nils-herrmann/clean_scirepeval huggingface dataset:
Block-Sparse Recurrent Neural Networks

Recurrent Neural Networks (RNNs) are used in state-of-the-art models in domains such as speech recognition, machine translation, and language modelling. Sparsity is a technique to reduce compute and memory requirements of deep learning models. Sparse RNNs are easier to deploy on devices and high-end server processors. Even though sparse operations need less compute and memory relative to their dense counterparts, the speed-up observed by using sparse operations is less than expected on different hardware platforms. In order to address this issue, we investigate two different approaches to induce block sparsity in RNNs: pruning blocks of weights in a layer and using group lasso regularization to create blocks of weights with zeros. Using these techniques, we demonstrate that we can create block-sparse RNNs with sparsity ranging from 80% to 90% with small loss in accuracy. This allows us to reduce the model size by roughly 10×. Additionally, we can prune a larger dense network to recover this loss in accuracy while maintaining high block sparsity and reducing the overall parameter count. Our technique works with a variety of block sizes up to 32×32. Block-sparse RNNs eliminate overheads related to data storage and irregular memory accesses while increasing hardware efficiency compared to unstructured sparsity.
Passive returning mechanism for twisted string actuators

The twisted string actuator is an actuator that is gaining popularity in various engineering and robotics and applications. However, the fundamental limitation of actuators of this type is the uni-directional action, meaning that the actuator can contract but requires external power to return to its initial state. This paper proposes 2 novel passive extension mechanisms based on buckling effect to solve the uni-directional issue of the twisted string actuator. The proposed mechanisms are mechanically simple and compact and provide a nearly-constant extension force throughout the operation range. The constant force can fully extend the twisted string actuator with minimal loss of force during contraction. The designed mechanisms are evaluated in a series of practical tests, and their performances are compared and discussed.
UWB-MIMO diversity antenna for next generation wireless applications

A Novel and compact UWB-MIMO antenna is proposed and analyzed. The two circular monopole antennas embedded on a FR4 substrate with dielectric constant 4.4 and substrate height of 0.8 mm. The proposed antenna with the size of 64×40 mm2, perform good diversity performance for entire ultra wideband. Antenna results are carried out on Computer simulation tool (CST) and results are analyzed in terms of return loss, isolation, antenna gain, radiation pattern and correlation coefficient. A comparative study of results has been taken into account for proposed antenna with and without ground stubs. The novel ground stubs inserted in the ground plane reduce the mutual coupling and enhance isolation from -10 dB to -18 dB for almost whole ultra wideband except the frequency range of 6.7 GHz to 9.35 GHz. A good impedance bandwidth, diversity performance makes the proposed designed antenna suitable for high-speed wireless communication system.
Improving Collocated Robot Teleoperation with Augmented Reality

Robot teleoperation can be a challenging task, often requiring a great deal of user training and expertise, especially for platforms with high degrees-of-freedom (e.g., industrial manipulators and aerial robots). Users often struggle to synthesize information robots collect (e.g., a camera stream) with contextual knowledge of how the robot is moving in the environment. We explore how advances in augmented reality (AR) technologies are creating a new design space for mediating robot teleoperation by enabling novel forms of intuitive, visual feedback. We prototype several aerial robot teleoperation interfaces using AR, which we evaluate in a 48-participant user study where participants completed an environmental inspection task. Our new interface designs provided several objective and subjective performance benefits over existing systems, which often force users into an undesirable paradigm that divides user attention between monitoring the robot and monitoring the robot»s camera feed(s).
Librarians’ Role in Development and Achievement of Central Library Users’ Information Literacy (a Case Study: Iran)

Due to the development of technologies, communications, databases and information resource varieties in today's information age, our various social, economic, cultural and political needs cannot be fulfilled by relying merely on past knowledge and skills as done previously. Information literacy (IL) as a set of necessary skills for all of us is an effective way of treating new technologies and their effective application in our lives. The study aimed to survey the library users' views in the Central Library of Babol University of Medical Sciences (The Library), Iran, on the role and influences of librarians on their IL development and improvement.



This analytical survey used a researcher-made questionnaire for data collection. Research population consisted of all users referring Information Unit of The Library during 22 September - 20 December 2010. Of them, 150 users participating in at least 5 workshops held by The Library were selected as the study sample.



Based on the findings, 52.7 percent of the subjects rated the influence of the librarians on their IL development much and very much. 44.7 percent claimed that they more acquired IL skills from librarians rather than others. 100 (63.3%) subjects preferred workshops held by the librarian to other workshops.



