectiveness of the proposed approach in improving recall while maintaining very high precision of face clustering.
Automatic Fire Detection System Using Adaptive Fusion Algorithm for Fire Fighting Robot

Multisensor fire detection algorithm (MSFDA) is one of the current important research issues for intelligent building. First, we design a fire fighting robot with extinguish for the intelligent building. The fire fighting robot is constructed using aluminium frame. The contour of the robot is cylinder. The diameter is 40 cm, and height is about 80 cm. There are six systems in the fire fighting robot, including structure, avoidance obstacle and driver system, software development system, fire detection, remote supervise system and others. We design the fire detection system using three flame sensors in the fire fighting robot. The adaptive fusion method is proposed for tire detection of fire fighting robot. We use computer simulation to improve the method to be adequate for fire detection. We design the fire detection system in the fire fighting robot, and program the tire detection and fighting procedure using sensor based method. Finally, we implement the fire detection system using fire fighting robot. If fire accident is true, the robot can find out fire source using the proposed method by fire detection system, and move to fire source to fight the fire using extinguish.
Reward, motivation, and emotion systems associated with early-stage intense romantic love.

Early-stage romantic love can induce euphoria, is a cross-cultural phenomenon, and is possibly a developed form of a mammalian drive to pursue preferred mates. It has an important influence on social behaviors that have reproductive and genetic consequences. To determine which reward and motivation systems may be involved, we used functional magnetic resonance imaging and studied 10 women and 7 men who were intensely "in love" from 1 to 17 mo. Participants alternately viewed a photograph of their beloved and a photograph of a familiar individual, interspersed with a distraction-attention task. Group activation specific to the beloved under the two control conditions occurred in dopamine-rich areas associated with mammalian reward and motivation, namely the right ventral tegmental area and the right postero-dorsal body and medial caudate nucleus. Activation in the left ventral tegmental area was correlated with facial attractiveness scores. Activation in the right anteromedial caudate was correlated with questionnaire scores that quantified intensity of romantic passion. In the left insula-putamen-globus pallidus, activation correlated with trait affect intensity. The results suggest that romantic love uses subcortical reward and motivation systems to focus on a specific individual, that limbic cortical regions process individual emotion factors, and that there is localization heterogeneity for reward functions in the human brain.
AffectButton: A method for reliable and valid affective self-report

In this article we report on a new digital interactive self-report method for the measurement of human affect. The AffectButton (Broekens & Brinkman, 2009) is a button that enables users to provide affective feedback in terms of values on the well-known three affective dimensions of Pleasure (Valence), Arousal and Dominance. The AffectButton is an interface component that functions and looks like a medium-sized button. The button presents one dynamically changing iconic facial expression that changes based on the coordinates of the user’s pointer in the button. To give affective feedback the user selects the most appropriate expression by clicking the button, effectively enabling 1-click affective self-report on 3 affective dimensions. Here we analyze 5 previously published studies, and 3 novel large-scale studies (n=325, n=202, n=128). Our results show the reliability, validity, and usability of the button for acquiring three types of affective feedback in various domains. The tested domains are holiday preferences, real-time music annotation, emotion words, and textual situation descriptions (ANET). The types of affective feedback tested are preferences, affect attribution to the previously mentioned stimuli, and self-reported mood. All of the subjects tested were Dutch and aged between 15 and 56 years. We end this article with a discussion of the limitations of the AffectButton and of its relevance to areas including recommender systems, preference elicitation, social computing, online surveys, coaching and tutoring, experimental psychology and psychometrics, content annotation, and game consoles.
DESIGNING VIRTUAL CUSTOMER ENVIRONMENTS FOR NEW PRODUCT DEVELOPMENT : TOWARD A THEORY

