nils-herrmann/clean_scirepeval huggingface dataset:
On the control of automatic processes: a parallel distributed processing account of the Stroop effect.

Traditional views of automaticity are in need of revision. For example, automaticity often has been treated as an all-or-none phenomenon, and traditional theories have held that automatic processes are independent of attention. Yet recent empirical data suggest that automatic processes are continuous, and furthermore are subject to attentional control. A model of attention is presented to address these issues. Within a parallel distributed processing framework, it is proposed that the attributes of automaticity depend on the strength of a processing pathway and that strength increases with training. With the Stroop effect as an example, automatic processes are shown to be continuous and to emerge gradually with practice. Specifically, a computational model of the Stroop task simulates the time course of processing as well as the effects of learning. This was accomplished by combining the cascade mechanism described by McClelland (1979) with the backpropagation learning algorithm (Rumelhart, Hinton, & Williams, 1986). The model can simulate performance in the standard Stroop task, as well as aspects of performance in variants of this task that manipulate stimulus-onset asynchrony, response set, and degree of practice. The model presented is contrasted against other models, and its relation to many of the central issues in the literature on attention, automaticity, and interference is discussed.
A non-reference image fusion metric based on mutual information of image features

The widespread usage of image fusion causes an increase in the importance of assessing the performance of different fusion algorithms. The problem of introducing a suitable quality measure for image fusion lies in the difficulty of defining an ideal fused image. In this paper, we propose a non-reference objective image fusion metric based on mutual information which calculates the amount of information conducted from the source images to the fused image. The considered information is represented by image features like gradients or edges, which are often in the form of two-dimensional signals. In this paper, a method of estimating the joint probability distribution from marginal distributions is also presented which is employed in calculation of mutual information. The proposed method is compared with the most popular existing algorithms. Various experiments, performed on several databases, certify the efficiency of our proposed method which is more consistent with the subjective criteria.
Deep reinforcement learning from human preferences

For sophisticated reinforcement learning (RL) systems to interact usefully with real-world environments, we need to communicate complex goals to these systems. In this work, we explore goals defined in terms of (non-expert) human preferences between pairs of trajectory segments. We show that this approach can effectively solve complex RL tasks without access to the reward function, including Atari games and simulated robot locomotion, while providing feedback on less than 1% of our agent’s interactions with the environment. This reduces the cost of human oversight far enough that it can be practically applied to state-of-the-art RL systems. To demonstrate the flexibility of our approach, we show that we can successfully train complex novel behaviors with about an hour of human time. These behaviors and environments are considerably more complex than any which have been previously learned from human feedback.
Learning Bayesian Networks from Incomplete Data

Much of the current research in learning Bayesian Networks fails to eeectively deal with missing data. Most of the methods assume that the data is complete, or make the data complete using fairly ad-hoc methods; other methods do deal with missing data but learn only the conditional probabilities, assuming that the structure is known. We present a principled approach to learn both the Bayesian network structure as well as the conditional probabilities from incomplete data. The proposed algorithm is an iterative method that uses a combination of Expectation-Maximization (EM) and Imputation techniques. Results are presented on synthetic data sets which show that the performance of the new algorithm is much better than ad-hoc methods for handling missing data.
Attention-Based Transactional Context Embedding for Next-Item Recommendation

To recommend the next item to a user in a transactional context is practical yet challenging in applications such as marketing campaigns. Transactional context refers to the items that are observable in a transaction. Most existing transactionbased recommender systems (TBRSs) make recommendations by mainly considering recently occurring items instead of all the ones observed in the current context. Moreover, they often assume a rigid order between items within a transaction, which is not always practical. More importantly, a long transaction often contains many items irreverent to the next choice, which tends to overwhelm the influence of a few truely relevant ones. Therefore, we posit that a good TBRS should not only consider all the observed items in the current transaction but also weight them with different relevance to build an attentive context that outputs the proper next item with a high probability. To this end, we design an effective attentionbased transaction embedding model (ATEM) for context embedding to weight each observed item in a transaction without assuming order. The empirical study on real-world transaction datasets proves that ATEM significantly outperforms the state-of-the-art methods in terms of both accuracy and novelty.
3D scene flow estimation with a rigid motion prior

We present an approach to 3D scene flow estimation, which exploits that in realistic scenarios image motion is frequently dominated by observer motion and independent, but rigid object motion. We cast the dense estimation of both scene structure and 3D motion from sequences of two or more views as a single energy minimization problem. We show that agnostic smoothness priors, such as the popular total variation, are biased against motion discontinuities in viewing direction. Instead, we propose to regularize by encouraging local rigidity of the 3D scene. We derive a local rigidity constraint of the 3D scene flow and define a smoothness term that penalizes deviations from that constraint, thus favoring solutions that consist largely of rigidly moving parts. Our experiments show that the new rigid motion prior reduces the 3D flow error by 42% compared to standard TV regularization with the same data term.
OOV Sensitive Named-Entity Recognition in Speech