The users of Information Unit of The Library perceived the training IL skills by librarians as a main influencing factor in their IL development and achievement. This emphasized the necessity of teaching IL to users and training the librarians in better teaching IL skills to library user.
A Survey of Computation Offloading for Mobile Systems

Mobile systems have limited resources, such as battery life, network bandwidth, storage capacity, and processor performance. These restrictions may be alleviated by computation of f loading: sending heavy computation to resourceful servers and receiving the results from these servers. Many issues related to offloading have been investigated in the past decade. This survey paper provides an overview of the background, techniques, systems, and research areas for offloading computation. We also describe directions for future research.
MATLAB / Simulink-based transient stability analysis of a multimachine power system

Simulink is advanced software by MathWorks Inc., which is increasingly being used as a basic building block in many areas of research. As such, it also holds great potential in the area of power system simulation. In this paper, we have taken a multi-machine power system example to demonstrate the features and scope of a Simulink-based model for transient stability analysis. A selfsufficient model has been given with full details, which can work as a basic structure for an advanced and detailed study.
Application of Statistical Relational Learning to Hybrid Recommendation Systems

Recommendation systems usually involve exploiting the relations among known features and content that describe items (content-based filtering) or the overlap of similar users who interacted with or rated the target item (collaborative filtering). To combine these two filtering approaches, current model-based hybrid recommendation systems typically require extensive feature engineering to construct a user profile. Statistical Relational Learning (SRL) provides a straightforward way to combine the two approaches. However, due to the large scale of the data used in real world recommendation systems, little research exists on applying SRL models to hybrid recommendation systems, and essentially none of that research has been applied on real big-data-scale systems. In this paper, we proposed a way to adapt the state-of-the-art in SRL learning approaches to construct a real hybrid recommendation system. Furthermore, in order to satisfy a common requirement in recommendation systems (i.e. that false positives are more undesirable and therefore penalized more harshly than false negatives), our approach can also allow tuning the tradeoff between the precision and recall of the system in a principled way. Our experimental results demonstrate the efficiency of our proposed approach as well as its improved performance on recommendation precision.
Autonomic nervous system activity in emotion: A review

Autonomic nervous system (ANS) activity is viewed as a major component of the emotion response in many recent theories of emotion. Positions on the degree of specificity of ANS activation in emotion, however, greatly diverge, ranging from undifferentiated arousal, over acknowledgment of strong response idiosyncrasies, to highly specific predictions of autonomic response patterns for certain emotions. A review of 134 publications that report experimental investigations of emotional effects on peripheral physiological responding in healthy individuals suggests considerable ANS response specificity in emotion when considering subtypes of distinct emotions. The importance of sound terminology of investigated affective states as well as of choice of physiological measures in assessing ANS reactivity is discussed.
Word Translation Without Parallel Data

State-of-the-art methods for learning cross-lingual word embeddings have relied on bilingual dictionaries or parallel corpora. Recent studies showed that the need for parallel data supervision can be alleviated with character-level information. While these methods showed encouraging results, they are not on par with their supervised counterparts and are limited to pairs of languages sharing a common alphabet. In this work, we show that we can build a bilingual dictionary between two languages without using any parallel corpora, by aligning monolingual word embedding spaces in an unsupervised way. Without using any character information, our model even outperforms existing supervised methods on cross-lingual tasks for some language pairs. Our experiments demonstrate that our method works very well also for distant language pairs, like English-Russian or EnglishChinese. We finally describe experiments on the English-Esperanto low-resource language pair, on which there only exists a limited amount of parallel data, to show the potential impact of our method in fully unsupervised machine translation. Our code, embeddings and dictionaries are publicly available1.
Parser Extraction of Triples in Unstructured Text

The web contains vast repositories of unstructured text. We investigate the opportunity for building a knowledge graph from these text sources. We generate a set of triples which can be used in knowledge gathering and integration. We define the architecture of a language compiler for processing subjectpredicate-object triples using the OpenNLP parser. We implement a depth-first search traversal on the POS tagged syntactic tree appending predicate and object information. A parser enables higher precision and higher recall extractions of syntactic relationships across conjunction boundaries. We are able to extract 2-2.5 times the correct extractions of ReVerb. The extractions are used in a variety of semantic web applications and question answering. We verify extraction of 50,000 triples on the ClueWeb dataset.  Open information extraction, Relation extraction, NLP
"Isn't it fun to get the respect that we're going to deserve?" Narcissism, social rejection, and aggression.

