nils-herrmann/clean_scirepeval huggingface dataset:
Fuel cell power conditioning for electric power applications : a summary

Fuel cells are considered to be one of the most promising sources of distributed energy because of their high efficiency, low environmental impact and scalability. Unfortunately, multiple complications exist in fuel cell operation. Fuel cells cannot accept current in the reverse direction, do not perform well with ripple current, have a low output voltage that varies with age and current, respond sluggishly to step changes in load and are limited in overload capabilities. For these reasons, power converters are often necessary to boost and regulate the voltage as a means to provide a stiff applicable DC power source. Furthermore, the addition of an inverter allows for the conversion of DC power to AC for an utility interface or for the application of an AC motor. To help motivate the use of power conditioning for the fuel cell, a brief introduction of the different types, applications and typical electrical characteristics of fuel cells is presented. This is followed by an examination of the various topologies of DC–DC boost converters and inverters used for power conditioning of fuel cells. Several architectures to aggregate multiple fuel cells for high-voltage/high-power applications are also reviewed.
Social and Heuristic Approaches to Credibility Evaluation Online

The tremendous amount of information available online has resulted in considerable research on information and source credibility. The vast majority of scholars, however, assume that individuals work in isolation to form credibility opinions and that people must assess information credibility in an effortful and time-consuming manner. Focus group data from 109 participants were used to examine these assumptions. Results show that most users rely on others to make credibility assessments, often through the use of group-based tools. Results also indicate that rather than systematically processing information, participants routinely invoked cognitive heuristics to evaluate the credibility of information and sources online. These findings are leveraged to suggest a number of avenues for further credibility theorizing, research, and practice.
Trait-based perspectives of leadership.

The trait-based perspective of leadership has a long but checkered history. Trait approaches dominated the initial decades of scientific leadership research. Later, they were disdained for their inability to offer clear distinctions between leaders and nonleaders and for their failure to account for situational variance in leadership behavior. Recently, driven by greater conceptual, methodological, and statistical sophistication, such approaches have again risen to prominence. However, their contributions are likely to remain limited unless leadership researchers who adopt this perspective address several fundamental issues. The author argues that combinations of traits and attributes, integrated in conceptually meaningful ways, are more likely to predict leadership than additive or independent contributions of several single traits. Furthermore, a defining core of these dominant leader trait patterns reflects a stable tendency to lead in different ways across disparate organizational domains. Finally, the author summarizes a multistage model that specifies some leader traits as having more distal influences on leadership processes and performance, whereas others have more proximal effects that are integrated with, and influenced by, situational parameters.
Who, What, When, and Where: Multi-Dimensional Collaborative Recommendations Using Tensor Factorization on Sparse User-Generated Data

Given the abundance of online information available to mobile users, particularly tourists and weekend travelers, recommender systems that effectively filter this information and suggest interesting participatory opportunities will become increasingly important. Previous work has explored recommending interesting locations; however, users would also benefit from recommendations for activities in which to participate at those locations along with suitable times and days. Thus, systems that provide collaborative recommendations involving multiple dimensions such as location, activities and time would enhance the overall experience of users.The relationship among these dimensions can be modeled by higher-order matrices called tensors which are then solved by tensor factorization. However, these tensors can be extremely sparse. In this paper, we present a system and an approach for performing multi-dimensional collaborative recommendations for Who (User), What (Activity), When (Time) and Where (Location), using tensor factorization on sparse user-generated data. We formulate an objective function which simultaneously factorizes coupled tensors and matrices constructed from heterogeneous data sources. We evaluate our system and approach on large-scale real world data sets consisting of 588,000 Flickr photos collected from three major metro regions in USA. We compare our approach with several state-of-the-art baselines and demonstrate that it outperforms all of them.
Deep Multimodal Fusion: A Hybrid Approach