Named Entity Recognition (NER), an information extraction task, is typically applied to spoken documents by cascading a large vocabulary continuous speech recognizer (LVCSR) and a named entity tagger. Recognizing named entities in automatically decoded speech is difficult since LVCSR errors can confuse the tagger. This is especially true of out-of-vocabulary (OOV) words, which are often named entities and always produce transcription errors. In this work, we improve speech NER by including features indicative of OOVs based on a OOV detector, allowing for the identification of regions of speech containing named entities, even if they are incorrectly transcribed. We construct a new speech NER data set and demonstrate significant improvements for this task.
The fundamental plan of the retina

The retina, like many other central nervous system structures, contains a huge diversity of neuronal types. Mammalian retinas contain approximately 55 distinct cell types, each with a different function. The census of cell types is nearing completion, as the development of quantitative methods makes it possible to be reasonably confident that few additional types exist. Although much remains to be learned, the fundamental structural principles are now becoming clear. They give a bottom-up view of the strategies used in the retina's processing of visual information and suggest new questions for physiological experiments and modeling.
Real-Time Inverse Kinematics Techniques for Anthropomorphic Limbs

In this paper we develop a set of inverse kinematics algorithms suitable for an anthropomorphic arm or leg. We use a combination of analytical and numerical methods to solve generalized inverse kinematics problems including position, orientation, and aiming constraints. Our combination of analytical and numerical methods results in faster and more reliable algorithms than conventional inverse Jacobian and optimization-based techniques. Additionally, unlike conventional numerical algorithms, our methods allow the user to interactively explore all possible solutions using an intuitive set of parameters that define the redundancy of the system.
Building a Research Model for Mobile Wallet Consumer Adoption: The Case of Mobile Suica in Japan

The growth of mobile commerce, or the purchase of services or goods using mobile technology, heavily depends on the availability, reliability, and acceptance of mobile wallet systems. Although several researchers have attempted to create models on the acceptance of such mobile payment systems, no single comprehensive framework has yet emerged. Based upon a broad literature review of mobile technology adoption, a comprehensive model integrating eleven key consumer-related variables affecting the adoption of mobile payment systems is proposed. This model, based on established theoretical underpinnings originally established in the technology acceptance literature, extends existing frameworks by including attractiveness of alternatives and by proposing relationships between the key constructs. Japan is at the forefront of such technology and a number of domestic companies have been effectively developing and marketing mobile wallets for some time. Using this proposed framework, we present the case of the successful adoption of Mobile Suica in Japan, which can serve as a model for the rapid diffusion of such payment systems for other countries where adoption has been unexpectedly slow.
Adoption and Use of Social Media in Small and Medium-Sized Enterprises

In recent years, social media have been increasingly adopted in enterprises. Enterprises use social media as an additional way to get in contact with their customers and support internal communication and collaboration. However, little research is devoted to the adoption and internal usage of social media in small and medium-sized enterprises (SMEs), which are of high social and economic importance. The purpose of this paper is to examine the adoption, usage, and benefits of social media in SMEs as well as potential concerns that may prevent a wider adoption of social media in SMEs. Therefore, a survey of decision-makers in German SMEs was conducted. Findings based on 190 responses indicate that SMEs started to use internal social media (e.g., wikis, blogs) in order to support collaboration among employees and to improve knowledge management. However, SMEs still face problems to manage adoption and to identify relevant business values. Based on our results, we derive several implications for SMEs, in particular how to overcome the obstacles to a wider adoption of social media.
Emotion regulation and culture: are the social consequences of emotion suppression culture-specific?

Emotional suppression has been associated with generally negative social consequences (Butler et al., 2003; Gross & John, 2003). A cultural perspective suggests, however, that these consequences may be moderated by cultural values. We tested this hypothesis in a two-part study, and found that, for Americans holding Western-European values, habitual suppression was associated with self-protective goals and negative emotion. In addition, experimentally elicited suppression resulted in reduced interpersonal responsiveness during face-to-face interaction, along with negative partner-perceptions and hostile behavior. These deleterious effects were reduced when individuals with more Asian values suppressed, and these reductions were mediated by cultural differences in the responsiveness of the suppressors. These findings suggest that many of suppression's negative social impacts may be moderated by cultural values.
Improvements in Remote Cardiopulmonary Measurement Using a Five Band Digital Camera