Virtual customer communities enable firms to establish distributed innovation models that involve varied customer roles in new product development. In this article I use a multitheorotic lens to examine the design of such virtual customer environments, focusing on four underlying theoretical themes (interaction pattern, knowledge creation, customer motivation, and virtual customer community-new product development team integration) and deriving their implications for virtual customer environment design. I offer propositions that relate specific virtual customer environment design elements to successful customer value creation, and thereby to new product development success.
SPADE: An Efficient Algorithm for Mining Frequent Sequences

In this paper we present SPADE, a new algorithm for fast discovery of Sequential Patterns. The existing solutions to this problem make repeated database scans, and use complex hash structures which have poor locality. SPADE utilizes combinatorial properties to decompose the original problem into smaller sub-problems, that can be independently solved in main-memory using efficient lattice search techniques, and using simple join operations. All sequences are discovered in only three database scans. Experiments show that SPADE outperforms the best previous algorithm by a factor of two, and by an order of magnitude with some pre-processed data. It also has linear scalability with respect to the number of input-sequences, and a number of other database parameters. Finally, we discuss how the results of sequence mining can be applied in a real application domain.
A Hierarchical Multimodal Attention-based Neural Network for Image Captioning

A novel hierarchical multimodal attention-based model is developed in this paper to generate more accurate and descriptive captions for images. Our model is an "end-to-end" neural network which contains three related sub-networks: a deep convolutional neural network to encode image contents, a recurrent neural network to identify the objects in images sequentially, and a multimodal attention-based recurrent neural network to generate image captions. The main contribution of our work is that the hierarchical structure and multimodal attention mechanism is both applied, thus each caption word can be generated with the multimodal attention on the intermediate semantic objects and the global visual content. Our experiments on two benchmark datasets have obtained very positive results.
Structure from Motion with Objects

This paper shows for the first time that is possible to reconstruct the position of rigid objects and to jointly recover affine camera calibration solely from a set of object detections in a video sequence. In practice, this work can be considered as the extension of Tomasi and Kanade factorization method using objects. Instead of using points to form a rank constrained measurement matrix, we can form a matrix with similar rank properties using 2D object detection proposals. In detail, we first fit an ellipse onto the image plane at each bounding box as given by the object detector. The collection of all the ellipses in the dual space is used to create a measurement matrix that gives a specific rank constraint. This matrix can be factorised and metrically upgraded in order to provide the affine camera matrices and the 3D position of the objects as an ellipsoid. Moreover, we recover the full 3D quadric thus giving additional information about object occupancy and 3D pose. Finally, we also show that 2D points measurements can be seamlessly included in the framework to reduce the number of objects required. This last aspect unifies the classical point-based Tomasi and Kanade approach with objects in a unique framework. Experiments with synthetic and real data show the feasibility of our approach for the affine camera case.
Hydrothermal liquefaction of Nannochloropsis sp . : Systematic study of process variables and analysis of the product fractions

We investigated hydrothermal liquefaction of Nannochloropsis sp. at different temperatures (250e400 C), times (10e90 min), water densities (0.3e0.5 g/mL), and biomass loadings (5 e35 wt %). Liquefaction produced a biocrude with light and heavy fractions, along with gaseous, aqueous, and solid by-product fractions. The gravimetric yields of the product fractions from experiments at 250 C summed to an average of 100 4 wt %, showing mass balance closure at 250 C. The gravimetric yields of the product fractions are independent of water density at 400 C. Increasing the biomass loading increases the biocrude yield from 36 to 46 wt %. The yields of light and heavy biocrude depend on reaction time and temperature, but their combined yield depends primarily on temperature. Regardless of reaction time and temperature, the yield of products distributed to the aqueous phase is 51 5 wt % and the light biocrude is 75 1 wt % C. Two-thirds of the N in the alga is immediately distributed to the aqueous phase and up to 84% can be partitioned there. Up to 85% of the P is distributed to the aqueous phase in the form of free phosphate. Thus, N and P can be recovered in the aqueous phase for nutrient recycling. Up to 80% of the chemical energy in the alga is retained within the biocrude. The quantitative results reported herein provide the basis for a reaction network for algae liquefaction. a 2012 Elsevier Ltd. All rights reserved.
Personal recommendation using deep recurrent neural networks in NetEase