Across four studies, narcissists were more angry and aggressive after experiencing a social rejection than were nonnarcissists. In Study 1, narcissism was positively correlated with feelings of anger and negatively correlated with more internalized negative emotions in a self-reported, past episode of social rejection. Study 2 replicated this effect for a concurrent lab manipulation of social rejection. In Study 3, narcissists aggressed more against someone who rejected them (i.e., direct aggression). In Study 4, narcissists were also more aggressive toward an innocent third party after experiencing social rejection (i.e., displaced aggression). Narcissists were not more aggressive after social acceptance. Self-esteem plays little role in predicting aggression in response to rejection. These results suggest that the combination of narcissism and social rejection is a powerful predictor of aggressive behavior.
Comprehensive Evaluation of Deep Learning Architectures for Prediction of DNA/RNA Sequence Binding Specificities

Deep learning architectures have recently demonstrated their power in predicting DNAand RNA-binding specificities. Existing methods fall into three classes: Some are based on Convolutional Neural Networks (CNNs), others use Recurrent Neural Networks (RNNs), and others rely on hybrid architectures combining CNNs and RNNs. However, based on existing studies it is still unclear which deep learning architecture is achieving the best performance. Thus an in-depth analysis and evaluation of the different methods is needed to fully evaluate their relative.  In this study, We present a systematic exploration of various deep learning architectures for predicting DNAand RNA-binding specificities. For this purpose, we present deepRAM, an end-to-end deep learning tool that provides an implementation of novel and previously proposed architectures; its fully automatic model selection procedure allows us to perform a fair and unbiased comparison of deep learning architectures. We find that an architecture that uses k-mer embedding to represent the sequence, a convolutional layer and a recurrent layer, outperforms all other methods in terms of model accuracy. Our work provides guidelines that will assist the practitioner in choosing the best architecture for the task at hand, and provides some insights on the differences between the models learned by convolutional and recurrent networks. In particular, we find that although recurrent networks improve model accuracy, this comes at the expense of a loss in the interpretability of the features learned by the model. Availability and implementation: The source code for deepRAM is available at https://github.com/MedChaabane/deepRAM Contact:  Supplementary data are available at Bioinformatics online.
MongoDB-Based Modular Ontology Building for Big Data Integration

Big Data are collections of data sets so large and complex to process using classical database management tools. Their main characteristics are volume, variety and velocity. Although these characteristics accentuate heterogeneity problems, users are always looking for a unified view of the data. Consequently, Big Data integration is a new research area that faces new challenges due to the aforementioned characteristics. Ontologies are widely used in data integration since they represent knowledge as a formal description of a domain of interest. With the advent of Big Data, their implementation faces new challenges due to the volume, variety and velocity dimensions of these data. This paper illustrates an approach to build a modular ontology for Big Data integration that considers the characteristics of big volume, high-speed generation and wide variety of the data. Our approach exploits a NOSQL database, namely MongoDB, and takes advantages of modular ontologies. It follows three main steps: wrapping data sources to MongoDB databases, generating local ontologies and finally composing the local ontologies to get a global one. We equally focus on the implementation of the two last steps.
Next-Generation Synthetic Gene Networks

Synthetic biology is focused on the rational construction of biological systems based on engineering principles. During the field's first decade of development, significant progress has been made in designing biological parts and assembling them into genetic circuits to achieve basic functionalities. These circuits have been used to construct proof-of-principle systems with promising results in industrial and medical applications. However, advances in synthetic biology have been limited by a lack of interoperable parts, techniques for dynamically probing biological systems and frameworks for the reliable construction and operation of complex, higher-order networks. As these challenges are addressed, synthetic biologists will be able to construct useful next-generation synthetic gene networks with real-world applications in medicine, biotechnology, bioremediation and bioenergy.
Face recognition using PCA and SVM

Automatic recognition of people has received much attention during the recent years due to its many applications in different fields such as law enforcement, security applications or video indexing. Face recognition is an important and very challenging technique to automatic people recognition. Up to date, there is no technique that provides a robust solution to all situations and different applications that face recognition may encounter. In general, we can make sure that performance of a face recognition system is determined by how to extract feature vector exactly and to classify them into a group accurately. It, therefore, is necessary for us to closely look at the feature extractor and classifier. In this paper, Principle Component Analysis (PCA) is used to play a key role in feature extractor and the SVMs are used to tackle the face recognition problem. Support Vector Machines (SVMs) have been recently proposed as a new classifier for pattern recognition. We illustrate the potential of SVMs on the Cambridge ORL Face database, which consists of 400 images of 40 individuals, containing quite a high degree of variability in expression, pose, and facial details. The SVMs that have been used included the Linear (LSVM), Polynomial (PSVM), and Radial Basis Function (RBFSVM) SVMs. We provide experimental evidence which show that Polynomial and Radial Basis Function (RBF) SVMs performs better than Linear SVM on the ORL Face Dataset when both are used with one against all classification. We also compared the SVMs based recognition with the standard eigenface approach using the Multi-Layer Perceptron (MLP) Classification criterion.
Shape driven kernel adaptation in Convolutional Neural Network for robust facial trait recognition