Remote measurement of the blood volume pulse via photoplethysmography (PPG) using digital cameras and ambient light has great potential for healthcare and affective computing. However, traditional RGB cameras have limited frequency resolution. We present results of PPG measurements from a novel five band camera and show that alternate frequency bands, in particular an orange band, allowed physiological measurements much more highly correlated with an FDA approved contact PPG sensor. In a study with participants (n = 10) at rest and under stress, correlations of over 0.92 (p <; 0.01) were obtained for heart rate, breathing rate, and heart rate variability measurements. In addition, the remotely measured heart rate variability spectrograms closely matched those from the contact approach. The best results were obtained using a combination of cyan, green, and orange (CGO) bands; incorporating red and blue channel observations did not improve performance. In short, RGB is not optimal for this problem: CGO is better. Incorporating alternative color channel sensors should not increase the cost of such cameras dramatically.
Virtual eXtensible Local Area Network (VXLAN): A Framework for Overlaying Virtualized Layer 2 Networks over Layer 3 Networks

This document describes Virtual eXtensible Local Area Network (VXLAN), which is used to address the need for overlay networks within virtualized data centers accommodating multiple tenants. The scheme and the related protocols can be used in networks for cloud service providers and enterprise data centers. This memo documents the deployed VXLAN protocol for the benefit of the Internet community.
Personalized, Cross-Lingual TTS Using Phonetic Posteriorgrams

We present a novel approach that enables a target speaker (e.g. monolingual Chinese speaker) to speak a new language (e.g. English) based on arbitrary textual input. Our system includes a trained English speaker-independent automatic speech recognition (SI-ASR) engine using TIMIT. Given the target speaker’s speech in a non-target language, we generate Phonetic PosteriorGrams (PPGs) with the SI-ASR and then train a Deep Bidirectional Long Short-Term Memory based Recurrent Neural Networks (DBLSTM) to model the relationships between the PPGs and the acoustic signal. Synthesis involves input of arbitrary text to a general TTS engine (trained on any nontarget speaker), the output of which is indexed by SI-ASR as PPGs. These are used by the DBLSTM to synthesize the target language in the target speaker’s voice. A main advantage of this approach has very low training data requirement of the target speaker which can be in any language, as compared with a reference approach of training a special TTS engine using many recordings from the target speaker only in the target language. For a given target speaker, our proposed approach trained on 100 Mandarin (i.e. non-target language) utterances achieves comparable performance (in MOS and ABX test) of English synthetic speech as an HTS system trained on 1,000 English utterances.
UNDERSTANDING THE RATIONAL FUNCTION MODEL : METHODS AND APPLICATIONS

The physical and generalized sensor models are two widely used imaging geometry models in the photogrammetry and remote sensing. Utilizing the rational function model (RFM) to replace physical sensor models in photogrammetric mapping is becoming a standard way for economical and fast mapping from high-resolution images. The RFM is accepted for imagery exploitation since high accuracies have been achieved in all stages of the photogrammetric process just as performed by rigorous sensor models. Thus it is likely to become a passkey in complex sensor modeling. Nowadays, commercial off-the-shelf (COTS) digital photogrammetric workstations have incorporated the RFM and related techniques. Following the increasing number of RFM related publications in recent years, this paper reviews the methods and key applications reported mainly over the past five years, and summarizes the essential progresses and address the future research directions in this field. These methods include the RFM solution, the terrainindependent and terrain-dependent computational scenarios, the direct and indirect RFM refinement methods, the photogrammetric exploitation techniques, and photogrammetric interoperability for cross sensor/platform imagery integration. Finally, several open questions regarding some aspects worth of further study are addressed.
Fault detection and isolation in manufacturing systems with an identified discrete event model

In this paper a generic method for fault detection and isolation (FDI) in manufacturing systems considered as discrete event systems (DES) is presented. The method uses an identified model of the closed loop of plant and controller built on the basis of observed fault free system behavior. An identification algorithm known from literature is used to determine the fault detection model in form of a non-deterministic automaton. New results of how to parameterize this algorithm are reported. To assess the fault detection capability of an identified automaton, probabilistic measures are proposed. For fault isolation, the concept of residuals adapted for DES is used by defining appropriate set operations representing generic fault symptoms. The method is applied to a case study system.
From Monolith to Microservices: Lessons Learned on an Industrial Migration to a Web Oriented Architecture

MGDIS SA is a software editing company that underwent a major strategic and technical change during the past three years, investing 17 300 man. Days rewriting its core business software from monolithic architecture to a Web Oriented Architecture using microservices. The paper presents technical lessons learned during and from this migration by addressing three crucial questions for a successful context-adapted migration towards a Web Oriented Architecture: how to determine (i) the most suitable granularity of micro-services, (ii) the most appropriate deployment and (iii) the most efficient orchestration?
Player Skill Decomposition in Multiplayer Online Battle Arenas