Each user session in an e-commerce system can be modeled as a sequence of web pages, indicating how the user interacts with the system and makes his/her purchase. A typical recommendation approach, e.g., Collaborative Filtering, generates its results at the beginning of each session, listing the most likely purchased items. However, such approach fails to exploit current viewing history of the user and hence, is unable to provide a real-time customized recommendation service. In this paper, we build a deep recurrent neural network to address the problem. The network tracks how users browse the website using multiple hidden layers. Each hidden layer models how the combinations of webpages are accessed and in what order. To reduce the processing cost, the network only records a finite number of states, while the old states collapse into a single history state. Our model refreshes the recommendation result each time when user opens a new web page. As user's session continues, the recommendation result is gradually refined. Furthermore, we integrate the recurrent neural network with a Feedfoward network which represents the user-item correlations to increase the prediction accuracy. Our approach has been applied to Kaola (http://www.kaola.com), an e-commerce website powered by the NetEase technologies. It shows a significant improvement over previous recommendation service.
Why You Can ' t Find a Taxi in the Rain and Other Labor Supply Lessons from Cab Drivers

Why You Can’t Find a Taxi in the Rain and Other Labor Supply Lessons from Cab Drivers In a seminal paper, Camerer, Babcock, Loewenstein, and Thaler (1997) find that the wage elasticity of daily hours of work New York City (NYC) taxi drivers is negative and conclude that their labor supply behavior is consistent with target earning (having reference dependent preferences). I replicate and extend the CBLT analysis using data from all trips taken in all taxi cabs in NYC for the five years from 2009-2013. Using the model of expectations-based reference points of Koszegi and Rabin (2006), I distinguish between anticipated and unanticipated daily wage variation and present evidence that only a small fraction of wage variation (about 1/8) is unanticipated so that reference dependence (which is relevant only in response to unanticipated variation) can, at best, play a limited role in determining labor supply. The overall pattern in my data is clear: drivers tend to respond positively to unanticipated as well as anticipated increases in earnings opportunities. This is consistent with the neoclassical optimizing model of labor supply and does not support the reference dependent preferences model. I explore heterogeneity across drivers in their labor supply elasticities and consider whether new drivers differ from more experienced drivers in their behavior. I find substantial heterogeneity across drivers in their elasticities, but the estimated elasticities are generally positive and only rarely substantially negative. I also find that new drivers with smaller elasticities are more likely to exit the industry while drivers who remain learn quickly to be better optimizers (have positive labor supply elasticities that grow with experience).
Thwarting Memory Disclosure with Efficient Hypervisor-enforced Intra-domain Isolation

Exploiting memory disclosure vulnerabilities like the HeartBleed bug may cause arbitrary reading of a victim's memory, leading to leakage of critical secrets such as crypto keys, personal identity and financial information. While isolating code that manipulates critical secrets into an isolated execution environment is a promising countermeasure, existing approaches are either too coarse-grained to prevent intra-domain attacks, or require excessive intervention from low-level software (e.g., hypervisor or OS), or both. Further, few of them are applicable to large-scale software with millions of lines of code. This paper describes a new approach, namely SeCage, which retrofits commodity hardware virtualization extensions to support efficient isolation of sensitive code manipulating critical secrets from the remaining code. SeCage is designed to work under a strong adversary model where a victim application or even the OS may be controlled by the adversary, while supporting large-scale software with small deployment cost. SeCage combines static and dynamic analysis to decompose monolithic software into several compart- ments, each of which may contain different secrets and their corresponding code. Following the idea of separating control and data plane, SeCage retrofits the VMFUNC mechanism and nested paging in Intel processors to transparently provide different memory views for different compartments, while allowing low-cost and transparent invocation across domains without hypervisor intervention.
 We have implemented SeCage in KVM on a commodity Intel machine. To demonstrate the effectiveness of SeCage, we deploy it to the Nginx and OpenSSH server with the OpenSSL library as well as CryptoLoop with small efforts. Security evaluation shows that SeCage can prevent the disclosure of private keys from HeartBleed attacks and memory scanning from rootkits. The evaluation shows that SeCage only incurs small performance and space overhead.
A highly efficient Vivaldi antenna array design on thick substrate and fed by SIW structure with integrated GCPW feed