We propose a novel hybrid model that exploits the strength of discriminative classifiers along with the representation power of generative models. Our focus is on detecting multimodal events in time varying sequences as well as generating missing data in any of the modalities. Discriminative classifiers have been shown to achieve higher performances than the corresponding generative likelihood-based classifiers. On the other hand, generative models learn a rich informative space which allows for data generation and joint feature representation that discriminative models lack. We propose a new model that jointly optimizes the representation space using a hybrid energy function. We employ a Restricted Boltzmann Machines (RBMs) based model to learn a shared representation across multiple modalities with time varying data. The Conditional RBMs (CRBMs) is an extension of the RBM model that takes into account short term temporal phenomena. The hybrid model involves augmenting CRBMs with a discriminative component for classification. For these purposes we propose a novel Multimodal Discriminative CRBMs (MMDCRBMs) model. First, we train the MMDCRBMs model using labeled data by training each modality, followed by training a fusion layer. Second, we exploit the generative capability of MMDCRBMs to activate the trained model so as to generate the lower-level data corresponding to the specific label that closely matches the actual input data. We evaluate our approach on ChaLearn dataset, audio-mocap, as well as the Tower Game dataset, mocap-mocap as well as three multimodal toy datasets. We report classification accuracy, generation accuracy, and localization accuracy and demonstrate its superiority compared to the state-of-the-art methods.
The Neural Architecture of the Language Comprehension Network: Converging Evidence from Lesion and Connectivity Analyses

While traditional models of language comprehension have focused on the left posterior temporal cortex as the neurological basis for language comprehension, lesion and functional imaging studies indicate the involvement of an extensive network of cortical regions. However, the full extent of this network and the white matter pathways that contribute to it remain to be characterized. In an earlier voxel-based lesion-symptom mapping analysis of data from aphasic patients (Dronkers et al., 2004), several brain regions in the left hemisphere were found to be critical for language comprehension: the left posterior middle temporal gyrus, the anterior part of Brodmann's area 22 in the superior temporal gyrus (anterior STG/BA22), the posterior superior temporal sulcus (STS) extending into Brodmann's area 39 (STS/BA39), the orbital part of the inferior frontal gyrus (BA47), and the middle frontal gyrus (BA46). Here, we investigated the white matter pathways associated with these regions using diffusion tensor imaging from healthy subjects. We also used resting-state functional magnetic resonance imaging data to assess the functional connectivity profiles of these regions. Fiber tractography and functional connectivity analyses indicated that the left MTG, anterior STG/BA22, STS/BA39, and BA47 are part of a richly interconnected network that extends to additional frontal, parietal, and temporal regions in the two hemispheres. The inferior occipito-frontal fasciculus, the arcuate fasciculus, and the middle and inferior longitudinal fasciculi, as well as transcallosal projections via the tapetum were found to be the most prominent white matter pathways bridging the regions important for language comprehension. The left MTG showed a particularly extensive structural and functional connectivity pattern which is consistent with the severity of the impairments associated with MTG lesions and which suggests a central role for this region in language comprehension.
Testbed Evaluation of Virtual Environment Interaction Techniques

As immersive virtual environment (VE) applications become more complex, it is clear that we need a firm understanding of the principles of VE interaction. In particular, designers need guidance in choosing three-dimensional interaction techniques. In this paper, we present a systematic approach, testbed evaluation, for the assessment of interaction techniques for VEs. Testbed evaluation uses formal frameworks and formal experiments with multiple independent and dependent variables in order to obtain a wide range of performance data for VE interaction techniques. We present two testbed experiments, covering techniques for the common VE tasks of travel and object selection/manipulation. The results of these experiments allow us to form general guidelines for VE interaction, and to provide an empirical basis for choosing interaction techniques in VE applications. This has been shown to produce measurable usability gains in a real-world VE application.
Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models

Inspired by recent advances in multimodal learning and machine translation, we introduce an encoder-decoder pipeline that learns (a): a multimodal joint embedding space with images and text and (b): a novel language model for decoding distributed representations from our space. Our pipeline effectively unifies joint image-text embedding models with multimodal neural language models. We introduce the structure-content neural language model that disentangles the structure of a sentence to its content, conditioned on representations produced by the encoder. The encoder allows one to rank images and sentences while the decoder can generate novel descriptions from scratch. Using LSTM to encode sentences, we match the state-of-the-art performance on Flickr8K and Flickr30K without using object detections. We also set new best results when using the 19-layer Oxford convolutional network. Furthermore we show that with linear encoders, the learned embedding space captures multimodal regularities in terms of vector space arithmetic e.g. *image of a blue car* "blue" + "red" is near images of red cars. Sample captions generated for 800 images are made available for comparison.
As time goes by : Evidence for two systems in processing space 4 ime metaphors