Successful analysis of player skills in video games has important impacts on the process of enhancing player experience without undermining their continuous skill development. Moreover, player skill analysis becomes more intriguing in team-based video games because such form of study can help discover useful factors in effective team formation. In this paper, we consider the problem of skill decomposition in MOBA (MultiPlayer Online Battle Arena) games, with the goal to understand what player skill factors are essential for the outcome of a game match. To understand the construct of MOBA player skills, we utilize various skill-based predictive models to decompose player skills into interpretative parts, the impact of which are assessed in statistical terms. We apply this analysis approach on two widely known MOBAs, namely League of Legends (LoL) and Defense of the Ancients 2 (DOTA2). The finding is that base skills of in-game avatars, base skills of players, and players’ champion-specific skills are three prominent skill components influencing LoL’s match outcomes, while those of DOTA2 are mainly impacted by in-game avatars’ base skills but not much by the other two. PLAYER SKILL DECOMPOSITION IN MULTIPLAYER ONLINE BATTLE ARENAS 3 Player Skill Decomposition in Multiplayer Online Battle Arenas
Detection of recurring software vulnerabilities

Software security vulnerabilities are discovered on an almost daily basis and have caused substantial damage. Aiming at supporting early detection and resolution for them, we have conducted an empirical study on thousands of vulnerabilities and found that many of them are recurring due to software reuse. Based on the knowledge gained from the study, we developed SecureSync, an automatic tool to detect recurring software vulnerabilities on the systems that reuse source code or libraries. The core of SecureSync includes two techniques to represent and compute the similarity of vulnerable code across different systems. The evaluation for 60 vulnerabilities on 176 releases of 119 open-source software systems shows that SecureSync is able to detect recurring vulnerabilities with high accuracy and to identify 90 releases having potentially vulnerable code that are not reported or fixed yet, even in mature systems. A couple of cases were actually confirmed by their developers.
MOCgraph: Scalable Distributed Graph Processing Using Message Online Computing

Existing distributed graph processing frameworks, e.g., Pregel, Giraph, GPS and GraphLab, mainly exploit main memory to support flexible graph operations for efficiency. Due to the complexity of graph analytics, huge memory space is required especially for those graph analytics that spawn large intermediate results. Existing frameworks may terminate abnormally or degrade performance seriously when the memory is exhausted or the external storage has to be used. In this paper, we propose MOCgraph, a scalable distributed graph processing framework to reduce the memory footprint and improve the scalability, based on message online computing. MOCgraph consumes incoming messages in a streaming manner, so as to handle larger graphs or more complex analytics with the same memory capacity. MOCgraph also exploits message online computing with external storage to provide an efficient out-of-core support. We implement MOCgraph on top of Apache Giraph, and test it against several representative graph algorithms on large graph datasets. Experiments illustrate that MOCgraph is efficient and memory-saving, especially for graph analytics with large intermediate results.
Use Case Pitfalls: Top 10 Problems from Real Projects Using Use Cases

One of the beauties of use cases is their accessible, informal format. Use cases are easy to write, and the graphical notation is trivial. Because of their simplicity, use cases are not intimidating, even for teams that have little experience with formal requirements specification and management. However, the simplicity can be deceptive; writing good use cases takes some skill and practice. Many groups writing use cases for the first time run into similar kinds of problems. This paper presents the author's "Top Ten" list of use case pitfalls and problems, based on observations from a number of real projects. The paper outlines the symptoms of the problems, and recommends pragmatic cures for each. Examples are provided to illustrate the problems and their solutions.
A broadband three-way tapered-line power divider with several strip resistors

A novel three-way power divider using tapered lines is presented. It has several strip resistors which are formed like a ladder between the tapered-line conductors to achieve a good output isolation. The equivalent circuits are derived with the EE/OE/OO-mode analysis based on the fundamental propagation modes in three-conductor coupled lines. The fabricated three-way power divider shows a broadband performance in input return loss which is greater than 20 dB over a 3:1 bandwidth in the C-Ku bands.
Visual Domain Adaptation: A survey of recent advances

In pattern recognition and computer vision, one is often faced with scenarios where the training data used to learn a model have different distribution from the data on which the model is applied. Regardless of the cause, any distributional change that occurs after learning a classifier can degrade its performance at test time. Domain adaptation tries to mitigate this degradation. In this article, we provide a survey of domain adaptation methods for visual recognition. We discuss the merits and drawbacks of existing domain adaptation approaches and identify promising avenues for research in this rapidly evolving field.