One key challenge of facial trait recognition is the large non-rigid appearance variations due to some irrelevant real world factors, such as viewpoint and expression changes. In this paper, we explore how the shape information, i.e. facial landmark positions, can be explicitly deployed into the popular Convolutional Neural Network (CNN) architecture to disentangle such irrelevant non-rigid appearance variations. First, instead of using fixed kernels, we propose a kernel adaptation method to dynamically determine the convolutional kernels according to the spatial distribution of facial landmarks, which helps learning more robust features. Second, motivated by the intuition that different local facial regions may demand different adaptation functions, we further propose a tree-structured convolutional architecture to hierarchically fuse multiple local adaptive CNN subnetworks. Comprehensive experiments on WebFace, Morph II and MultiPIE databases well validate the effectiveness of the proposed kernel adaptation method and tree-structured convolutional architecture for facial trait recognition tasks, including identity, age and gender recognition. For all the tasks, the proposed architecture consistently achieves the state-of-the-art performances.
Broadband high PAE GaN push-pull power amplifier for 500MHz to 2.5 GHz operation

A broadband push-pull power amplifier with power added efficiency (PAE) greater than 47% from 500 MHz to 2.5 GHz is reported. The power amplifier comprises two GaN integrated circuits whose outputs are combined with a broadband balun to achieve power up to 20W. Each IC contains two stacked GaN HEMTs and is designed to employ a load impedance of 25Ω without any reactive matching in order to achieve broadband operation. To provide a better input impedance match to 25Ω and to reduce gain variation over frequency, an RC feedback network is used. Measurements, under CW excitation, demonstrated greater than 63% power added efficiency at select frequencies and greater than 47% over the entire bandwidth.
Connecting and separating mind-sets: culture as situated cognition.

People perceive meaningful wholes and later separate out constituent parts (D. Navon, 1977). Yet there are cross-national differences in whether a focal target or integrated whole is first perceived. Rather than construe these differences as fixed, the proposed culture-as-situated-cognition model explains these differences as due to whether a collective or individual mind-set is cued at the moment of observation. Eight studies demonstrated that when cultural mind-set and task demands are congruent, easier tasks are accomplished more quickly and more difficult or time-constrained tasks are accomplished more accurately (



Koreans, Korean Americans;  Hong Kong Chinese;  European- and Asian-heritage Americans; 



5 Hong Kong Chinese;   Norwegians;  African-, European-, and Asian-heritage Americans). Meta-analyses (d = .34) demonstrated homogeneous effects across geographic place (East-West), racial-ethnic group, task, and sensory mode-differences are cued in the moment. Contrast and separation are salient individual mind-set procedures, resulting in focus on a single target or main point. Assimilation and connection are salient collective mind-set procedures, resulting in focus on multiplicity and integration.
Simple low-dimensional features approximating NCC-based image matching

This paper proposes new low-dimensional image features that enable images to be very efficiently matched. Image matching is one of the key technologies for many vision-based applications, including template matching, block motion estimation, video compression, stereo vision, image/video near-duplicate detection, similarity join for image/video database, and so on. Normalized cross correlation (NCC) is one of widely used methods for image matching with preferable characteristics such as robustness to intensity offsets and contrast changes, but it is computationally expensive. The proposed features, derived by the method of Lagrange multipliers, can provide upper-bounds of NCC as a simple dot product between two low-dimensional feature vectors. By using the proposed features, NCC-based image matching can be effectively accelerated. The matching performance with the proposed features is demonstrated using an image database obtained from actual broadcast videos. The new features are shown to outperform other methods: multilevel successive elimination algorithm (MSEA), discrete cosine transform (DCT) coefficients, and histograms. The proposed method can achieve very high precision while only slightly sacrificing recall.
Training induces changes in white matter architecture

Although experience-dependent structural changes have been found in adult gray matter, there is little evidence for such changes in white matter. Using diffusion imaging, we detected a localized increase in fractional anisotropy, a measure of microstructure, in white matter underlying the intraparietal sulcus following training of a complex visuo-motor skill. This provides, to the best of our knowledge, the first evidence for training-related changes in white-matter structure in the healthy human adult brain.