Temporal language is often couched in spatial metaphors. English has been claimed to have two space-time metaphoric systems: the ego-moving metaphor, wherein the observer’s context progresses along the time-line towards the future, and the time-moving metaphor, wherein time is conceived of as a river or conveyor belt on which events are moving from the future to the past. In three experiments, we investigated the psychological status of these metaphors by asking subjects to carry out temporal inferences stated in terms of spatial metaphors. In Experiment 1, we found that subjects were slowed in their processing when the assertions shifted from one spatial metaphoric system to the other. In Experiment 2, we determined that this cost of shifting could not be attributed to local lexical factors. In Experiment 3, we again found this metaphor consistency effect in a naturalistic version of the study in which we asked commonsense time questions of passengers at an airport. The results of the three studies provide converging evidence that people use spatial metaphors in temporal reasoning. Implications for the status of metaphoric systems arc discussed.
Describing objects by their attributes

We propose to shift the goal of recognition from naming to describing. Doing so allows us not only to name familiar objects, but also: to report unusual aspects of a familiar object (“spotty dog”, not just “dog”); to say something about unfamiliar objects (“hairy and four-legged”, not just “unknown”); and to learn how to recognize new objects with few or no visual examples. Rather than focusing on identity assignment, we make inferring attributes the core problem of recognition. These attributes can be semantic (“spotty”) or discriminative (“dogs have it but sheep do not”). Learning attributes presents a major new challenge: generalization across object categories, not just across instances within a category. In this paper, we also introduce a novel feature selection method for learning attributes that generalize well across categories. We support our claims by thorough evaluation that provides insights into the limitations of the standard recognition paradigm of naming and demonstrates the new abilities provided by our attribute-based framework.
Immersive Learning Technologies: Realism and Online Authentic Learning

The development of immersive learning technologies in the form of virtual reality and advanced computer applications has meant that realistic creations of simulated environments are now possible. Such simulations have been used to great effect in training in the military, air force, and in medical training. But how realistic do problems need to be in education for effective learning to occur? Some authors and researchers argue that problems should be real, or that simulations should have ultra-realistic physical similarity to an actual context. This paper proposes that physical verisimilitude to real situations is of less importance in learning than ‘cognitive realism’, provided by immersing students in engaging and complex tasks. The paper presents a description of the theory and research that provide the foundations for this approach. Examples of courses employing cognitive, rather than physical, realism are presented together with the views of teachers, authors and instructional designers. Finally, the implications of this approach are discussed. Immersive learning technologies: Realism and online authentic learning
WordNet Editor to Refine Indonesian Language Lexical Database

This paper describes an approach for editing Indonesian Language Lexical Database especially noun category and its relations. The purpose of this editor is to refine Indonesian Lexical Database that was developed in our previous researches. The visualization of the editor is using graph library with some modifications and additions. Furthermore, this editor will be web based so that everyone can participate to improve Indonesian Language Lexical Database. There is an administrator role that had to accept or reject any suggestion for the changes suggested by any member. We believe that this editing approach can also be used to improve WordNet developed in other languages.
Pulse-width modulation of Z-source inverters

Z-Source inverters have recently been proposed as an alternative power conversion concept as they have both voltage buck and boost capabilities. These inverters use a unique impedance network, coupled between the power source and converter circuit, to provide both voltage buck and boost properties, which cannot be achieved with conventional voltage-source and current-source inverters. To facilitate understanding of Z-source inverter modulation, this paper presents a detailed analysis, showing how various conventional pulse-width modulation strategies can be modified to switch a voltage-type Z-source inverter either continuously or discontinuously, while retaining all the unique harmonic performance features of these conventional modulation strategies. The paper starts by analyzing the modulation requirements of a single-phase H-bridge Z-source inverter, and subsequently extends the analysis to cover the more complex three-phase-leg and four-phase-leg Z-source inverters, with carrier-based implementation reference equations derived for all the inverters. The theoretical and modulation concepts presented have been verified both in simulation and experimentally.
Comprehensive Dataset of Broadcast Soccer Videos

In the absence of a rich dataset, sports video analysis remains a challenging task. Shot analysis, event detection, and player tracking are important aspects of soccer game research, however currently available datasets focus mainly on single-type annotations, and have several limitations when applied to full video analysis; these include a lack of training samples, temporal annotations, and rich labels of different types. In this paper, we focus on broadcast soccer videos and present a comprehensive dataset for analysis. In its current version, the dataset contains 222 broadcast soccer videos, totaling 170 video hours. The dataset covers three annotation types: (1) A shot boundary with two shot transition types, and shot type annotations with five shot types; (2) Event annotations with 11 event labels, and a story annotation with 15 story labels at coarser granularity; and (3) bounding boxes of the players under analysis in a subset of 19908 video frames. We hope that the introduction of this dataset will enable the research community to develop soccer video analysis techniques.
NV-Tree: Reducing Consistency Cost for NVM-based Single Level Systems