A compact Vivaldi antenna array printed on thick substrate and fed by a substrate integrated waveguide (SIW) structure is presented here. The array utilizes an SIW binary splitter to significantly minimize the feed structure insertion losses. Meanwhile, the splitter is fed with an integrated CPW to sustain a good input match and prevent higher order modes excitation over wide band operation. Extensive simulation and measurements were carried out and was presented here with a comparison to previously developed arrays.
TimeGraph: GPU Scheduling for Real-Time Multi-Tasking Environments

The Graphics Processing Unit (GPU) is now commonly used for graphics and data-parallel computing. As more and more applications tend to accelerate on the GPU in multi-tasking environments where multiple tasks access the GPU concurrently, operating systems must provide prioritization and isolation capabilities in GPU resource management, particularly in real-time setups. We present TimeGraph, a real-time GPU scheduler at the device-driver level for protecting important GPU workloads from performance interference. TimeGraph adopts a new event-driven model that synchronizes the GPU with the CPU to monitor GPU commands issued from the user space and control GPU resource usage in a responsive manner. TimeGraph supports two prioritybased scheduling policies in order to address the tradeoff between response times and throughput introduced by the asynchronous and non-preemptive nature of GPU processing. Resource reservation mechanisms are also employed to account and enforce GPU resource usage, which prevent misbehaving tasks from exhausting GPU resources. Prediction of GPU command execution costs is further provided to enhance isolation. Our experiments using OpenGL graphics benchmarks demonstrate that TimeGraph maintains the frame-rates of primary GPU tasks at the desired level even in the face of extreme GPU workloads, whereas these tasks become nearly unresponsive without TimeGraph support. Our findings also include that the performance overhead imposed on TimeGraph can be limited to 4-10%, and its event-driven scheduler improves throughput by about 30 times over the existing tick-driven scheduler.
A Dataset and Taxonomy for Urban Sound Research

Automatic urban sound classification is a growing area of research with applications in multimedia retrieval and urban informatics. In this paper we identify two main barriers to research in this area - the lack of a common taxonomy and the scarceness of large, real-world, annotated data. To address these issues we present a taxonomy of urban sounds and a new dataset, UrbanSound, containing 27 hours of audio with 18.5 hours of annotated sound event occurrences across 10 sound classes. The challenges presented by the new dataset are studied through a series of experiments using a baseline classification system.
Spam Filtering with Naive Bayes - Which Naive Bayes?

Naive Bayes is very popular in commercial and open-source anti-spam e-mail filters. There are, however, several forms of Naive Bayes, something the anti-spam literature does not always acknowledge. We discuss five different versions of Naive Bayes, and compare them on six new, non-encoded datasets, that contain ham messages of particular Enron users and fresh spam messages. The new datasets, which we make publicly available, are more realistic than previous comparable benchmarks, because they maintain the temporal order of the messages in the two categories, and they emulate the varying proportion of spam and ham messages that users receive over time. We adopt an experimental procedure that emulates the incremental training of personalized spam filters, and we plot roc curves that allow us to compare the different versions of nb over the entire tradeoff between true positives and true negatives.
PathFinder: A Pattern-Based Packet Classifier

This paper describes a pattern-based approach to building packet classifiers. One novelty of the approach is that it can be implemented efficiently in both software and hardware. A performance study shows that the software implementation is about twice as fast as existing mechanisms, and that the hardware implementation is currently able to keep up with OC-12 (622Mbps) network links and is likely to operate at gigabit speeds in the near future.