The non-volatile memory (NVM) has DRAM-like performance and disk-like persistency which make it possible to replace both disk and DRAM to build single level systems. To keep data consistency in such systems is non-trivial because memory writes may be reordered by CPU and memory controller. In this paper, we study the consistency cost for an important and common data structure, B+Tree. Although the memory fence and CPU cacheline flush instructions can order memory writes to achieve data consistency, they introduce a significant overhead (more than 10X slower in performance). Based on our quantitative analysis of consistency cost, we propose NV-Tree, a consistent and cache-optimized B+Tree variant with reduced CPU cacheline flush. We implement and evaluate NV-Tree and NV-Store, a key-value store based on NV-Tree, on an NVDIMM server. NVTree outperforms the state-of-art consistent tree structures by up to 12X under write-intensive workloads. NV-Store increases the throughput by up to 4.8X under YCSB workloads compared to Redis.
Nonoutsourceable Scratch-Off Puzzles to Discourage Bitcoin Mining Coalitions

An implicit goal of Bitcoin's reward structure is to diffuse network influence over a diverse, decentralized population of individual participants. Indeed, Bitcoin's security claims rely on no single entity wielding a sufficiently large portion of the network's overall computational power. Unfortunately, rather than participating independently, most Bitcoin miners join coalitions called mining pools in which a central pool administrator largely directs the pool's activity, leading to a consolidation of power. Recently, the largest mining pool has accounted for more than half of network's total mining capacity. Relatedly, "hosted mining" service providers offer their clients the benefit of economies-of-scale, tempting them away from independent participation. We argue that the prevalence of mining coalitions is due to a limitation of the Bitcoin proof-of-work puzzle -- specifically, that it affords an effective mechanism for enforcing cooperation in a coalition. We present several definitions and constructions for "nonoutsourceable" puzzles that thwart such enforcement mechanisms, thereby deterring coalitions. We also provide an implementation and benchmark results for our schemes to show they are practical.
Enhancing Accountability and Trust in Distributed Ledgers

Permisionless decentralized ledgers (“blockchains”) such as the one underlying the cryptocurrency Bitcoin allow anonymous participants to maintain the ledger, while avoiding control or “censorship” by any single entity. In contrast, permissioned decentralized ledgers exploit real-world trust and accountability, allowing only explicitly authorized parties to maintain the ledger. Permissioned ledgers support more flexible governance and a wider choice of consensus mechanisms. Both kinds of decentralized ledgers may be susceptible to manipulation by participants who favor some transactions over others. The real-world accountability underlying permissioned ledgers provides an opportunity to impose fairness constraints that can be enforced by penalizing violators after-thefact. To date, however, this opportunity has not been fully exploited, unnecessarily leaving participants latitude to manipulate outcomes undetectably. This paper draws attention to this issue, and proposes design principles to make such manipulation more difficult, as well as specific mechanisms to make it easier to detect when violations occur.
A Learning Algorithm for Boltzmann Machines

The computotionol power of massively parallel networks of simple processing elements resides in the communication bandwidth provided by the hardware connections between elements. These connections con allow a significant fraction of the knowledge of the system to be applied to an instance of a problem in o very short time. One kind of computation for which massively porollel networks appear to be well suited is large constraint satisfaction searches, but to use the connections efficiently two conditions must be met: First, a search technique that is suitable for parallel networks must be found. Second, there must be some way of choosing internal representations which allow the preexisting hardware connections to be used efficiently for encoding the constraints in the domain being searched. We describe a generol parallel search method, based on statistical mechanics, and we show how it leads to a general learning rule for modifying the connection strengths so as to incorporate knowledge obout o task domain in on efficient way. We describe some simple examples in which the learning algorithm creates internal representations thot ore demonstrobly the most efficient way of using the preexisting connectivity structure.
Detecting protein function and protein-protein interactions from genome sequences.

A computational method is proposed for inferring protein interactions from genome sequences on the basis of the observation that some pairs of interacting proteins have homologs in another organism fused into a single protein chain. Searching sequences from many genomes revealed 6809 such putative protein-protein interactions in Escherichia coli and 45,502 in yeast. Many members of these pairs were confirmed as functionally related; computational filtering further enriches for interactions. Some proteins have links to several other proteins; these coupled links appear to represent functional interactions such as complexes or pathways. Experimentally confirmed interacting pairs are documented in a Database of Interacting Proteins.